From d5a87f06c2d1d5e587a853e9bdb56cdc1cd471d9 Mon Sep 17 00:00:00 2001 From: xinzhu-L <3021236220@qq.com> Date: Wed, 12 Nov 2025 18:21:19 +0800 Subject: [PATCH] update document --- docs_state/api/index.rst | 73 +- docs_state/core-concepts/architecture.rst | 609 ------- docs_state/core-concepts/neurons.rst | 598 ------ docs_state/core-concepts/projections.rst | 896 --------- docs_state/core-concepts/state-management.rst | 1016 ----------- docs_state/core-concepts/synapses.rst | 643 ------- docs_state/examples/gallery.rst | 484 ++--- .../how-to-guides/custom-components.rst | 510 ------ .../how-to-guides/debugging-networks.rst | 811 --------- docs_state/how-to-guides/gpu-tpu-usage.rst | 826 --------- .../performance-optimization.rst | 340 ---- docs_state/how-to-guides/save-load-models.rst | 890 --------- docs_state/index.rst | 8 +- docs_state/quickstart/5min-tutorial.ipynb | 259 ++- docs_state/quickstart/concepts-overview.rst | 267 --- .../core-concepts/architecture.ipynb | 972 ++++++++++ .../{ => quickstart}/core-concepts/index.rst | 10 +- .../quickstart/core-concepts/neurons.ipynb | 1223 +++++++++++++ .../core-concepts/projections.ipynb | 1348 ++++++++++++++ .../core-concepts/state-management.ipynb | 1599 +++++++++++++++++ .../quickstart/core-concepts/synapses.ipynb | 938 ++++++++++ docs_state/quickstart/index.rst | 7 +- docs_state/quickstart/installation.rst | 2 +- docs_state/quickstart/overview.ipynb | 402 +++++ .../tutorials/advanced/05-snn-training.ipynb | 14 +- .../advanced/06-synaptic-plasticity.ipynb | 206 ++- .../advanced/07-large-scale-simulations.ipynb | 22 +- .../tutorials/basic/01-lif-neuron.ipynb | 216 ++- .../tutorials/basic/02-synapse-models.ipynb | 209 ++- .../basic/03-network-connections.ipynb | 237 ++- .../tutorials/basic/04-input-output.ipynb | 14 +- .../how-to-guides/custom-components.ipynb | 633 +++++++ .../how-to-guides/debugging-networks.ipynb | 1144 ++++++++++++ .../{ => tutorials}/how-to-guides/index.rst | 10 +- .../performance-optimization.ipynb | 507 ++++++ .../how-to-guides/save-load-models.ipynb | 621 +++++++ docs_state/tutorials/index.rst | 355 +--- 37 files changed, 10425 insertions(+), 8494 deletions(-) delete mode 100644 docs_state/core-concepts/architecture.rst delete mode 100644 docs_state/core-concepts/neurons.rst delete mode 100644 docs_state/core-concepts/projections.rst delete mode 100644 docs_state/core-concepts/state-management.rst delete mode 100644 docs_state/core-concepts/synapses.rst delete mode 100644 docs_state/how-to-guides/custom-components.rst delete mode 100644 docs_state/how-to-guides/debugging-networks.rst delete mode 100644 docs_state/how-to-guides/gpu-tpu-usage.rst delete mode 100644 docs_state/how-to-guides/performance-optimization.rst delete mode 100644 docs_state/how-to-guides/save-load-models.rst delete mode 100644 docs_state/quickstart/concepts-overview.rst create mode 100644 docs_state/quickstart/core-concepts/architecture.ipynb rename docs_state/{ => quickstart}/core-concepts/index.rst (94%) create mode 100644 docs_state/quickstart/core-concepts/neurons.ipynb create mode 100644 docs_state/quickstart/core-concepts/projections.ipynb create mode 100644 docs_state/quickstart/core-concepts/state-management.ipynb create mode 100644 docs_state/quickstart/core-concepts/synapses.ipynb create mode 100644 docs_state/quickstart/overview.ipynb create mode 100644 docs_state/tutorials/how-to-guides/custom-components.ipynb create mode 100644 docs_state/tutorials/how-to-guides/debugging-networks.ipynb rename docs_state/{ => tutorials}/how-to-guides/index.rst (87%) create mode 100644 docs_state/tutorials/how-to-guides/performance-optimization.ipynb create mode 100644 docs_state/tutorials/how-to-guides/save-load-models.ipynb diff --git a/docs_state/api/index.rst b/docs_state/api/index.rst index 24f2fa87..32ab8814 100644 --- a/docs_state/api/index.rst +++ b/docs_state/api/index.rst @@ -1,10 +1,10 @@ API Reference ============= -Complete API reference for BrainPy 3.0. +Complete API reference for ``brainpy.state`` .. note:: - BrainPy 3.0 is built on top of `brainstate `_, + ``brainpy.state`` is built on top of `brainstate `_, `brainunit `_, and `braintools `_. Organization @@ -183,9 +183,6 @@ Readout Layers # Leaky spiking readout brainpy.state.LeakySpikeReadout(in_size=100, tau=5*u.ms, V_th=1*u.mV) -Documentation Sections ----------------------- - .. toctree:: :maxdepth: 2 @@ -195,68 +192,4 @@ Documentation Sections synouts stp readouts - inputs - -Import Structure ----------------- - -BrainPy uses a clear import hierarchy: - -.. code-block:: python - - import brainpy as bp # Core BrainPy - import brainstate # State management and modules - import brainunit as u # Physical units - import braintools # Training utilities - - # Neurons and synapses - neuron = brainpy.state.LIF(100, ...) - synapse = brainpy.state.Expon.desc(100, tau=5*u.ms) - - # State management - state = brainstate.ShortTermState(...) - brainstate.nn.init_all_states(net) - - # Units - current = 2.0 * u.nA - voltage = -65 * u.mV - time = 10 * u.ms - - # Training - optimizer = braintools.optim.Adam(lr=1e-3) - loss = braintools.metric.softmax_cross_entropy(...) - -Type Conventions ----------------- - -**States:** - -- ``ShortTermState`` - Temporary dynamics (V, g, spikes) -- ``ParamState`` - Learnable parameters (weights, biases) -- ``LongTermState`` - Persistent statistics - -**Units:** - -All physical quantities use ``brainunit``: - -- Voltage: ``u.mV`` -- Current: ``u.nA``, ``u.pA`` -- Time: ``u.ms``, ``u.second`` -- Conductance: ``u.mS``, ``u.nS`` -- Concentration: ``u.mM`` - -**Shapes:** - -- Single trial: ``(n_neurons,)`` -- Batched: ``(batch_size, n_neurons)`` -- Connectivity: ``(n_pre, n_post)`` - -See Also --------- - -**External Documentation:** - -- `BrainState Documentation `_ - State management -- `BrainUnit Documentation `_ - Physical units -- `BrainTools Documentation `_ - Training utilities -- `JAX Documentation `_ - Underlying computation + inputs \ No newline at end of file diff --git a/docs_state/core-concepts/architecture.rst b/docs_state/core-concepts/architecture.rst deleted file mode 100644 index 4b6df834..00000000 --- a/docs_state/core-concepts/architecture.rst +++ /dev/null @@ -1,609 +0,0 @@ -Architecture Overview -==================== - -``brainpy.state`` represents a complete architectural redesign built on top of the ``brainstate`` framework. This document explains the design principles and architectural components that make BrainPy 3.0 powerful and flexible. - -Design Philosophy ------------------ - -``brainpy.state`` is built around several core principles: - -**State-Based Programming** - All dynamical variables are managed as explicit states, enabling automatic differentiation, efficient compilation, and clear data flow. - -**Modular Composition** - Complex models are built by composing simple, reusable components. Each component has a well-defined interface and responsibility. - -**Scientific Accuracy** - Integration with ``brainunit`` ensures physical correctness and prevents unit-related errors. - -**Performance by Default** - JIT compilation and optimization are built into the framework, not an afterthought. - -**Extensibility** - Adding new neuron models, synapse types, or learning rules is straightforward and follows clear patterns. - -Architectural Layers --------------------- - -BrainPy 3.0 is organized into several layers: - -.. code-block:: text - - ┌─────────────────────────────────────────┐ - │ User Models & Networks │ ← Your code - ├─────────────────────────────────────────┤ - │ BrainPy Components Layer │ ← Neurons, Synapses, Projections - ├─────────────────────────────────────────┤ - │ BrainState Framework │ ← State management, compilation - ├─────────────────────────────────────────┤ - │ JAX + XLA Backend │ ← JIT compilation, autodiff - └─────────────────────────────────────────┘ - -1. JAX + XLA Backend -~~~~~~~~~~~~~~~~~~~~ - -The foundation layer provides: - -- Just-In-Time (JIT) compilation -- Automatic differentiation -- Hardware acceleration (CPU/GPU/TPU) -- Functional transformations (vmap, grad, etc.) - -2. BrainState Framework -~~~~~~~~~~~~~~~~~~~~~~~~ - -Built on JAX, ``brainstate`` provides: - -- State management system -- Module composition -- Compilation and optimization -- Program transformations (for_loop, etc.) - -3. BrainPy Components -~~~~~~~~~~~~~~~~~~~~~ - -High-level neuroscience-specific components: - -- Neuron models (LIF, ALIF, etc.) -- Synapse models (Expon, Alpha, etc.) -- Projection architectures -- Learning rules and plasticity - -4. User Models -~~~~~~~~~~~~~~ - -Your custom networks and experiments built using BrainPy components. - -State Management System ------------------------ - -The Foundation: brainstate.State -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Everything in ``brainpy.state`` revolves around **states**: - -.. code-block:: python - - import brainstate - - # Create a state - voltage = brainstate.State(0.0) # Single value - weights = brainstate.State([[0.1, 0.2], [0.3, 0.4]]) # Matrix - -States are special containers that: - -- Track their values across time -- Support automatic differentiation -- Enable efficient compilation -- Handle batching automatically - -State Types -~~~~~~~~~~~ - -BrainPy uses different state types for different purposes: - -**ParamState** - Trainable Parameters - Used for weights, time constants, and other trainable parameters. - - .. code-block:: python - - class MyNeuron(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.tau = brainstate.ParamState(10.0) # Trainable - self.weight = brainstate.ParamState([[0.1, 0.2]]) - -**ShortTermState** - Temporary Variables - Used for membrane potentials, synaptic currents, and other dynamics. - - .. code-block:: python - - class MyNeuron(brainstate.nn.Module): - def __init__(self, size): - super().__init__() - self.V = brainstate.ShortTermState(jnp.zeros(size)) # Dynamic - self.spike = brainstate.ShortTermState(jnp.zeros(size)) - -State Initialization -~~~~~~~~~~~~~~~~~~~~ - -States can be initialized with various strategies: - -.. code-block:: python - - import braintools - import brainunit as u - - # Constant initialization - V = brainstate.ShortTermState( - braintools.init.Constant(-65.0, unit=u.mV)(size) - ) - - # Normal distribution - V = brainstate.ShortTermState( - braintools.init.Normal(-65.0, 5.0, unit=u.mV)(size) - ) - - # Uniform distribution - weights = brainstate.ParamState( - braintools.init.Uniform(0.0, 1.0)(shape) - ) - -Module System -------------- - -Base Class: brainstate.nn.Module -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -All BrainPy components inherit from ``brainstate.nn.Module``: - -.. code-block:: python - - class MyComponent(brainstate.nn.Module): - def __init__(self, size): - super().__init__() - # Initialize states - self.state1 = brainstate.ShortTermState(...) - self.param1 = brainstate.ParamState(...) - - def update(self, input): - # Define dynamics - pass - -Benefits of Module: - -- Automatic state registration -- Nested module support -- State collection and filtering -- Serialization support - -Module Composition -~~~~~~~~~~~~~~~~~~ - -Modules can contain other modules: - -.. code-block:: python - - import brainpy - - class Network(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.neurons = brainpy.LIF(100) # Neuron module - self.synapse = brainpy.Expon(100) # Synapse module - self.projection = brainpy.AlignPostProj(...) # Projection module - - def update(self, input): - # Compose behavior - self.projection(spikes) - self.neurons(input) - -Component Architecture ----------------------- - -Neurons -~~~~~~~ - -Neurons model the dynamics of neural populations: - -.. code-block:: python - - class Neuron(brainstate.nn.Module): - def __init__(self, size, ...): - super().__init__() - # Membrane potential - self.V = brainstate.ShortTermState(...) - # Spike output - self.spike = brainstate.ShortTermState(...) - - def update(self, input_current): - # Update membrane potential - # Generate spikes - pass - -Key responsibilities: - -- Maintain membrane potential -- Generate spikes when threshold is crossed -- Reset after spiking -- Integrate input currents - -Synapses -~~~~~~~~ - -Synapses model temporal filtering of spike trains: - -.. code-block:: python - - class Synapse(brainstate.nn.Module): - def __init__(self, size, tau, ...): - super().__init__() - # Synaptic conductance/current - self.g = brainstate.ShortTermState(...) - - def update(self, spike_input): - # Update synaptic variable - # Return filtered output - pass - -Key responsibilities: - -- Filter spike inputs temporally -- Model synaptic dynamics (exponential, alpha, etc.) -- Provide smooth currents to postsynaptic neurons - -Projections: The Comm-Syn-Out Pattern -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Projections connect populations using a three-stage architecture: - -.. code-block:: text - - Presynaptic Spikes → [Comm] → [Syn] → [Out] → Postsynaptic Neurons - │ │ │ - Connectivity │ Current - & Weights Dynamics Injection - -**Communication (Comm)** - Handles spike transmission, connectivity, and weights. - - .. code-block:: python - - comm = brainstate.nn.EventFixedProb( - pre_size, post_size, prob=0.1, weight=0.5 - ) - -**Synaptic Dynamics (Syn)** - Temporal filtering of transmitted spikes. - - .. code-block:: python - - syn = brainpy.Expon.desc(post_size, tau=5*u.ms) - -**Output Mechanism (Out)** - How synaptic variables affect postsynaptic neurons. - - .. code-block:: python - - out = brainpy.state.CUBA.desc() # Current-based - # or - out = brainpy.state.COBA.desc() # Conductance-based - -**Complete Projection** - -.. code-block:: python - - projection = brainpy.state.AlignPostProj( - comm=comm, - syn=syn, - out=out, - post=postsynaptic_neurons - ) - -This separation provides: - -- Clear responsibility boundaries -- Easy component swapping -- Reusable building blocks -- Better testing and debugging - -Compilation and Execution --------------------------- - -Time-Stepped Simulation -~~~~~~~~~~~~~~~~~~~~~~~ - -BrainPy uses discrete time steps: - -.. code-block:: python - - import brainunit as u - - # Set global time step - brainstate.environ.set(dt=0.1 * u.ms) - - # Define simulation duration - times = u.math.arange(0*u.ms, 1000*u.ms, brainstate.environ.get_dt()) - - # Run simulation - results = brainstate.transform.for_loop( - network.update, - times, - pbar=brainstate.transform.ProgressBar(10) - ) - -JIT Compilation -~~~~~~~~~~~~~~~ - -Functions are compiled for performance: - -.. code-block:: python - - @brainstate.transform.jit - def simulate_step(input): - return network.update(input) - - # First call: compile - result = simulate_step(input) # Slow (compilation) - - # Subsequent calls: fast - result = simulate_step(input) # Fast (compiled) - -Compilation benefits: - -- 10-100x speedup over Python -- Automatic GPU/TPU dispatch -- Memory optimization -- Fusion of operations - -Gradient Computation -~~~~~~~~~~~~~~~~~~~~ - -For training, gradients are computed automatically: - -.. code-block:: python - - def loss_fn(): - predictions = network.forward(inputs) - return compute_loss(predictions, targets) - - # Compute gradients - grads, loss = brainstate.transform.grad( - loss_fn, - network.states(brainstate.ParamState), - return_value=True - )() - - # Update parameters - optimizer.update(grads) - -Physical Units System ---------------------- - -Integration with brainunit -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -``brainpy.state`` integrates ``brainunit`` for scientific accuracy: - -.. code-block:: python - - import brainunit as u - - # Define with units - tau = 10 * u.ms - threshold = -50 * u.mV - current = 5 * u.nA - - # Units are checked automatically - neuron = brainpy.state.LIF(100, tau=tau, V_th=threshold) - -Benefits: - -- Prevents unit errors (e.g., ms vs s) -- Self-documenting code -- Automatic unit conversions -- Scientific correctness - -Unit Operations -~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Arithmetic with units - total_time = 100 * u.ms + 0.5 * u.second # → 600 ms - - # Unit conversion - time_in_seconds = (100 * u.ms).to_decimal(u.second) # → 0.1 - - # Unit checking (automatic in BrainPy operations) - voltage = -65 * u.mV - current = 2 * u.nA - resistance = voltage / current # Automatically gives MΩ - -Ecosystem Integration ---------------------- - -``brainpy.state`` integrates tightly with its ecosystem: - -braintools -~~~~~~~~~~ - -Utilities and tools: - -.. code-block:: python - - import braintools - - # Optimizers - optimizer = braintools.optim.Adam(lr=1e-3) - - # Initializers - init = braintools.init.KaimingNormal() - - # Surrogate gradients - spike_fn = braintools.surrogate.ReluGrad() - - # Metrics - loss = braintools.metric.cross_entropy(pred, target) - -brainunit -~~~~~~~~~ - -Physical units: - -.. code-block:: python - - import brainunit as u - - # All standard SI units - time = 10 * u.ms - voltage = -65 * u.mV - current = 2 * u.nA - -brainstate -~~~~~~~~~~ - -Core framework (used automatically): - -.. code-block:: python - - import brainstate - - # Module system - class Net(brainstate.nn.Module): ... - - # Compilation - @brainstate.transform.jit - def fn(): ... - - # Transformations - result = brainstate.transform.for_loop(...) - -Data Flow Example ------------------ - -Here's how data flows through a typical ``brainpy.state`` simulation: - -.. code-block:: python - - # 1. Define network - class EINetwork(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.E = brainpy.state.LIF(800) # States: V, spike - self.I = brainpy.state.LIF(200) # States: V, spike - self.E2E = brainpy.state.AlignPostProj(...) # States: g (in synapse) - self.E2I = brainpy.state.AlignPostProj(...) - self.I2E = brainpy.state.AlignPostProj(...) - self.I2I = brainpy.state.AlignPostProj(...) - - def update(self, input): - # Get spikes from last time step - e_spikes = self.E.get_spike() - i_spikes = self.I.get_spike() - - # Update projections (spikes → synaptic currents) - self.E2E(e_spikes) # Updates E2E.syn.g - self.E2I(e_spikes) - self.I2E(i_spikes) - self.I2I(i_spikes) - - # Update neurons (currents → new V and spikes) - self.E(input) # Updates E.V and E.spike - self.I(input) # Updates I.V and I.spike - - return e_spikes, i_spikes - - # 2. Initialize - net = EINetwork() - brainstate.nn.init_all_states(net) - - # 3. Compile - @brainstate.transform.jit - def step(input): - return net.update(input) - - # 4. Simulate - times = u.math.arange(0*u.ms, 1000*u.ms, 0.1*u.ms) - results = brainstate.transform.for_loop(step, times) - -State Flow: - -.. code-block:: text - - Time t: - ┌──────────────────────────────────────────┐ - │ States at t-1: │ - │ E.V[t-1], E.spike[t-1] │ - │ I.V[t-1], I.spike[t-1] │ - │ E2E.syn.g[t-1], ... │ - └──────────────────────────────────────────┘ - ↓ - ┌──────────────────────────────────────────┐ - │ Projection Updates: │ - │ E2E.syn.g[t] = f(g[t-1], E.spike[t-1])│ - │ ... (other projections) │ - └──────────────────────────────────────────┘ - ↓ - ┌──────────────────────────────────────────┐ - │ Neuron Updates: │ - │ E.V[t] = f(V[t-1], Σ currents[t]) │ - │ E.spike[t] = E.V[t] >= V_th │ - │ ... (other neurons) │ - └──────────────────────────────────────────┘ - ↓ - Time t+1... - -Performance Considerations --------------------------- - -Memory Management -~~~~~~~~~~~~~~~~~ - -- States are preallocated -- In-place updates when possible -- Efficient batching support -- Automatic garbage collection - -Compilation Strategy -~~~~~~~~~~~~~~~~~~~~ - -- Compile simulation loops -- Batch operations when possible -- Use ``for_loop`` for long sequences -- Leverage JAX's XLA optimization - -Hardware Acceleration -~~~~~~~~~~~~~~~~~~~~~ - -- Automatic GPU dispatch for large arrays -- TPU support for massive parallelism -- Efficient CPU fallback for small problems - -Summary -------- - -``brainpy.state`` 's architecture provides: - -✅ **Clear Abstractions**: Neurons, synapses, and projections with well-defined roles - -✅ **State Management**: Explicit, efficient handling of dynamical variables - -✅ **Modularity**: Compose complex models from simple components - -✅ **Performance**: JIT compilation and hardware acceleration - -✅ **Scientific Accuracy**: Integrated physical units - -✅ **Extensibility**: Easy to add custom components - -✅ **Modern Design**: Built on proven frameworks (JAX, brainstate) - -Next Steps ----------- - -- Learn about specific components: :doc:`neurons`, :doc:`synapses`, :doc:`projections` -- Understand state management in depth: :doc:`state-management` -- See practical examples: :doc:`../tutorials/basic/01-lif-neuron` -- Explore the ecosystem: `brainstate docs `_ diff --git a/docs_state/core-concepts/neurons.rst b/docs_state/core-concepts/neurons.rst deleted file mode 100644 index 9aa84460..00000000 --- a/docs_state/core-concepts/neurons.rst +++ /dev/null @@ -1,598 +0,0 @@ -Neurons -======= - -Neurons are the fundamental computational units in ``brainpy.state``. This document explains how neurons work, what models are available, and how to use and create them. - -Overview --------- - -In ``brainpy.state``, neurons model the dynamics of neural populations. Each neuron model: - -- Maintains **membrane potential** (voltage) -- Integrates **input currents** -- Generates **spikes** when threshold is crossed -- **Resets** after spiking (various strategies) - -All neuron models inherit from the base ``Neuron`` class and follow consistent interfaces. - -Basic Usage ------------ - -Creating Neurons -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import brainpy - import brainunit as u - - # Create a population of 100 LIF neurons - neurons = brainpy.state.LIF( - size=100, - V_rest=-65. * u.mV, - V_th=-50. * u.mV, - V_reset=-65. * u.mV, - tau=10. * u.ms - ) - -Initializing States -~~~~~~~~~~~~~~~~~~~ - -Before simulation, initialize neuron states: - -.. code-block:: python - - import brainstate - - # Initialize all states to default values - brainstate.nn.init_all_states(neurons) - - # Or with specific batch size - brainstate.nn.init_all_states(neurons, batch_size=32) - -Running Neurons -~~~~~~~~~~~~~~~ - -Update neurons by calling them with input current: - -.. code-block:: python - - # Single time step - input_current = 2.0 * u.nA - neurons(input_current) - - # Access results - voltage = neurons.V.value # Membrane potential - spikes = neurons.get_spike() # Spike output - -Available Neuron Models ------------------------ - -IF (Integrate-and-Fire) -~~~~~~~~~~~~~~~~~~~~~~~ - -The simplest spiking neuron model. - -**Mathematical Model:** - -.. math:: - - \\tau \\frac{dV}{dt} = -V + R \\cdot I(t) - -**Spike condition:** If :math:`V \\geq V_{th}`, emit spike and reset. - -**Example:** - -.. code-block:: python - - neuron = brainpy.state.IF( - size=100, - V_rest=0. * u.mV, - V_th=1. * u.mV, - V_reset=0. * u.mV, - tau=20. * u.ms, - R=1. * u.ohm - ) - -**Parameters:** - -- ``size``: Number of neurons -- ``V_rest``: Resting potential -- ``V_th``: Spike threshold -- ``V_reset``: Reset potential after spike -- ``tau``: Membrane time constant -- ``R``: Input resistance - -**Use cases:** - -- Simple rate coding -- Fast simulations -- Theoretical studies - -LIF (Leaky Integrate-and-Fire) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The most commonly used spiking neuron model. - -**Mathematical Model:** - -.. math:: - - \\tau \\frac{dV}{dt} = -(V - V_{rest}) + R \\cdot I(t) - -**Spike condition:** If :math:`V \\geq V_{th}`, emit spike and reset. - -**Example:** - -.. code-block:: python - - neuron = brainpy.state.LIF( - size=100, - V_rest=-65. * u.mV, - V_th=-50. * u.mV, - V_reset=-65. * u.mV, - tau=10. * u.ms, - R=1. * u.ohm, - V_initializer=braintools.init.Normal(-65., 5., unit=u.mV) - ) - -**Parameters:** - -All IF parameters, plus: - -- ``V_initializer``: How to initialize membrane potential - -**Key Features:** - -- Leak toward resting potential -- Realistic temporal integration -- Well-studied dynamics - -**Use cases:** - -- General spiking neural networks -- Cortical neuron modeling -- Learning and training - -LIFRef (LIF with Refractory Period) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -LIF neuron with absolute refractory period. - -**Mathematical Model:** - -Same as LIF, but after spiking: - -- Neuron is "frozen" for refractory period -- No integration during refractory period -- More biologically realistic - -**Example:** - -.. code-block:: python - - neuron = brainpy.state.LIFRef( - size=100, - V_rest=-65. * u.mV, - V_th=-50. * u.mV, - V_reset=-65. * u.mV, - tau=10. * u.ms, - tau_ref=2. * u.ms, # Refractory period - R=1. * u.ohm - ) - -**Additional Parameters:** - -- ``tau_ref``: Refractory period duration - -**Key Features:** - -- Absolute refractory period -- Prevents immediate re-firing -- More realistic spike timing - -**Use cases:** - -- Precise temporal coding -- Biological realism -- Rate regulation - -ALIF (Adaptive Leaky Integrate-and-Fire) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -LIF with spike-frequency adaptation. - -**Mathematical Model:** - -.. math:: - - \\tau \\frac{dV}{dt} &= -(V - V_{rest}) - R \\cdot w + R \\cdot I(t) - - \\tau_w \\frac{dw}{dt} &= -w - -When spike occurs: :math:`w \\leftarrow w + \\beta` - -**Example:** - -.. code-block:: python - - neuron = brainpy.state.ALIF( - size=100, - V_rest=-65. * u.mV, - V_th=-50. * u.mV, - V_reset=-65. * u.mV, - tau=10. * u.ms, - tau_w=200. * u.ms, # Adaptation time constant - beta=0.01, # Adaptation strength - R=1. * u.ohm - ) - -**Additional Parameters:** - -- ``tau_w``: Adaptation time constant -- ``beta``: Adaptation increment per spike - -**Key Features:** - -- Spike-frequency adaptation -- Reduced firing with sustained input -- More complex dynamics - -**Use cases:** - -- Cortical neuron modeling -- Sensory adaptation -- Complex temporal patterns - -Reset Modes ------------ - -BrainPy supports different reset behaviors after spiking: - -Soft Reset (Default) -~~~~~~~~~~~~~~~~~~~~ - -Subtract threshold from membrane potential: - -.. math:: - - V \\leftarrow V - V_{th} - -.. code-block:: python - - neuron = brainpy.state.LIF(..., spk_reset='soft') - -**Properties:** - -- Preserves extra charge above threshold -- Allows rapid re-firing -- Common in machine learning - -Hard Reset -~~~~~~~~~~ - -Reset to fixed potential: - -.. math:: - - V \\leftarrow V_{reset} - -.. code-block:: python - - neuron = brainpy.state.LIF(..., spk_reset='hard') - -**Properties:** - -- Discards extra charge -- More biologically realistic -- Prevents immediate re-firing - -Choosing Reset Mode -~~~~~~~~~~~~~~~~~~~~ - -- **Soft reset**: Machine learning, rate coding, fast oscillations -- **Hard reset**: Biological modeling, temporal coding, realism - -Spike Functions ---------------- - -For training spiking neural networks, use surrogate gradients: - -.. code-block:: python - - import braintools - - neuron = brainpy.state.LIF( - size=100, - ..., - spk_fun=braintools.surrogate.ReluGrad() - ) - -Available surrogate functions: - -- ``ReluGrad()``: ReLU-like gradient -- ``SigmoidGrad()``: Sigmoid-like gradient -- ``GaussianGrad()``: Gaussian-like gradient -- ``SuperSpike()``: SuperSpike surrogate - -See :doc:`../tutorials/advanced/03-snn-training` for training details. - -Advanced Features ------------------ - -Initialization Strategies -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Different ways to initialize membrane potential: - -.. code-block:: python - - import braintools - - # Constant initialization - neuron = brainpy.state.LIF( - size=100, - V_initializer=braintools.init.Constant(-65., unit=u.mV) - ) - - # Normal distribution - neuron = brainpy.state.LIF( - size=100, - V_initializer=braintools.init.Normal(-65., 5., unit=u.mV) - ) - - # Uniform distribution - neuron = brainpy.state.LIF( - size=100, - V_initializer=braintools.init.Uniform(-70., -60., unit=u.mV) - ) - -Accessing Neuron States -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Membrane potential (with units) - voltage = neuron.V.value # Quantity with units - - # Spike output (binary or real-valued) - spikes = neuron.get_spike() - - # Access underlying array (without units) - voltage_array = neuron.V.value.to_decimal(u.mV) - -Batched Simulation -~~~~~~~~~~~~~~~~~~ - -Simulate multiple trials in parallel: - -.. code-block:: python - - # Initialize with batch dimension - brainstate.nn.init_all_states(neuron, batch_size=32) - - # Input shape: (batch_size,) or (batch_size, size) - input_current = jnp.ones((32, 100)) * 2.0 * u.nA - neuron(input_current) - - # Output shape: (batch_size, size) - spikes = neuron.get_spike() - -Complete Example ----------------- - -Here's a complete example simulating a LIF neuron: - -.. code-block:: python - - import brainpy - import brainstate - import brainunit as u - import matplotlib.pyplot as plt - - # Set time step - brainstate.environ.set(dt=0.1 * u.ms) - - # Create neuron - neuron = brainpy.state.LIF( - size=1, - V_rest=-65. * u.mV, - V_th=-50. * u.mV, - V_reset=-65. * u.mV, - tau=10. * u.ms, - spk_reset='hard' - ) - - # Initialize - brainstate.nn.init_all_states(neuron) - - # Simulation parameters - duration = 200. * u.ms - dt = brainstate.environ.get_dt() - times = u.math.arange(0. * u.ms, duration, dt) - - # Input current (step input) - def get_input(t): - return 2.0 * u.nA if t > 50*u.ms else 0.0 * u.nA - - # Run simulation - voltages = [] - spikes = [] - - for t in times: - neuron(get_input(t)) - voltages.append(neuron.V.value) - spikes.append(neuron.get_spike()) - - # Plot results - voltages = u.math.asarray(voltages) - times_plot = times.to_decimal(u.ms) - voltages_plot = voltages.to_decimal(u.mV) - - plt.figure(figsize=(10, 4)) - plt.plot(times_plot, voltages_plot) - plt.axhline(y=-50, color='r', linestyle='--', label='Threshold') - plt.xlabel('Time (ms)') - plt.ylabel('Membrane Potential (mV)') - plt.title('LIF Neuron Response') - plt.legend() - plt.grid(True, alpha=0.3) - plt.show() - -Creating Custom Neurons ------------------------- - -You can create custom neuron models by inheriting from ``Neuron``: - -.. code-block:: python - - import brainstate - from brainpy.state import Neuron - - class MyNeuron(Neuron): - def __init__(self, size, tau, V_th, **kwargs): - super().__init__(size, **kwargs) - - # Store parameters - self.tau = tau - self.V_th = V_th - - # Initialize states - self.V = brainstate.ShortTermState( - braintools.init.Constant(0., unit=u.mV)(size) - ) - self.spike = brainstate.ShortTermState( - jnp.zeros(size) - ) - - def update(self, x): - # Get time step - dt = brainstate.environ.get_dt() - - # Update membrane potential (custom dynamics) - dV = (-self.V.value + x) / self.tau * dt - V_new = self.V.value + dV - - # Check for spikes - spike = (V_new >= self.V_th).astype(float) - - # Reset - V_new = jnp.where(spike > 0, 0. * u.mV, V_new) - - # Update states - self.V.value = V_new - self.spike.value = spike - - return spike - - def get_spike(self): - return self.spike.value - -Usage: - -.. code-block:: python - - neuron = MyNeuron(size=100, tau=10*u.ms, V_th=1*u.mV) - brainstate.nn.init_all_states(neuron) - neuron(input_current) - -Performance Tips ----------------- - -1. **Use JIT compilation** for repeated simulations: - - .. code-block:: python - - @brainstate.transform.jit - def simulate_step(input): - neuron(input) - return neuron.V.value - -2. **Batch multiple trials** for parallelism: - - .. code-block:: python - - brainstate.nn.init_all_states(neuron, batch_size=100) - -3. **Use appropriate data types**: - - .. code-block:: python - - # Float32 is usually sufficient and faster - brainstate.environ.set(dtype=jnp.float32) - -4. **Preallocate arrays** when recording: - - .. code-block:: python - - n_steps = len(times) - voltages = jnp.zeros((n_steps, neuron.size)) - -Common Patterns ---------------- - -Rate Coding -~~~~~~~~~~~ - -Neurons encoding information in firing rate: - -.. code-block:: python - - neuron = brainpy.state.LIF(100, tau=10*u.ms, spk_reset='soft') - # Use soft reset for higher firing rates - -Temporal Coding -~~~~~~~~~~~~~~~ - -Neurons encoding information in spike timing: - -.. code-block:: python - - neuron = brainpy.state.LIFRef( - 100, - tau=10*u.ms, - tau_ref=2*u.ms, - spk_reset='hard' - ) - # Use refractory period for precise timing - -Burst Firing -~~~~~~~~~~~~ - -Neurons with bursting behavior: - -.. code-block:: python - - neuron = brainpy.state.ALIF( - 100, - tau=10*u.ms, - tau_w=200*u.ms, - beta=0.01, - spk_reset='soft' - ) - # Adaptation creates bursting patterns - -Summary -------- - -Neurons in ``brainpy.state``: - -✅ **Multiple models**: IF, LIF, LIFRef, ALIF - -✅ **Physical units**: All parameters with proper units - -✅ **Flexible reset**: Soft or hard reset modes - -✅ **Training-ready**: Surrogate gradients for learning - -✅ **High performance**: JIT compilation and batching - -✅ **Extensible**: Easy to create custom models - -Next Steps ----------- - -- Learn about :doc:`synapses` to connect neurons -- Explore :doc:`projections` for network connectivity -- Follow :doc:`../tutorials/basic/01-lif-neuron` for hands-on practice -- See :doc:`../examples/classical-networks/ei-balanced` for network examples diff --git a/docs_state/core-concepts/projections.rst b/docs_state/core-concepts/projections.rst deleted file mode 100644 index c68960f3..00000000 --- a/docs_state/core-concepts/projections.rst +++ /dev/null @@ -1,896 +0,0 @@ -Projections: Connecting Neural Populations -========================================== - -Projections are ``brainpy.state`` 's mechanism for connecting neural populations. -They implement the **Communication-Synapse-Output (Comm-Syn-Out)** architecture, -which separates connectivity, synaptic dynamics, and output computation into modular components. - -This guide provides a comprehensive understanding of projections in ``brainpy.state``. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Overview --------- - -What are Projections? -~~~~~~~~~~~~~~~~~~~~~ - -A **projection** connects a presynaptic population to a postsynaptic population through: - -1. **Communication (Comm)**: How spikes propagate through connections -2. **Synapse (Syn)**: Temporal filtering and synaptic dynamics -3. **Output (Out)**: How synaptic currents affect postsynaptic neurons - -**Key benefits:** - -- Modular design (swap components independently) -- Biologically realistic (separate connectivity and dynamics) -- Efficient (optimized sparse operations) -- Flexible (combine components in different ways) - -The Comm-Syn-Out Architecture -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: text - - Presynaptic Communication Synapse Output Postsynaptic - Population ──► (Connectivity) ──► (Dynamics) ──► (Current) ──► Population - - Spikes ──► Weight matrix ──► g(t) ──► I_syn ──► Neurons - Sparse/Dense Expon/Alpha CUBA/COBA - -**Flow:** - -1. Presynaptic spikes arrive -2. Communication: Spikes propagate through connectivity matrix -3. Synapse: Temporal dynamics filter the signal -4. Output: Convert to current/conductance -5. Postsynaptic neurons receive input - -Types of Projections -~~~~~~~~~~~~~~~~~~~~~ - -BrainPy provides two main projection types: - -**AlignPostProj** - - Align synaptic states with postsynaptic neurons - - Most common for standard neural networks - - Efficient memory layout - -**AlignPreProj** - - Align synaptic states with presynaptic neurons - - Useful for certain learning rules - - Different memory organization - -For most use cases, use ``AlignPostProj``. - -Communication Layer -------------------- - -The Communication layer defines **how spikes propagate** through connections. - -Dense Connectivity -~~~~~~~~~~~~~~~~~~ - -All neurons potentially connected (though weights may be zero). - -**Use case:** Small networks, fully connected layers - -.. code-block:: python - - import brainpy - import brainstate - import brainunit as u - - # Dense linear transformation - comm = brainstate.nn.Linear( - in_size=100, # Presynaptic neurons - out_size=50, # Postsynaptic neurons - w_init=brainstate.init.KaimingNormal(), - b_init=None # No bias for synapses - ) - -**Characteristics:** - -- Memory: O(n_pre × n_post) -- Computation: Full matrix multiplication -- Best for: Small networks, fully connected architectures - -Sparse Connectivity -~~~~~~~~~~~~~~~~~~~ - -Only a subset of connections exist (biologically realistic). - -**Use case:** Large networks, biological connectivity patterns - -Event-Based Fixed Probability -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Connect neurons with fixed probability. - -.. code-block:: python - - # Sparse random connectivity (2% connection probability) - comm = brainstate.nn.EventFixedProb( - pre_size=1000, - post_size=800, - prob=0.02, # 2% connectivity - weight=0.5 * u.mS # Synaptic weight - ) - -**Characteristics:** - -- Memory: O(n_pre × n_post × prob) -- Computation: Only active connections -- Best for: Large-scale networks, biological models - -Event-Based All-to-All -^^^^^^^^^^^^^^^^^^^^^^^ - -All neurons connected (but stored sparsely). - -.. code-block:: python - - # All-to-all sparse (event-driven) - comm = brainstate.nn.EventAll2All( - pre_size=100, - post_size=100, - weight=0.3 * u.mS - ) - -Event-Based One-to-One -^^^^^^^^^^^^^^^^^^^^^^^ - -One-to-one mapping (same size populations). - -.. code-block:: python - - # One-to-one connections - comm = brainstate.nn.EventOne2One( - size=100, - weight=1.0 * u.mS - ) - -**Use case:** Feedforward pathways, identity mappings - -Comparison Table -~~~~~~~~~~~~~~~~ - -.. list-table:: Communication Layer Options - :header-rows: 1 - :widths: 20 20 20 20 20 - - * - Type - - Memory - - Speed - - Use Case - - Example - * - Linear (Dense) - - High (O(n²)) - - Fast (optimized) - - Small networks - - Fully connected - * - EventFixedProb - - Low (O(n²p)) - - Very fast - - Large networks - - Cortical connectivity - * - EventAll2All - - Medium - - Fast - - Medium networks - - Recurrent layers - * - EventOne2One - - Minimal (O(n)) - - Fastest - - Feedforward - - Sensory pathways - -Synapse Layer -------------- - -The Synapse layer defines **temporal dynamics** of synaptic transmission. - -Exponential Synapse -~~~~~~~~~~~~~~~~~~~ - -Single exponential decay (most common). - -**Dynamics:** - -.. math:: - - \tau \frac{dg}{dt} = -g + \sum_k \delta(t - t_k) - -**Implementation:** - -.. code-block:: python - - # Exponential synapse with 5ms time constant - syn = brainpy.state.Expon.desc( - size=100, # Postsynaptic population size - tau=5.0 * u.ms # Decay time constant - ) - -**Characteristics:** - -- Single time constant -- Fast computation -- Good for most applications - -**When to use:** Default choice for most models - -Alpha Synapse -~~~~~~~~~~~~~ - -Dual exponential with rise and decay. - -**Dynamics:** - -.. math:: - - \tau \frac{dg}{dt} = -g + h - - \tau \frac{dh}{dt} = -h + \sum_k \delta(t - t_k) - -**Implementation:** - -.. code-block:: python - - # Alpha synapse - syn = brainpy.state.Alpha.desc( - size=100, - tau=10.0 * u.ms # Characteristic time - ) - -**Characteristics:** - -- Realistic rise time -- Smoother response -- Slightly slower computation - -**When to use:** When rise time matters, more biological realism - -NMDA Synapse -~~~~~~~~~~~~ - -Voltage-dependent NMDA receptors. - -**Dynamics:** - -.. math:: - - g_{NMDA} = \frac{g}{1 + \eta [Mg^{2+}] e^{-\gamma V}} - -**Implementation:** - -.. code-block:: python - - # NMDA receptor - syn = brainpy.state.NMDA.desc( - size=100, - tau_decay=100.0 * u.ms, # Slow decay - tau_rise=2.0 * u.ms, # Fast rise - a=0.5 / u.mM, # Mg²⁺ sensitivity - cc_Mg=1.2 * u.mM # Mg²⁺ concentration - ) - -**Characteristics:** - -- Voltage-dependent -- Slow kinetics -- Important for plasticity - -**When to use:** Long-term potentiation, working memory models - -AMPA Synapse -~~~~~~~~~~~~ - -Fast glutamatergic transmission. - -.. code-block:: python - - # AMPA receptor (fast excitation) - syn = brainpy.state.AMPA.desc( - size=100, - tau=2.0 * u.ms # Fast decay (~2ms) - ) - -**When to use:** Fast excitatory transmission - -GABA Synapse -~~~~~~~~~~~~ - -Inhibitory transmission. - -**GABAa (fast):** - -.. code-block:: python - - # GABAa receptor (fast inhibition) - syn = brainpy.state.GABAa.desc( - size=100, - tau=6.0 * u.ms # ~6ms decay - ) - -**GABAb (slow):** - -.. code-block:: python - - # GABAb receptor (slow inhibition) - syn = brainpy.state.GABAb.desc( - size=100, - tau_decay=150.0 * u.ms, # Very slow - tau_rise=3.5 * u.ms - ) - -**When to use:** -- GABAa: Fast inhibition, cortical networks -- GABAb: Slow inhibition, rhythm generation - -Custom Synapses -~~~~~~~~~~~~~~~ - -Create custom synaptic dynamics by subclassing ``Synapse``. - -.. code-block:: python - - class DoubleExpSynapse(brainpy.state.Synapse): - """Custom synapse with two time constants.""" - - def __init__(self, size, tau_fast=2*u.ms, tau_slow=10*u.ms, **kwargs): - super().__init__(size, **kwargs) - self.tau_fast = tau_fast - self.tau_slow = tau_slow - - # State variables - self.g_fast = brainstate.ShortTermState(jnp.zeros(size)) - self.g_slow = brainstate.ShortTermState(jnp.zeros(size)) - - def reset_state(self, batch_size=None): - shape = self.size if batch_size is None else (batch_size, self.size) - self.g_fast.value = jnp.zeros(shape) - self.g_slow.value = jnp.zeros(shape) - - def update(self, x): - dt = brainstate.environ.get_dt() - - # Fast component - dg_fast = -self.g_fast.value / self.tau_fast.to_decimal(u.ms) - self.g_fast.value += dg_fast * dt.to_decimal(u.ms) + x * 0.7 - - # Slow component - dg_slow = -self.g_slow.value / self.tau_slow.to_decimal(u.ms) - self.g_slow.value += dg_slow * dt.to_decimal(u.ms) + x * 0.3 - - return self.g_fast.value + self.g_slow.value - -Output Layer ------------- - -The Output layer defines **how synaptic conductance affects neurons**. - -CUBA (Current-Based) -~~~~~~~~~~~~~~~~~~~~ - -Synaptic conductance directly becomes current. - -**Model:** - -.. math:: - - I_{syn} = g_{syn} - -**Implementation:** - -.. code-block:: python - - # Current-based output - out = brainpy.state.CUBA.desc() - -**Characteristics:** - -- Simple and fast -- No voltage dependence -- Good for rate-based models - -**When to use:** -- Abstract models -- When voltage dependence not important -- Faster computation needed - -COBA (Conductance-Based) -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Synaptic conductance with reversal potential. - -**Model:** - -.. math:: - - I_{syn} = g_{syn} (E_{syn} - V_{post}) - -**Implementation:** - -.. code-block:: python - - # Excitatory conductance-based - out_exc = brainpy.state.COBA.desc(E=0.0 * u.mV) - - # Inhibitory conductance-based - out_inh = brainpy.state.COBA.desc(E=-80.0 * u.mV) - -**Characteristics:** - -- Voltage-dependent -- Biologically realistic -- Self-limiting (saturates near reversal) - -**When to use:** -- Biologically detailed models -- When voltage dependence matters -- Shunting inhibition needed - -MgBlock (NMDA) -~~~~~~~~~~~~~~ - -Voltage-dependent magnesium block for NMDA. - -.. code-block:: python - - # NMDA with Mg²⁺ block - out_nmda = brainpy.state.MgBlock.desc( - E=0.0 * u.mV, - cc_Mg=1.2 * u.mM, - alpha=0.062 / u.mV, - beta=3.57 - ) - -**When to use:** NMDA receptors, voltage-dependent plasticity - -Complete Projection Examples ------------------------------ - -Example 1: Simple Feedforward -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import brainpy as bp - import brainstate - import brainunit as u - - # Create populations - pre = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - post = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - # Create projection: 100 → 50 neurons - proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb( - pre_size=100, - post_size=50, - prob=0.1, # 10% connectivity - weight=0.5 * u.mS - ), - syn=brainpy.state.Expon.desc( - size=50, # Postsynaptic size - tau=5.0 * u.ms - ), - out=brainpy.state.CUBA.desc(), - post=post # Postsynaptic population - ) - - # Initialize - brainstate.nn.init_all_states([pre, post, proj]) - - # Simulate - def step(inp): - # Get presynaptic spikes - pre_spikes = pre.get_spike() - - # Update projection - proj(pre_spikes) - - # Update neurons - pre(inp) - post(0.0 * u.nA) # Projection provides input - - return pre.get_spike(), post.get_spike() - -Example 2: Excitatory-Inhibitory Network -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class EINetwork(brainstate.nn.Module): - def __init__(self, n_exc=800, n_inh=200): - super().__init__() - - # Populations - self.E = brainpy.state.LIF(n_exc, V_rest=-65*u.mV, V_th=-50*u.mV, tau=15*u.ms) - self.I = brainpy.state.LIF(n_inh, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - # E → E projection (AMPA, excitatory) - self.E2E = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob=0.02, weight=0.6*u.mS), - syn=brainpy.state.AMPA.desc(n_exc, tau=2.0*u.ms), - out=brainpy.state.COBA.desc(E=0.0*u.mV), - post=self.E - ) - - # E → I projection (AMPA, excitatory) - self.E2I = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_exc, n_inh, prob=0.02, weight=0.6*u.mS), - syn=brainpy.state.AMPA.desc(n_inh, tau=2.0*u.ms), - out=brainpy.state.COBA.desc(E=0.0*u.mV), - post=self.I - ) - - # I → E projection (GABAa, inhibitory) - self.I2E = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_inh, n_exc, prob=0.02, weight=6.7*u.mS), - syn=brainpy.state.GABAa.desc(n_exc, tau=6.0*u.ms), - out=brainpy.state.COBA.desc(E=-80.0*u.mV), - post=self.E - ) - - # I → I projection (GABAa, inhibitory) - self.I2I = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_inh, n_inh, prob=0.02, weight=6.7*u.mS), - syn=brainpy.state.GABAa.desc(n_inh, tau=6.0*u.ms), - out=brainpy.state.COBA.desc(E=-80.0*u.mV), - post=self.I - ) - - def update(self, inp_e, inp_i): - # Get spikes BEFORE updating neurons - spk_e = self.E.get_spike() - spk_i = self.I.get_spike() - - # Update all projections - self.E2E(spk_e) - self.E2I(spk_e) - self.I2E(spk_i) - self.I2I(spk_i) - - # Update neurons (projections provide synaptic input) - self.E(inp_e) - self.I(inp_i) - - return spk_e, spk_i - -Example 3: Multi-Timescale Synapses -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Combine AMPA (fast) and NMDA (slow) for realistic excitation. - -.. code-block:: python - - class DualExcitatory(brainstate.nn.Module): - """E → E with both AMPA and NMDA.""" - - def __init__(self, n_pre=100, n_post=100): - super().__init__() - - self.post = brainpy.state.LIF(n_post, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - # Fast AMPA component - self.ampa_proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_pre, n_post, prob=0.1, weight=0.3*u.mS), - syn=brainpy.state.AMPA.desc(n_post, tau=2.0*u.ms), - out=brainpy.state.COBA.desc(E=0.0*u.mV), - post=self.post - ) - - # Slow NMDA component - self.nmda_proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_pre, n_post, prob=0.1, weight=0.3*u.mS), - syn=brainpy.state.NMDA.desc(n_post, tau_decay=100.0*u.ms, tau_rise=2.0*u.ms), - out=brainpy.state.MgBlock.desc(E=0.0*u.mV, cc_Mg=1.2*u.mM), - post=self.post - ) - - def update(self, pre_spikes): - # Both projections share same presynaptic spikes - self.ampa_proj(pre_spikes) - self.nmda_proj(pre_spikes) - - # Post receives combined input - self.post(0.0 * u.nA) - - return self.post.get_spike() - -Advanced Topics ---------------- - -Delay Projections -~~~~~~~~~~~~~~~~~ - -Add synaptic delays to projections. - -.. code-block:: python - - # Projection with 5ms synaptic delay - proj_delayed = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(100, 100, prob=0.1, weight=0.5*u.mS), - syn=brainpy.state.Expon.desc(100, tau=5.0*u.ms), - out=brainpy.state.CUBA.desc(), - post=post_neurons, - delay=5.0 * u.ms # Synaptic delay - ) - -**Use cases:** -- Biologically realistic transmission delays -- Axonal conduction delays -- Synchronization studies - -Heterogeneous Weights -~~~~~~~~~~~~~~~~~~~~~~ - -Different weights for different connections. - -.. code-block:: python - - import jax.numpy as jnp - - # Custom weight matrix - n_pre, n_post = 100, 50 - weights = jnp.abs(brainstate.random.randn(n_pre, n_post)) * 0.5 * u.mS - - # Sparse with heterogeneous weights - comm = brainstate.nn.EventJitFPHomoLinear( - num_in=n_pre, - num_out=n_post, - prob=0.1, - weight=weights # Heterogeneous - ) - -Learning Synapses -~~~~~~~~~~~~~~~~~ - -Combine with plasticity (see :doc:`../tutorials/advanced/06-synaptic-plasticity`). - -.. code-block:: python - - # Projection with learnable weights - class PlasticProjection(brainstate.nn.Module): - def __init__(self, n_pre, n_post): - super().__init__() - - # Initialize weights as parameters - self.weights = brainstate.ParamState( - jnp.ones((n_pre, n_post)) * 0.5 * u.mS - ) - - self.proj = brainpy.state.AlignPostProj( - comm=CustomComm(self.weights), # Use learnable weights - syn=brainpy.state.Expon.desc(n_post, tau=5.0*u.ms), - out=brainpy.state.CUBA.desc(), - post=post_neurons - ) - - def update_weights(self, dw): - """Update weights based on learning rule.""" - self.weights.value += dw - -Best Practices --------------- - -Choosing Communication Type -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Use EventFixedProb when:** -- Large networks (>1000 neurons) -- Sparse connectivity (<10%) -- Biological models - -**Use Linear when:** -- Small networks (<1000 neurons) -- Fully connected layers -- Training with gradients - -**Use EventOne2One when:** -- Same-size populations -- Feedforward pathways -- Identity mappings - -Choosing Synapse Type -~~~~~~~~~~~~~~~~~~~~~~ - -**Use Expon when:** -- Default choice for most models -- Fast computation needed -- Simple dynamics sufficient - -**Use Alpha when:** -- Rise time is important -- More biological realism -- Smoother responses - -**Use AMPA/NMDA/GABA when:** -- Specific receptor types matter -- Pharmacological studies -- Detailed biological models - -Choosing Output Type -~~~~~~~~~~~~~~~~~~~~~ - -**Use CUBA when:** -- Abstract models -- Training with gradients -- Speed is critical - -**Use COBA when:** -- Biological realism needed -- Voltage dependence matters -- Shunting inhibition required - -Performance Tips -~~~~~~~~~~~~~~~~ - -1. **Sparse over Dense:** Use sparse connectivity for large networks -2. **Batch initialization:** Initialize all modules together -3. **JIT compile:** Wrap simulation loop with ``@brainstate.transform.jit`` -4. **Appropriate precision:** Use float32 unless high precision needed -5. **Minimize communication:** Group projections with same connectivity - -Common Patterns -~~~~~~~~~~~~~~~ - -**Pattern 1: Dale's Principle** - -Neurons are either excitatory OR inhibitory (not both). - -.. code-block:: python - - # Separate excitatory and inhibitory populations - E = brainpy.state.LIF(800, ...) # Excitatory - I = brainpy.state.LIF(200, ...) # Inhibitory - - # E always excitatory (E=0mV) - # I always inhibitory (E=-80mV) - -**Pattern 2: Balanced Networks** - -Excitation balanced by inhibition. - -.. code-block:: python - - # Strong inhibition to balance excitation - w_exc = 0.6 * u.mS - w_inh = 6.7 * u.mS # ~10× stronger - - # More E neurons than I (4:1 ratio) - n_exc = 800 - n_inh = 200 - -**Pattern 3: Recurrent Loops** - -Self-connections for persistent activity. - -.. code-block:: python - - # Excitatory recurrence (working memory) - E2E = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob=0.02, weight=0.5*u.mS), - syn=brainpy.state.Expon.desc(n_exc, tau=5*u.ms), - out=brainpy.state.COBA.desc(E=0*u.mV), - post=E - ) - -Troubleshooting ---------------- - -Issue: Spikes not propagating -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** Postsynaptic neurons don't receive input - -**Solutions:** - -1. Check spike timing: Call ``get_spike()`` BEFORE updating -2. Verify connectivity: Check ``prob`` and ``weight`` -3. Check update order: Projections before neurons - -.. code-block:: python - - # CORRECT order - spk = pre.get_spike() # Get spikes from previous step - proj(spk) # Update projection - pre(inp) # Update neurons - - # WRONG order - pre(inp) # Update first - spk = pre.get_spike() # Then get spikes (too late!) - proj(spk) - -Issue: Network silent or exploding -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** No activity or runaway firing - -**Solutions:** - -1. Balance E/I weights (I should be ~10× stronger) -2. Check reversal potentials (E=0mV, I=-80mV) -3. Verify threshold and reset values -4. Add external input - -.. code-block:: python - - # Balanced weights - w_exc = 0.5 * u.mS - w_inh = 5.0 * u.mS # Strong inhibition - - # Proper reversal potentials - out_exc = brainpy.state.COBA.desc(E=0.0 * u.mV) - out_inh = brainpy.state.COBA.desc(E=-80.0 * u.mV) - -Issue: Slow simulation -~~~~~~~~~~~~~~~~~~~~~~ - -**Solutions:** - -1. Use sparse connectivity (EventFixedProb) -2. Use JIT compilation -3. Use CUBA instead of COBA (if appropriate) -4. Reduce connectivity or neurons - -.. code-block:: python - - # Fast configuration - @brainstate.transform.jit - def simulate_step(net, inp): - return net(inp) - - # Sparse connectivity - comm = brainstate.nn.EventFixedProb(1000, 1000, prob=0.02, ...) - -Further Reading ---------------- - -- :doc:`../tutorials/basic/03-network-connections` - Network connections tutorial -- :doc:`architecture` - Overall BrainPy architecture -- :doc:`synapses` - Detailed synapse models -- :doc:`../tutorials/advanced/06-synaptic-plasticity` - Learning in projections -- :doc:`../tutorials/advanced/07-large-scale-simulations` - Scaling projections - -Summary -------- - -**Key takeaways:** - -✅ Projections use Comm-Syn-Out architecture - -✅ Communication: Dense (Linear) or Sparse (EventFixedProb) - -✅ Synapse: Temporal dynamics (Expon, Alpha, AMPA, GABA, NMDA) - -✅ Output: Current-based (CUBA) or Conductance-based (COBA) - -✅ Choose components based on scale, realism, and performance needs - -✅ Follow Dale's principle and balanced E/I patterns - -✅ Get spikes BEFORE updating for correct propagation - -**Quick reference:** - -.. code-block:: python - - # Standard projection template - proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_pre, n_post, prob=0.1, weight=0.5*u.mS), - syn=brainpy.state.Expon.desc(n_post, tau=5.0*u.ms), - out=brainpy.state.COBA.desc(E=0.0*u.mV), - post=post_neurons - ) - - # Usage in network - def update(self): - spk = self.pre.get_spike() # Get spikes first - self.proj(spk) # Update projection - self.pre(inp) # Update neurons - self.post(0*u.nA) diff --git a/docs_state/core-concepts/state-management.rst b/docs_state/core-concepts/state-management.rst deleted file mode 100644 index 7583189a..00000000 --- a/docs_state/core-concepts/state-management.rst +++ /dev/null @@ -1,1016 +0,0 @@ -State Management: The Foundation of ``brainpy.state`` -===================================================== - -State management is the core architectural change in ``brainpy.state``. Understanding states is -essential for using BrainPy effectively. This guide provides comprehensive coverage of the state -system built on ``brainstate``. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Overview --------- - -What is State? -~~~~~~~~~~~~~~ - -**State** is any variable that persists across function calls and can change over time. In neural simulations: - -- Membrane potentials -- Synaptic conductances -- Spike trains -- Learnable weights -- Temporary buffers - -**Key insight:** ``brainpy.state`` makes states **explicit** rather than implicit. Every stateful variable is declared and tracked. - -Why Explicit State Management? -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Problems with implicit state (BrainPy 2.x):** - -- Hard to track what changes when -- Difficult to serialize/checkpoint -- Unclear initialization procedures -- Conflicts with JAX functional programming - -**Benefits of explicit state (``brainpy.state``):** - -✅ Clear variable lifecycle - -✅ Easy checkpointing and loading - -✅ Functional programming compatible - -✅ Better debugging and introspection - -✅ Automatic differentiation support - -✅ Type safety and validation - -The State Hierarchy -~~~~~~~~~~~~~~~~~~~~ - -BrainPy uses different state types for different purposes: - -.. code-block:: text - - State (base class) - │ - ├── ParamState ← Learnable parameters (weights, biases) - ├── ShortTermState ← Temporary dynamics (V, g, spikes) - └── LongTermState ← Persistent but non-learnable (statistics) - -Each type has different semantics and handling: - -- **ParamState**: Updated by optimizers, saved in checkpoints -- **ShortTermState**: Reset each trial, not saved -- **LongTermState**: Saved but not trained - -State Types ------------ - -ParamState: Learnable Parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Use for:** Weights, biases, trainable parameters - -**Characteristics:** - -- Updated by gradient descent -- Saved in model checkpoints -- Persistent across trials -- Registered with optimizers - -**Example:** - -.. code-block:: python - - import brainstate - import jax.numpy as jnp - - class LinearLayer(brainstate.nn.Module): - def __init__(self, in_size, out_size): - super().__init__() - - # Learnable weight matrix - self.W = brainstate.ParamState( - brainstate.random.randn(in_size, out_size) * 0.01 - ) - - # Learnable bias vector - self.b = brainstate.ParamState( - jnp.zeros(out_size) - ) - - def update(self, x): - # Use parameters in computation - return jnp.dot(x, self.W.value) + self.b.value - - # Access all parameters - layer = LinearLayer(100, 50) - params = layer.states(brainstate.ParamState) - # Returns: {'W': ParamState(...), 'b': ParamState(...)} - -**Common uses:** - -- Synaptic weights -- Neural biases -- Time constants (if learning them) -- Connectivity matrices (if plastic) - -ShortTermState: Temporary Dynamics -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Use for:** Variables that reset each trial - -**Characteristics:** - -- Reset at trial start -- Not saved in checkpoints -- Represent current dynamics -- Fastest state type - -**Example:** - -.. code-block:: python - - import brainpy as bp - import brainunit as u - - class LIFNeuron(brainstate.nn.Module): - def __init__(self, size): - super().__init__() - - self.size = size - self.V_rest = -65.0 * u.mV - self.V_th = -50.0 * u.mV - - # Membrane potential (resets each trial) - self.V = brainstate.ShortTermState( - jnp.ones(size) * self.V_rest.to_decimal(u.mV) - ) - - # Spike indicator (resets each trial) - self.spike = brainstate.ShortTermState( - jnp.zeros(size) - ) - - def reset_state(self, batch_size=None): - """Called at trial start.""" - if batch_size is None: - self.V.value = jnp.ones(self.size) * self.V_rest.to_decimal(u.mV) - self.spike.value = jnp.zeros(self.size) - else: - self.V.value = jnp.ones((batch_size, self.size)) * self.V_rest.to_decimal(u.mV) - self.spike.value = jnp.zeros((batch_size, self.size)) - - def update(self, I): - # Update membrane potential - # ... (LIF dynamics) - self.V.value = new_V - self.spike.value = new_spike - -**Common uses:** - -- Membrane potentials -- Synaptic conductances -- Spike indicators -- Refractory counters -- Temporary buffers - -LongTermState: Persistent Non-Learnable -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Use for:** Statistics, counters, persistent metadata - -**Characteristics:** - -- Not reset each trial -- Saved in checkpoints -- Not updated by optimizers -- Accumulates over time - -**Example:** - -.. code-block:: python - - class NeuronWithStatistics(brainstate.nn.Module): - def __init__(self, size): - super().__init__() - - self.V = brainstate.ShortTermState(jnp.zeros(size)) - - # Running spike count (persists across trials) - self.total_spikes = brainstate.LongTermState( - jnp.zeros(size, dtype=jnp.int32) - ) - - # Running average firing rate - self.avg_rate = brainstate.LongTermState( - jnp.zeros(size) - ) - - def update(self, I): - # ... update dynamics ... - - # Accumulate statistics - self.total_spikes.value += self.spike.value.astype(jnp.int32) - -**Common uses:** - -- Spike counters -- Running averages -- Homeostatic variables -- Simulation metadata -- Custom statistics - -State Initialization --------------------- - -Automatic Initialization -~~~~~~~~~~~~~~~~~~~~~~~~ - -BrainPy provides ``init_all_states()`` for automatic initialization. - -**Basic usage:** - -.. code-block:: python - - import brainstate - - # Create network - net = MyNetwork() - - # Initialize all states (single trial) - brainstate.nn.init_all_states(net) - - # Initialize with batch dimension - brainstate.nn.init_all_states(net, batch_size=32) - -**What it does:** - -1. Finds all modules in the hierarchy -2. Calls ``reset_state()`` on each module -3. Handles nested structures automatically -4. Sets up batch dimensions if requested - -**Example with network:** - -.. code-block:: python - - class EINetwork(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.E = bp.LIF(800, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - self.I = bp.LIF(200, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - # ... projections ... - - net = EINetwork() - - # This initializes E, I, and all projections - brainstate.nn.init_all_states(net, batch_size=10) - -Manual Initialization -~~~~~~~~~~~~~~~~~~~~~ - -For custom initialization, override ``reset_state()``. - -.. code-block:: python - - class CustomNeuron(brainstate.nn.Module): - def __init__(self, size, V_init_range=(-70, -60)): - super().__init__() - self.size = size - self.V_init_range = V_init_range - - self.V = brainstate.ShortTermState(jnp.zeros(size)) - - def reset_state(self, batch_size=None): - """Custom initialization: random voltage in range.""" - - # Generate random initial voltages - low, high = self.V_init_range - if batch_size is None: - init_V = brainstate.random.uniform(low, high, size=self.size) - else: - init_V = brainstate.random.uniform(low, high, size=(batch_size, self.size)) - - self.V.value = init_V - -**Best practices:** - -- Always check ``batch_size`` parameter -- Handle both single and batched cases -- Initialize all ShortTermStates -- Don't initialize ParamStates (they're learnable) -- Don't initialize LongTermStates (they persist) - -Initializers for Parameters -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Use ``braintools.init`` for parameter initialization. - -.. code-block:: python - - import braintools.init as init - - class Network(brainstate.nn.Module): - def __init__(self, in_size, out_size): - super().__init__() - - # Xavier/Glorot initialization - self.W1 = brainstate.ParamState( - init.XavierNormal()(shape=(in_size, 100)) - ) - - # Kaiming/He initialization (for ReLU) - self.W2 = brainstate.ParamState( - init.KaimingNormal()(shape=(100, out_size)) - ) - - # Zero initialization - self.b = brainstate.ParamState( - init.Constant(0.0)(shape=(out_size,)) - ) - - # Orthogonal initialization (for RNNs) - self.W_rec = brainstate.ParamState( - init.Orthogonal()(shape=(100, 100)) - ) - -**Available initializers:** - -- ``Constant(value)`` - Fill with constant -- ``Normal(mean, std)`` - Gaussian distribution -- ``Uniform(low, high)`` - Uniform distribution -- ``XavierNormal()`` - Xavier/Glorot normal -- ``XavierUniform()`` - Xavier/Glorot uniform -- ``KaimingNormal()`` - He normal (for ReLU) -- ``KaimingUniform()`` - He uniform -- ``Orthogonal()`` - Orthogonal matrix (for RNNs) -- ``Identity()`` - Identity matrix - -State Access and Manipulation ------------------------------- - -Reading State Values -~~~~~~~~~~~~~~~~~~~~ - -Access the current value with ``.value``. - -.. code-block:: python - - neuron = bp.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - brainstate.nn.init_all_states(neuron) - - # Read current membrane potential - current_V = neuron.V.value - - # Read shape - print(current_V.shape) # (100,) - - # Read specific neurons - V_neuron_0 = neuron.V.value[0] - -Writing State Values -~~~~~~~~~~~~~~~~~~~~ - -Update state by assigning to ``.value``. - -.. code-block:: python - - # Set new value (entire array) - neuron.V.value = jnp.ones(100) * -60.0 - - # Update subset - neuron.V.value = neuron.V.value.at[0:10].set(-55.0) - - # Increment - neuron.V.value = neuron.V.value + 0.1 - -**Important:** Always assign to ``.value``, not the state object itself! - -.. code-block:: python - - # CORRECT - neuron.V.value = new_V - - # WRONG (creates new object, doesn't update state) - neuron.V = new_V - -Collecting States -~~~~~~~~~~~~~~~~~ - -Get all states of a specific type from a module. - -.. code-block:: python - - # Get all parameters - params = net.states(brainstate.ParamState) - # Returns: dict with parameter names as keys - - # Get all short-term states - short_term = net.states(brainstate.ShortTermState) - - # Get all states (any type) - all_states = net.states() - -**Example:** - -.. code-block:: python - - class SimpleNet(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.W = brainstate.ParamState(jnp.ones((10, 10))) - self.V = brainstate.ShortTermState(jnp.zeros(10)) - - net = SimpleNet() - - params = net.states(brainstate.ParamState) - # {'W': ParamState(...)} - - states = net.states(brainstate.ShortTermState) - # {'V': ShortTermState(...)} - -State in Training ------------------ - -Gradient Computation -~~~~~~~~~~~~~~~~~~~~ - -Use ``brainstate.transform.grad()`` to compute gradients w.r.t. parameters. - -.. code-block:: python - - def loss_fn(params, net, X, y): - """Loss function parameterized by params.""" - # params is automatically used by net - output = net(X) - return jnp.mean((output - y) ** 2) - - # Get parameters - params = net.states(brainstate.ParamState) - - # Compute gradients - grads = brainstate.transform.grad(loss_fn, params)(net, X, y) - - # grads has same structure as params - # grads = {'W': gradient_for_W, 'b': gradient_for_b, ...} - -**Key points:** - -- Gradients computed only for ParamState -- ShortTermState treated as constants -- Gradient structure matches parameter structure - -Optimizer Updates -~~~~~~~~~~~~~~~~~ - -Register parameters with optimizer and update. - -.. code-block:: python - - import braintools - - # Create optimizer - optimizer = braintools.optim.Adam(learning_rate=1e-3) - - # Register trainable parameters - params = net.states(brainstate.ParamState) - optimizer.register_trainable_weights(params) - - # Training loop - for epoch in range(num_epochs): - for batch in data_loader: - X, y = batch - - # Compute gradients - grads = brainstate.transform.grad( - loss_fn, - params, - return_value=False - )(net, X, y) - - # Update parameters - optimizer.update(grads) - -**The optimizer automatically:** - -- Updates all registered parameters -- Applies learning rate -- Handles momentum/adaptive rates -- Maintains optimizer state (momentum buffers, etc.) - -State Persistence -~~~~~~~~~~~~~~~~~ - -Training doesn't reset ShortTermState between batches (unless you do it manually). - -.. code-block:: python - - # Training with state reset each example - for X, y in data_loader: - # Reset dynamics for new example - brainstate.nn.init_all_states(net) - - # Forward pass (dynamics evolve) - output = net(X) - - # Backward pass - grads = compute_grads(...) - optimizer.update(grads) - - # Training with persistent state (e.g., RNN) - for X, y in data_loader: - # Don't reset - state carries over - output = net(X) - grads = compute_grads(...) - optimizer.update(grads) - -Batching --------- - -Batch Dimensions -~~~~~~~~~~~~~~~~ - -States can have a batch dimension for parallel trials. - -**Single trial:** - -.. code-block:: python - - neuron = bp.LIF(100, ...) # 100 neurons - brainstate.nn.init_all_states(neuron) - # neuron.V.value.shape = (100,) - -**Batched trials:** - -.. code-block:: python - - neuron = bp.LIF(100, ...) # 100 neurons - brainstate.nn.init_all_states(neuron, batch_size=32) - # neuron.V.value.shape = (32, 100) - -**Usage:** - -.. code-block:: python - - # Input also needs batch dimension - inp = brainstate.random.rand(32, 100) * 2.0 * u.nA - - # Update operates on all batches in parallel - neuron(inp) - - # Output has batch dimension - spikes = neuron.get_spike() # shape: (32, 100) - -Benefits of Batching -~~~~~~~~~~~~~~~~~~~~ - -**1. Parallelism:** GPU processes all batches simultaneously - -**2. Statistical averaging:** Reduce noise in gradients - -**3. Exploration:** Try different initial conditions - -**4. Efficiency:** Amortize compilation cost - -**Example: Parameter sweep with batching** - -.. code-block:: python - - # Test 10 different input currents in parallel - batch_size = 10 - neuron = bp.LIF(100, ...) - brainstate.nn.init_all_states(neuron, batch_size=batch_size) - - # Different input for each batch - currents = jnp.linspace(0, 5, batch_size).reshape(-1, 1) * u.nA - inp = jnp.broadcast_to(currents, (batch_size, 100)) - - # Simulate - for _ in range(1000): - neuron(inp) - - # Analyze each trial separately - spike_counts = jnp.sum(neuron.spike.value, axis=1) # (10,) - -Checkpointing and Serialization --------------------------------- - -Saving Models -~~~~~~~~~~~~~ - -Save model state to disk. - -.. code-block:: python - - import pickle - - # Get all states to save - state_dict = { - 'params': net.states(brainstate.ParamState), - 'long_term': net.states(brainstate.LongTermState), - 'epoch': current_epoch, - 'optimizer_state': optimizer.state_dict() # If applicable - } - - # Save to file - with open('checkpoint.pkl', 'wb') as f: - pickle.dump(state_dict, f) - -**Note:** Don't save ShortTermState (it resets each trial). - -Loading Models -~~~~~~~~~~~~~~ - -Restore model state from disk. - -.. code-block:: python - - # Load checkpoint - with open('checkpoint.pkl', 'rb') as f: - state_dict = pickle.load(f) - - # Create fresh model - net = MyNetwork() - brainstate.nn.init_all_states(net) - - # Restore parameters - params = state_dict['params'] - for name, param_state in params.items(): - # Find corresponding parameter in net - # and copy value - net_params = net.states(brainstate.ParamState) - if name in net_params: - net_params[name].value = param_state.value - - # Restore long-term states similarly - # ... - - # Restore optimizer if continuing training - optimizer.load_state_dict(state_dict['optimizer_state']) - -Best Practices for Checkpointing -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**1. Save regularly during training** - -.. code-block:: python - - if epoch % save_interval == 0: - save_checkpoint(net, optimizer, epoch, path) - -**2. Keep multiple checkpoints** - -.. code-block:: python - - # Save with epoch number - save_path = f'checkpoint_epoch_{epoch}.pkl' - -**3. Save best model separately** - -.. code-block:: python - - if val_loss < best_val_loss: - best_val_loss = val_loss - save_checkpoint(net, optimizer, epoch, 'best_model.pkl') - -**4. Include metadata** - -.. code-block:: python - - state_dict = { - 'params': ..., - 'epoch': epoch, - 'best_val_loss': best_val_loss, - 'config': model_config, # Hyperparameters - 'timestamp': datetime.now() - } - -Common Patterns ---------------- - -Pattern 1: Resetting Between Trials -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Simulate multiple trials - for trial in range(num_trials): - # Reset dynamics - brainstate.nn.init_all_states(net) - - # Run trial - for t in range(trial_length): - inp = get_input(trial, t) - output = net(inp) - record(output) - -Pattern 2: Accumulating Statistics -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class NeuronWithStats(brainstate.nn.Module): - def __init__(self, size): - super().__init__() - self.V = brainstate.ShortTermState(jnp.zeros(size)) - - # Accumulate across trials - self.total_spikes = brainstate.LongTermState( - jnp.zeros(size, dtype=jnp.int32) - ) - self.n_steps = brainstate.LongTermState(0) - - def update(self, I): - # ... dynamics ... - - # Accumulate - self.total_spikes.value += self.spike.value.astype(jnp.int32) - self.n_steps.value += 1 - - def get_firing_rate(self): - """Average firing rate across all trials.""" - dt = brainstate.environ.get_dt() - total_time = self.n_steps.value * dt.to_decimal(u.second) - return self.total_spikes.value / total_time - -Pattern 3: Conditional Updates -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class AdaptiveNeuron(brainstate.nn.Module): - def __init__(self, size): - super().__init__() - self.V = brainstate.ShortTermState(jnp.zeros(size)) - self.threshold = brainstate.ParamState(jnp.ones(size) * (-50.0)) - - def update(self, I): - # Dynamics - # ... - - # Homeostatic threshold adaptation - spike_rate = compute_spike_rate(self.spike.value) - - # Adjust threshold based on activity - target_rate = 5.0 # Hz - adjustment = 0.01 * (spike_rate - target_rate) - - # Update learnable threshold - self.threshold.value -= adjustment - -Pattern 4: Hierarchical States -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class HierarchicalNetwork(brainstate.nn.Module): - def __init__(self): - super().__init__() - - # Submodules have their own states - self.layer1 = MyLayer(100, 50) - self.layer2 = MyLayer(50, 10) - - def update(self, x): - # Each layer manages its own states - h1 = self.layer1(x) - h2 = self.layer2(h1) - return h2 - - net = HierarchicalNetwork() - - # Collect ALL states from hierarchy - all_params = net.states(brainstate.ParamState) - # Includes params from layer1 AND layer2 - - # Initialize ALL states in hierarchy - brainstate.nn.init_all_states(net) - # Calls reset_state() on net, layer1, and layer2 - -Advanced Topics ---------------- - -Custom State Types -~~~~~~~~~~~~~~~~~~ - -Create custom state types for specialized needs. - -.. code-block:: python - - class RandomState(brainstate.State): - """State that re-randomizes on reset.""" - - def __init__(self, shape, low=0.0, high=1.0): - super().__init__(jnp.zeros(shape)) - self.shape = shape - self.low = low - self.high = high - - def reset(self): - """Re-randomize on reset.""" - self.value = brainstate.random.uniform( - self.low, self.high, size=self.shape - ) - -State Sharing -~~~~~~~~~~~~~ - -Share state between modules (use with caution). - -.. code-block:: python - - class SharedState(brainstate.nn.Module): - def __init__(self): - super().__init__() - - # Shared weight matrix - shared_W = brainstate.ParamState(jnp.ones((100, 100))) - - self.module1 = ModuleA(shared_W) - self.module2 = ModuleB(shared_W) - - # module1 and module2 both modify the same weights - -**When to use:** Siamese networks, weight tying, parameter sharing - -**Caution:** Makes dependencies implicit, harder to debug - -State Inspection -~~~~~~~~~~~~~~~~ - -Debug by inspecting state values. - -.. code-block:: python - - # Print all parameter shapes - params = net.states(brainstate.ParamState) - for name, state in params.items(): - print(f"{name}: {state.value.shape}") - - # Check for NaN values - for name, state in params.items(): - if jnp.any(jnp.isnan(state.value)): - print(f"NaN detected in {name}!") - - # Compute statistics - V_values = neuron.V.value - print(f"V range: [{V_values.min():.2f}, {V_values.max():.2f}]") - print(f"V mean: {V_values.mean():.2f}") - -Troubleshooting ---------------- - -Issue: States not updating -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** Values stay constant - -**Solutions:** - -1. Assign to ``.value``, not the state itself -2. Check you're updating the right variable -3. Verify update function is called - -.. code-block:: python - - # WRONG - self.V = new_V # Creates new object! - - # CORRECT - self.V.value = new_V # Updates state - -Issue: Batch dimension errors -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** Shape mismatch errors - -**Solutions:** - -1. Initialize with ``batch_size`` parameter -2. Ensure inputs have batch dimension -3. Check ``reset_state()`` handles batching - -.. code-block:: python - - # Initialize with batching - brainstate.nn.init_all_states(net, batch_size=32) - - # Input needs batch dimension - inp = jnp.zeros((32, 100)) # (batch, neurons) - -Issue: Gradients are None -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** No gradients for parameters - -**Solutions:** - -1. Ensure parameters are ``ParamState`` -2. Check parameters are used in loss computation -3. Verify gradient function call - -.. code-block:: python - - # Parameters must be ParamState - self.W = brainstate.ParamState(init_W) # Correct - - # Compute gradients for parameters only - params = net.states(brainstate.ParamState) - grads = brainstate.transform.grad(loss_fn, params)(...) - -Issue: Memory leak during training -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** Memory grows over time - -**Solutions:** - -1. Don't accumulate history in Python lists -2. Clear unnecessary references -3. Use ``jnp.array`` operations (not Python append) - -.. code-block:: python - - # BAD - accumulates in Python memory - history = [] - for t in range(10000): - output = net(inp) - history.append(output) # Memory leak! - - # GOOD - use fixed-size buffer or don't store - for t in range(10000): - output = net(inp) - # Process immediately, don't store - -Further Reading ---------------- - -- :doc:`architecture` - Overall BrainPy architecture -- :doc:`neurons` - Neuron models and their states -- :doc:`synapses` - Synapse models and their states -- :doc:`../tutorials/advanced/05-snn-training` - Training with states -- BrainState documentation: https://brainstate.readthedocs.io/ - -Summary -------- - -**Key takeaways:** - -✅ **Three state types:** - - ``ParamState``: Learnable parameters - - ``ShortTermState``: Temporary dynamics - - ``LongTermState``: Persistent statistics - -✅ **Initialization:** - - Use ``brainstate.nn.init_all_states(module)`` - - Implement ``reset_state()`` for custom logic - - Handle batch dimensions - -✅ **Access:** - - Read/write with ``.value`` - - Collect with ``.states(StateType)`` - - Never assign to state object directly - -✅ **Training:** - - Gradients computed for ``ParamState`` - - Register with optimizer - - Update with ``optimizer.update(grads)`` - -✅ **Checkpointing:** - - Save ``ParamState`` and ``LongTermState`` - - Don't save ``ShortTermState`` - - Include metadata and optimizer state - -**Quick reference:** - -.. code-block:: python - - # Define states - class MyModule(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.W = brainstate.ParamState(init_W) # Learnable - self.V = brainstate.ShortTermState(init_V) # Resets - self.count = brainstate.LongTermState(init_c) # Persists - - def reset_state(self, batch_size=None): - """Initialize ShortTermState.""" - shape = self.size if batch_size is None else (batch_size, self.size) - self.V.value = jnp.zeros(shape) - - # Initialize - brainstate.nn.init_all_states(module, batch_size=32) - - # Access - params = module.states(brainstate.ParamState) - module.V.value = new_V - - # Train - grads = brainstate.transform.grad(loss, params)(...) - optimizer.update(grads) diff --git a/docs_state/core-concepts/synapses.rst b/docs_state/core-concepts/synapses.rst deleted file mode 100644 index 378a9880..00000000 --- a/docs_state/core-concepts/synapses.rst +++ /dev/null @@ -1,643 +0,0 @@ -Synapses -======== - -Synapses model the temporal dynamics of neural connections in ``brainpy.state``. This document explains -how synapses work, what models are available, and how to use them effectively. - -Overview --------- - -Synapses provide temporal filtering of spike trains, transforming discrete spikes into continuous currents or conductances. They model: - -- **Postsynaptic potentials** (PSPs) -- **Temporal integration** of spike trains -- **Synaptic dynamics** (rise and decay) - -In BrainPy's architecture, synapses are part of the projection system: - -.. code-block:: text - - Spikes → [Connectivity] → [Synapse] → [Output] → Neurons - ↑ - Temporal filtering - -Basic Usage ------------ - -Creating Synapses -~~~~~~~~~~~~~~~~~ - -Synapses are typically created as part of projections: - -.. code-block:: python - - import brainpy - import brainunit as u - - # Create synapse descriptor - syn = brainpy.state.Expon.desc( - size=100, # Number of synapses - tau=5. * u.ms # Time constant - ) - - # Use in projection - projection = brainpy.state.AlignPostProj( - comm=..., - syn=syn, # Synapse here - out=..., - post=neurons - ) - -Synapse Lifecycle -~~~~~~~~~~~~~~~~~ - -1. **Creation**: Define synapse with `.desc()` method -2. **Integration**: Include in projection -3. **Update**: Called automatically by projection -4. **Access**: Read synaptic variables as needed - -.. code-block:: python - - # During simulation - projection(presynaptic_spikes) # Updates synapse internally - - # Access synaptic variable - synaptic_current = projection.syn.g.value - -Available Synapse Models ------------------------- - -Expon (Single Exponential) -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -The simplest and most commonly used synapse model. - -**Mathematical Model:** - -.. math:: - - \\tau \\frac{dg}{dt} = -g - -When spike arrives: :math:`g \\leftarrow g + 1` - -**Impulse Response:** - -.. math:: - - g(t) = \\exp(-t/\\tau) - -**Example:** - -.. code-block:: python - - syn = brainpy.state.Expon.desc( - size=100, - tau=5. * u.ms, - g_initializer=braintools.init.Constant(0. * u.mS) - ) - -**Parameters:** - -- ``size``: Number of synapses -- ``tau``: Decay time constant -- ``g_initializer``: Initial synaptic variable (optional) - -**Key Features:** - -- Single time constant -- Fast computation -- Instantaneous rise - -**Use cases:** - -- General-purpose modeling -- Fast simulations -- When precise kinetics are not critical - -**Behavior:** - -.. code-block:: python - - # Response to single spike at t=0 - # g(t) = exp(-t/τ) - # Fast rise, exponential decay - -Alpha Synapse -~~~~~~~~~~~~~ - -A more realistic model with non-instantaneous rise time. - -**Mathematical Model:** - -.. math:: - - \\tau \\frac{dh}{dt} &= -h - - \\tau \\frac{dg}{dt} &= -g + h - -When spike arrives: :math:`h \\leftarrow h + 1` - -**Impulse Response:** - -.. math:: - - g(t) = \\frac{t}{\\tau} \\exp(-t/\\tau) - -**Example:** - -.. code-block:: python - - syn = brainpy.state.Alpha.desc( - size=100, - tau=5. * u.ms, - g_initializer=braintools.init.Constant(0. * u.mS) - ) - -**Parameters:** - -Same as Expon, but produces alpha-shaped response. - -**Key Features:** - -- Smooth rise and fall -- Biologically realistic -- Peak at t = τ - -**Use cases:** - -- Biological realism -- Detailed cortical modeling -- When kinetics matter - -**Behavior:** - -.. code-block:: python - - # Response to single spike at t=0 - # g(t) = (t/τ) * exp(-t/τ) - # Gradual rise to peak at τ, then decay - -AMPA (Excitatory) -~~~~~~~~~~~~~~~~~ - -Models AMPA receptor dynamics for excitatory synapses. - -**Mathematical Model:** - -Similar to Alpha, but with parameters tuned for AMPA receptors. - -**Example:** - -.. code-block:: python - - syn = brainpy.state.AMPA.desc( - size=100, - tau=2. * u.ms, # Fast AMPA kinetics - g_initializer=braintools.init.Constant(0. * u.mS) - ) - -**Key Features:** - -- Fast kinetics (τ ≈ 2 ms) -- Excitatory receptor -- Biologically parameterized - -**Use cases:** - -- Excitatory synapses -- Cortical pyramidal neurons -- Biological realism - -GABAa (Inhibitory) -~~~~~~~~~~~~~~~~~~ - -Models GABAa receptor dynamics for inhibitory synapses. - -**Mathematical Model:** - -Similar to Alpha, but with parameters tuned for GABAa receptors. - -**Example:** - -.. code-block:: python - - syn = brainpy.state.GABAa.desc( - size=100, - tau=10. * u.ms, # Slower GABAa kinetics - g_initializer=braintools.init.Constant(0. * u.mS) - ) - -**Key Features:** - -- Slower kinetics (τ ≈ 10 ms) -- Inhibitory receptor -- Biologically parameterized - -**Use cases:** - -- Inhibitory synapses -- GABAergic interneurons -- Biological realism - -Synaptic Variables ------------------- - -The Descriptor Pattern -~~~~~~~~~~~~~~~~~~~~~~~ - -BrainPy synapses use a descriptor pattern: - -.. code-block:: python - - # Create descriptor (not yet instantiated) - syn_desc = brainpy.state.Expon.desc(size=100, tau=5*u.ms) - - # Instantiated within projection - projection = brainpy.state.AlignPostProj(..., syn=syn_desc, ...) - - # Access instantiated synapse - actual_synapse = projection.syn - g_value = actual_synapse.g.value - -Why Descriptors? -~~~~~~~~~~~~~~~~ - -- **Deferred instantiation**: Created when needed -- **Reusability**: Same descriptor for multiple projections -- **Flexibility**: Configure before instantiation - -Accessing Synaptic State -~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Within projection - projection = brainpy.state.AlignPostProj( - comm=..., - syn=brainpy.state.Expon.desc(100, tau=5*u.ms), - out=..., - post=neurons - ) - - # After simulation step - synaptic_var = projection.syn.g.value # Current value with units - - # Convert to array for plotting - g_array = synaptic_var.to_decimal(u.mS) - -Synaptic Dynamics Visualization --------------------------------- - -Comparing Different Models -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import brainpy as bp - import brainstate - import brainunit as u - import matplotlib.pyplot as plt - import jax.numpy as jnp - - brainstate.environ.set(dt=0.1 * u.ms) - - # Create different synapses - expon = brainpy.state.Expon(100, tau=5*u.ms) - alpha = brainpy.state.Alpha(100, tau=5*u.ms) - ampa = brainpy.state.AMPA(100, tau=2*u.ms) - gaba = brainpy.state.GABAa(100, tau=10*u.ms) - - # Initialize - for syn in [expon, alpha, ampa, gaba]: - brainstate.nn.init_all_states(syn) - - # Single spike at t=0 - spike_input = jnp.zeros(100) - spike_input = spike_input.at[0].set(1.0) - - # Simulate - times = u.math.arange(0*u.ms, 50*u.ms, 0.1*u.ms) - responses = { - 'Expon': [], - 'Alpha': [], - 'AMPA': [], - 'GABAa': [] - } - - for syn, name in zip([expon, alpha, ampa, gaba], - ['Expon', 'Alpha', 'AMPA', 'GABAa']): - brainstate.nn.init_all_states(syn) - for i, t in enumerate(times): - if i == 0: - syn(spike_input) - else: - syn(jnp.zeros(100)) - responses[name].append(syn.g.value[0]) - - # Plot - plt.figure(figsize=(10, 6)) - for name, response in responses.items(): - response_array = u.math.asarray(response) - plt.plot(times.to_decimal(u.ms), - response_array.to_decimal(u.mS), - label=name, linewidth=2) - - plt.xlabel('Time (ms)') - plt.ylabel('Synaptic Variable (mS)') - plt.title('Comparison of Synapse Models (Single Spike)') - plt.legend() - plt.grid(True, alpha=0.3) - plt.show() - -Integration with Projections ------------------------------ - -Complete Example -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import brainpy as bp - import brainstate - import brainunit as u - - # Create neurons - pre_neurons = brainpy.state.LIF(80, V_th=-50*u.mV, tau=10*u.ms) - post_neurons = brainpy.state.LIF(100, V_th=-50*u.mV, tau=10*u.ms) - - # Create projection with exponential synapse - projection = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb( - 80, 100, prob=0.1, weight=0.5*u.mS - ), - syn=brainpy.state.Expon.desc(100, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=post_neurons - ) - - # Initialize - brainstate.nn.init_all_states(pre_neurons) - brainstate.nn.init_all_states(post_neurons) - - # Simulation - def update(input_current): - # Update presynaptic neurons - pre_neurons(input_current) - - # Get spikes and propagate through projection - spikes = pre_neurons.get_spike() - projection(spikes) - - # Update postsynaptic neurons - post_neurons(0 * u.nA) - - return post_neurons.get_spike() - - # Run - times = u.math.arange(0*u.ms, 100*u.ms, 0.1*u.ms) - results = brainstate.transform.for_loop( - lambda t: update(2*u.nA), - times - ) - -Short-Term Plasticity ---------------------- - -Synapses can be combined with short-term plasticity (STP): - -.. code-block:: python - - # Create projection with STP - projection = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(80, 100, prob=0.1, weight=0.5*u.mS), - syn=brainpy.state.STP.desc( - brainpy.state.Expon.desc(100, tau=5*u.ms), # Underlying synapse - tau_f=200*u.ms, # Facilitation time constant - tau_d=150*u.ms, # Depression time constant - U=0.2 # Utilization of synaptic efficacy - ), - out=brainpy.state.CUBA.desc(), - post=post_neurons - ) - -See :doc:`plasticity` for more details on STP. - -Custom Synapses ---------------- - -Creating Custom Synapse Models -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -You can create custom synapse models by inheriting from ``Synapse``: - -.. code-block:: python - - import brainstate - from brainpy.state import Synapse - - class MyCustomSynapse(Synapse): - def __init__(self, size, tau1, tau2, **kwargs): - super().__init__(size, **kwargs) - - self.tau1 = tau1 - self.tau2 = tau2 - - # Synaptic variable - self.g = brainstate.ShortTermState( - braintools.init.Constant(0., unit=u.mS)(size) - ) - - def update(self, spike_input): - dt = brainstate.environ.get_dt() - - # Custom dynamics (double exponential) - dg = (-self.g.value / self.tau1 + - spike_input / self.tau2) - self.g.value = self.g.value + dg * dt - - return self.g.value - - @classmethod - def desc(cls, size, tau1, tau2, **kwargs): - """Descriptor for deferred instantiation.""" - def create(): - return cls(size, tau1, tau2, **kwargs) - return create - -Usage: - -.. code-block:: python - - # Create descriptor - syn_desc = MyCustomSynapse.desc( - size=100, - tau1=5*u.ms, - tau2=10*u.ms - ) - - # Use in projection - projection = brainpy.state.AlignPostProj(..., syn=syn_desc, ...) - -Choosing the Right Synapse ---------------------------- - -Decision Guide -~~~~~~~~~~~~~~ - -.. list-table:: - :header-rows: 1 - :widths: 20 30 25 25 - - * - Model - - When to Use - - Pros - - Cons - * - Expon - - General purpose, speed - - Fast, simple - - Unrealistic rise - * - Alpha - - Biological realism - - Realistic kinetics - - Slower computation - * - AMPA - - Excitatory, fast - - Biologically accurate - - Specific use case - * - GABAa - - Inhibitory, slow - - Biologically accurate - - Specific use case - -Recommendations -~~~~~~~~~~~~~~~ - -**For machine learning / SNNs:** - Use ``Expon`` for speed and simplicity. - -**For biological modeling:** - Use ``Alpha``, ``AMPA``, or ``GABAa`` for realism. - -**For cortical networks:** - - Excitatory: ``AMPA`` (τ ≈ 2 ms) - - Inhibitory: ``GABAa`` (τ ≈ 10 ms) - -**For custom dynamics:** - Implement custom synapse class. - -Performance Considerations --------------------------- - -Computational Cost -~~~~~~~~~~~~~~~~~~ - -.. list-table:: - :header-rows: 1 - :widths: 25 25 50 - - * - Model - - Relative Cost - - Notes - * - Expon - - 1x (baseline) - - Single state variable - * - Alpha - - 2x - - Two state variables - * - AMPA/GABAa - - 2x - - Similar to Alpha - -Optimization Tips -~~~~~~~~~~~~~~~~~ - -1. **Use Expon when possible**: Fastest option - -2. **Batch operations**: Multiple synapses together - - .. code-block:: python - - # Good: Single projection with 1000 synapses - proj = brainpy.state.AlignPostProj(..., syn=brainpy.state.Expon.desc(1000, ...)) - - # Bad: 1000 separate projections - projs = [brainpy.state.AlignPostProj(..., syn=brainpy.state.Expon.desc(1, ...)) - for _ in range(1000)] - -3. **JIT compilation**: Always use for simulations - - .. code-block:: python - - @brainstate.transform.jit - def step(): - projection(spikes) - neurons(0*u.nA) - -Common Patterns ---------------- - -Excitatory-Inhibitory Balance -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Excitatory projection (fast) - E_proj = brainpy.state.AlignPostProj( - comm=..., - syn=brainpy.state.Expon.desc(post_size, tau=2*u.ms), - out=brainpy.state.CUBA.desc(), - post=neurons - ) - - # Inhibitory projection (slow) - I_proj = brainpy.state.AlignPostProj( - comm=..., - syn=brainpy.state.Expon.desc(post_size, tau=10*u.ms), - out=brainpy.state.CUBA.desc(), - post=neurons - ) - -Multiple Receptor Types -~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # AMPA (fast excitatory) - ampa_proj = brainpy.state.AlignPostProj( - ..., syn=brainpy.state.AMPA.desc(size, tau=2*u.ms), ... - ) - - # NMDA (slow excitatory) - custom - nmda_proj = brainpy.state.AlignPostProj( - ..., syn=CustomNMDA.desc(size, tau=100*u.ms), ... - ) - - # GABAa (fast inhibitory) - gaba_proj = brainpy.state.AlignPostProj( - ..., syn=brainpy.state.GABAa.desc(size, tau=10*u.ms), ... - ) - -Summary -------- - -Synapses in ``brainpy.state``: - -✅ **Multiple models**: Expon, Alpha, AMPA, GABAa - -✅ **Temporal filtering**: Convert spikes to continuous signals - -✅ **Descriptor pattern**: Flexible, reusable configuration - -✅ **Integration ready**: Seamless use in projections - -✅ **Extensible**: Easy custom synapse models - -✅ **Physical units**: Proper unit handling throughout - -Next Steps ----------- - -- Learn about :doc:`projections` for complete connectivity -- Explore :doc:`plasticity` for learning rules -- Follow :doc:`../tutorials/basic/02-synapse-models` for practice -- See :doc:`../examples/classical-networks/ei-balanced` for network examples diff --git a/docs_state/examples/gallery.rst b/docs_state/examples/gallery.rst index c0197592..f94f17df 100644 --- a/docs_state/examples/gallery.rst +++ b/docs_state/examples/gallery.rst @@ -1,7 +1,7 @@ Examples Gallery ================ -Welcome to the BrainPy 3.0 examples gallery! Here you'll find complete, runnable examples demonstrating various aspects of computational neuroscience modeling. +Welcome to the ``brainpy.state`` examples gallery! Here you'll find complete, runnable examples demonstrating various aspects of computational neuroscience modeling. All examples are available in the `examples_state/ `_ directory of the BrainPy repository. @@ -10,450 +10,176 @@ Classical Network Models These examples reproduce influential models from the computational neuroscience literature. -E-I Balanced Networks -~~~~~~~~~~~~~~~~~~~~~ +.. grid:: 2 + :gutter: 3 -**102_EI_net_1996.py** - Van Vreeswijk & Sompolinsky (1996) + .. grid-item-card:: E-I Balanced Networks + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/102_EI_net_1996.py -Implements the classic excitatory-inhibitory balanced network showing chaotic dynamics. + Implements the classic excitatory-inhibitory balanced network showing chaotic dynamics. -.. code-block:: python + - 80% excitatory, 20% inhibitory neurons + - Random sparse connectivity + - Balanced excitation and inhibition + - Asynchronous irregular firing - # Key features: - - 80% excitatory, 20% inhibitory neurons - - Random sparse connectivity - - Balanced excitation and inhibition - - Asynchronous irregular firing + + .. grid-item-card:: COBA Network (2005) + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/103_COBA_2005.py + + Conductance-based synaptic integration in balanced networks. -:download:`Download <../../examples_state/102_EI_net_1996.py>` + - Conductance-based synapses (COBA) + - Reversal potentials + - More biologically realistic + - Stable asynchronous activity -**Key Concepts**: E-I balance, network dynamics, sparse connectivity ---- + .. grid-item-card:: CUBA Network (2005) + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/104_CUBA_2005.py -COBA Network (2005) -~~~~~~~~~~~~~~~~~~~ + Current-based synaptic integration (simpler, faster variant). -**103_COBA_2005.py** - Vogels & Abbott (2005) + - Current-based synapses (CUBA) + - Faster computation + - Widely used for large-scale simulations -Conductance-based synaptic integration in balanced networks. -.. code-block:: python - # Key features: - - Conductance-based synapses (COBA) - - Reversal potentials - - More biologically realistic - - Stable asynchronous activity + .. grid-item-card:: COBA with Hodgkin-Huxley Neurons (2007) + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/106_COBA_HH_2007.py -:download:`Download <../../examples_state/103_COBA_2005.py>` + More detailed neuron model with sodium and potassium channels. -**Key Concepts**: COBA synapses, conductance-based models, reversal potentials + - Hodgkin-Huxley neuron dynamics + - Action potential generation + - Biophysically detailed + - Computationally intensive ---- - -CUBA Network (2005) -~~~~~~~~~~~~~~~~~~~ - -**104_CUBA_2005.py** - Vogels & Abbott (2005) - -Current-based synaptic integration (simpler, faster variant). - -.. code-block:: python - - # Key features: - - Current-based synapses (CUBA) - - Faster computation - - Widely used for large-scale simulations - -:download:`Download <../../examples_state/104_CUBA_2005.py>` - -**Alternative**: `104_CUBA_2005_version2.py <../../examples_state/104_CUBA_2005_version2.py>`_ - Different parameterization - -**Key Concepts**: CUBA synapses, current-based models - ---- - -COBA with Hodgkin-Huxley Neurons (2007) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**106_COBA_HH_2007.py** - Conductance-based network with HH neurons - -More detailed neuron model with sodium and potassium channels. - -.. code-block:: python - - # Key features: - - Hodgkin-Huxley neuron dynamics - - Action potential generation - - Biophysically detailed - - Computationally intensive - -:download:`Download <../../examples_state/106_COBA_HH_2007.py>` - -**Key Concepts**: Hodgkin-Huxley model, ion channels, biophysical detail Oscillations and Rhythms ------------------------- -Gamma Oscillation (1996) -~~~~~~~~~~~~~~~~~~~~~~~~~ - -**107_gamma_oscillation_1996.py** - Gamma rhythm generation - -Interneuron network generating gamma oscillations (30-80 Hz). - -.. code-block:: python - - # Key features: - - Interneuron-based gamma - - Inhibition-based synchrony - - Physiologically relevant frequency - - Network oscillations - -:download:`Download <../../examples_state/107_gamma_oscillation_1996.py>` - -**Key Concepts**: Gamma oscillations, network synchrony, inhibitory networks - ---- - -Synfire Chains (199x) -~~~~~~~~~~~~~~~~~~~~~ +.. grid:: 2 + :gutter: 3 -**108_synfire_chains_199.py** - Feedforward activity propagation + .. grid-item-card:: Gamma Oscillation (1996) + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/107_gamma_oscillation_1996.py -Demonstrates reliable spike sequence propagation. + Interneuron network generating gamma oscillations (30-80 Hz). -.. code-block:: python + - Interneuron-based gamma + - Inhibition-based synchrony + - Physiologically relevant frequency + - Network oscillations - # Key features: - - Feedforward architecture - - Reliable spike timing - - Wave propagation - - Temporal coding + .. grid-item-card:: Synfire Chains (199x) + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/108_synfire_chains_199.py -:download:`Download <../../examples_state/108_synfire_chains_199.py>` + Demonstrates reliable spike sequence propagation. -**Key Concepts**: Synfire chains, feedforward networks, spike timing + - Feedforward architecture + - Reliable spike timing + - Wave propagation + - Temporal coding ---- + .. grid-item-card:: Fast Global Oscillation + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/109_fast_global_oscillation.py -Fast Global Oscillation -~~~~~~~~~~~~~~~~~~~~~~~ + High-frequency oscillations (>100 Hz) in inhibitory networks. -**109_fast_global_oscillation.py** - Ultra-fast network rhythms + - Very fast oscillations + - Gap junction coupling + - Inhibitory synchrony + - Pathological rhythms -High-frequency oscillations (>100 Hz) in inhibitory networks. - -.. code-block:: python - - # Key features: - - Very fast oscillations (>100 Hz) - - Gap junction coupling - - Inhibitory synchrony - - Pathological rhythms - -:download:`Download <../../examples_state/109_fast_global_oscillation.py>` - -**Key Concepts**: Fast oscillations, gap junctions, pathological rhythms Gamma Oscillation Mechanisms (Susin & Destexhe 2021) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Series of models exploring different gamma generation mechanisms: -**110_Susin_Destexhe_2021_gamma_oscillation_AI.py** - Asynchronous Irregular - -.. code-block:: python - - # AI state: No oscillations, irregular firing - - Background activity state - - Asynchronous firing - - No clear rhythm +.. grid:: 2 + :gutter: 3 -:download:`Download <../../examples_state/110_Susin_Destexhe_2021_gamma_oscillation_AI.py>` + .. grid-item-card:: Asynchronous Irregular (AI) + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/110_Susin_Destexhe_2021_gamma_oscillation_AI.py ---- + AI state: No oscillations, irregular firing -**111_Susin_Destexhe_2021_gamma_oscillation_CHING.py** - Coherent High-frequency INhibition-based Gamma + - Background activity state + - Asynchronous firing + - No clear rhythm -.. code-block:: python - # CHING mechanism - - Coherent inhibition - - High-frequency gamma - - Interneuron synchrony + .. grid-item-card:: CHING Mechanism + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/111_Susin_Destexhe_2021_gamma_oscillation_CHING.py -:download:`Download <../../examples_state/111_Susin_Destexhe_2021_gamma_oscillation_CHING.py>` + Coherent High-frequency INhibition-based Gamma ---- + - Coherent inhibition + - High-frequency gamma + - Interneuron synchrony -**112_Susin_Destexhe_2021_gamma_oscillation_ING.py** - Inhibition-based Gamma -.. code-block:: python + .. grid-item-card:: ING Mechanism + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/112_Susin_Destexhe_2021_gamma_oscillation_ING.py - # ING mechanism - - Pure inhibitory network - - Gamma through inhibition - - Fast synaptic kinetics + Inhibition-based Gamma -:download:`Download <../../examples_state/112_Susin_Destexhe_2021_gamma_oscillation_ING.py>` + - Pure inhibitory network + - Gamma through inhibition + - Fast synaptic kinetics ---- -**113_Susin_Destexhe_2021_gamma_oscillation_PING.py** - Pyramidal-Interneuron Gamma + .. grid-item-card:: PING Mechanism + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/113_Susin_Destexhe_2021_gamma_oscillation_PING.py -.. code-block:: python + Pyramidal-Interneuron Gamma - # PING mechanism - - E-I loop generates gamma - - Most common mechanism - - Excitatory-inhibitory interaction + - E-I loop generates gamma + - Most common mechanism + - Excitatory-inhibitory interaction -:download:`Download <../../examples_state/113_Susin_Destexhe_2021_gamma_oscillation_PING.py>` -**Combined**: `Susin_Destexhe_2021_gamma_oscillation.py <../../examples_state/Susin_Destexhe_2021_gamma_oscillation.py>`_ - All mechanisms +**Combined**: `Susin_Destexhe_2021_gamma_oscillation.py `_ - All mechanisms **Key Concepts**: Gamma mechanisms, network states, oscillation generation Spiking Neural Network Training -------------------------------- -Supervised Learning with Surrogate Gradients -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**200_surrogate_grad_lif.py** - Basic SNN training (SpyTorch tutorial reproduction) - -Trains a simple spiking network using surrogate gradients. - -.. code-block:: python - - # Key features: - - Surrogate gradient method - - LIF neuron training - - Simple classification task - - Gradient-based learning - -:download:`Download <../../examples_state/200_surrogate_grad_lif.py>` - -**Key Concepts**: Surrogate gradients, SNN training, backpropagation through time - ---- - -Fashion-MNIST Classification -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**201_surrogate_grad_lif_fashion_mnist.py** - Image classification with SNNs - -Trains a spiking network on Fashion-MNIST dataset. - -.. code-block:: python - - # Key features: - - Fashion-MNIST dataset - - Multi-layer SNN - - Spike-based processing - - Real-world classification - -:download:`Download <../../examples_state/201_surrogate_grad_lif_fashion_mnist.py>` - -**Key Concepts**: Image classification, multi-layer SNNs, practical applications - ---- - -MNIST with Readout Layer -~~~~~~~~~~~~~~~~~~~~~~~~~ - -**202_mnist_lif_readout.py** - MNIST with specialized readout - -Uses readout layer for classification. - -.. code-block:: python - - # Key features: - - MNIST handwritten digits - - Specialized readout layer - - Spike counting - - Classification from spike rates - -:download:`Download <../../examples_state/202_mnist_lif_readout.py>` - -**Key Concepts**: Readout layers, spike-based classification, MNIST - -Example Categories ------------------- - -By Difficulty -~~~~~~~~~~~~~ - -**Beginner** (Start here!) - - 102_EI_net_1996.py - Simple E-I network - - 104_CUBA_2005.py - Current-based synapses - - 200_surrogate_grad_lif.py - Basic training - -**Intermediate** - - 103_COBA_2005.py - Conductance-based synapses - - 107_gamma_oscillation_1996.py - Network oscillations - - 201_surrogate_grad_lif_fashion_mnist.py - Image classification - -**Advanced** - - 106_COBA_HH_2007.py - Biophysical detail - - 113_Susin_Destexhe_2021_gamma_oscillation_PING.py - Complex mechanisms - - Large-scale simulations (coming soon) - -By Topic -~~~~~~~~ - -**Network Dynamics** - - E-I balanced networks (102, 103, 104) - - Oscillations (107, 109, 110-113) - - Synfire chains (108) - -**Synaptic Mechanisms** - - CUBA models (104) - - COBA models (103, 106) - - Different synapse types - -**Learning and Training** - - Surrogate gradients (200, 201, 202) - - Classification tasks - - Supervised learning - -**Biophysical Models** - - Hodgkin-Huxley neurons (106) - - Detailed conductances - - Realistic parameters - -Running Examples ----------------- - -All examples can be run directly: - -.. code-block:: bash - - # Clone repository - git clone https://github.com/brainpy/BrainPy.git - cd BrainPy - - # Run an example - python examples_state/102_EI_net_1996.py - -Or in Jupyter: - -.. code-block:: python - - # In Jupyter notebook - %run examples_state/102_EI_net_1996.py - -Requirements -~~~~~~~~~~~~ - -Examples require: - -- Python 3.10+ -- BrainPy 3.0 -- matplotlib (for visualization) -- Additional dependencies as noted in examples - -Example Structure ------------------ - -Most examples follow this structure: - -.. code-block:: python - - # 1. Imports - import brainpy as bp - import brainstate - import brainunit as u - import matplotlib.pyplot as plt - - # 2. Network definition - class MyNetwork(brainstate.nn.Module): - def __init__(self): - super().__init__() - # Define components - - def update(self, input): - # Define dynamics - - # 3. Setup - brainstate.environ.set(dt=0.1 * u.ms) - net = MyNetwork() - brainstate.nn.init_all_states(net) - - # 4. Simulation - times = u.math.arange(0*u.ms, 1000*u.ms, dt) - results = brainstate.transform.for_loop(net.update, times) - - # 5. Visualization - plt.figure() - # ... plotting code ... - plt.show() - -Contributing Examples ---------------------- - -We welcome new examples! To contribute: - -1. Fork the BrainPy repository -2. Add your example to ``examples_state/`` -3. Follow naming convention: ``NNN_descriptive_name.py`` -4. Include documentation at the top -5. Submit a pull request - -Example Template: - -.. code-block:: python - - # Copyright 2024 BrainX Ecosystem Limited. - # Licensed under Apache License 2.0 - - """ - Short description of the example. - - This example demonstrates: - - Feature 1 - - Feature 2 - - References: - - Citation if reproducing paper - """ - - # Your code here... - -Additional Resources --------------------- - -**Tutorials** - For step-by-step learning, see :doc:`../tutorials/basic/01-lif-neuron` - -**API Documentation** - For detailed API reference, see :doc:`../api/neurons` +.. grid:: 2 + :gutter: 3 -**Core Concepts** - For architectural understanding, see :doc:`../core-concepts/architecture` + .. grid-item-card:: Supervised Learning with Surrogate Gradients + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/200_surrogate_grad_lif.py + Trains a simple spiking network using surrogate gradients. -Browse All Examples -------------------- + - Surrogate gradient method + - LIF neuron training + - Simple classification task + - Gradient-based learning -View all examples on GitHub: -`BrainPy-State Examples `_ + .. grid-item-card:: Fashion-MNIST Classification + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/201_surrogate_grad_lif_fashion_mnist.py -For more extensive examples and notebooks: + Trains a spiking network on Fashion-MNIST dataset. -`BrainPy Examples Repository `_ + - Fashion-MNIST dataset + - Multi-layer SNN + - Spike-based processing + - Real-world classification -Getting Help ------------- -If you have questions about examples: + .. grid-item-card:: MNIST with Readout Layer + :link: https://github.com/brainpy/BrainPy/tree/master/examples_state/202_mnist_lif_readout.py -- Open an issue on GitHub -- Check existing discussions -- Read the tutorials -- Consult the documentation + Uses readout layer for classification. -Happy modeling! 🧠 + - MNIST handwritten digits + - Specialized readout layer + - Spike counting + - Classification from spike rates diff --git a/docs_state/how-to-guides/custom-components.rst b/docs_state/how-to-guides/custom-components.rst deleted file mode 100644 index 2f2adc6c..00000000 --- a/docs_state/how-to-guides/custom-components.rst +++ /dev/null @@ -1,510 +0,0 @@ -How to Create Custom Components -================================ - -This guide shows you how to create custom neurons, synapses, and other components in BrainPy. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Quick Start ------------ - -**Custom neuron template:** - -.. code-block:: python - - import brainpy - import brainstate - import brainunit as u - import jax.numpy as jnp - - class CustomNeuron(brainpy.state.Neuron): - def __init__(self, size, **kwargs): - super().__init__(size, **kwargs) - - # Parameters - self.tau = 10.0 * u.ms - self.V_th = -50.0 * u.mV - - # States - self.V = brainstate.ShortTermState(jnp.zeros(size)) - self.spike = brainstate.ShortTermState(jnp.zeros(size)) - - def reset_state(self, batch_size=None): - shape = self.size if batch_size is None else (batch_size, self.size) - self.V.value = jnp.zeros(shape) - self.spike.value = jnp.zeros(shape) - - def update(self, x): - dt = brainstate.environ.get_dt() - - # Dynamics - dV = -self.V.value / self.tau.to_decimal(u.ms) + x.to_decimal(u.nA) - self.V.value += dV * dt.to_decimal(u.ms) - - # Spike generation - self.spike.value = (self.V.value >= self.V_th.to_decimal(u.mV)).astype(float) - - # Reset - self.V.value = jnp.where( - self.spike.value > 0, - 0.0, # Reset voltage - self.V.value - ) - - return self.V.value - - def get_spike(self): - return self.spike.value - -Custom Neurons --------------- - -Example 1: Adaptive LIF -~~~~~~~~~~~~~~~~~~~~~~~ - -**LIF with spike-frequency adaptation:** - -.. code-block:: python - - class AdaptiveLIF(brainpy.state.Neuron): - """LIF neuron with adaptation current.""" - - def __init__(self, size, tau=10*u.ms, tau_w=100*u.ms, - V_th=-50*u.mV, V_reset=-65*u.mV, a=0.1*u.nA, - b=0.5*u.nA, **kwargs): - super().__init__(size, **kwargs) - - self.tau = tau - self.tau_w = tau_w - self.V_th = V_th - self.V_reset = V_reset - self.a = a # Adaptation coupling - self.b = b # Spike-triggered adaptation - - # States - self.V = brainstate.ShortTermState(jnp.ones(size) * V_reset.to_decimal(u.mV)) - self.w = brainstate.ShortTermState(jnp.zeros(size)) # Adaptation current - self.spike = brainstate.ShortTermState(jnp.zeros(size)) - - def reset_state(self, batch_size=None): - shape = self.size if batch_size is None else (batch_size, self.size) - self.V.value = jnp.ones(shape) * self.V_reset.to_decimal(u.mV) - self.w.value = jnp.zeros(shape) - self.spike.value = jnp.zeros(shape) - - def update(self, I_ext): - dt = brainstate.environ.get_dt() - - # Membrane potential dynamics - dV = (-self.V.value + self.V_reset.to_decimal(u.mV) + I_ext.to_decimal(u.nA) - self.w.value) / self.tau.to_decimal(u.ms) - self.V.value += dV * dt.to_decimal(u.ms) - - # Adaptation dynamics - dw = (self.a.to_decimal(u.nA) * (self.V.value - self.V_reset.to_decimal(u.mV)) - self.w.value) / self.tau_w.to_decimal(u.ms) - self.w.value += dw * dt.to_decimal(u.ms) - - # Spike generation - self.spike.value = (self.V.value >= self.V_th.to_decimal(u.mV)).astype(float) - - # Reset and adaptation jump - self.V.value = jnp.where( - self.spike.value > 0, - self.V_reset.to_decimal(u.mV), - self.V.value - ) - self.w.value += self.spike.value * self.b.to_decimal(u.nA) - - return self.V.value - - def get_spike(self): - return self.spike.value - -Example 2: Izhikevich Neuron -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class Izhikevich(brainpy.state.Neuron): - """Izhikevich neuron model.""" - - def __init__(self, size, a=0.02, b=0.2, c=-65*u.mV, d=8*u.mV, **kwargs): - super().__init__(size, **kwargs) - - self.a = a - self.b = b - self.c = c - self.d = d - - # States - self.V = brainstate.ShortTermState(jnp.ones(size) * c.to_decimal(u.mV)) - self.u = brainstate.ShortTermState(jnp.zeros(size)) - self.spike = brainstate.ShortTermState(jnp.zeros(size)) - - def reset_state(self, batch_size=None): - shape = self.size if batch_size is None else (batch_size, self.size) - self.V.value = jnp.ones(shape) * self.c.to_decimal(u.mV) - self.u.value = jnp.zeros(shape) - self.spike.value = jnp.zeros(shape) - - def update(self, I): - dt = brainstate.environ.get_dt() - - # Izhikevich dynamics - dV = (0.04 * self.V.value**2 + 5 * self.V.value + 140 - self.u.value + I.to_decimal(u.nA)) - du = self.a * (self.b * self.V.value - self.u.value) - - self.V.value += dV * dt.to_decimal(u.ms) - self.u.value += du * dt.to_decimal(u.ms) - - # Spike and reset - self.spike.value = (self.V.value >= 30).astype(float) - self.V.value = jnp.where(self.spike.value > 0, self.c.to_decimal(u.mV), self.V.value) - self.u.value = jnp.where(self.spike.value > 0, self.u.value + self.d.to_decimal(u.mV), self.u.value) - - return self.V.value - - def get_spike(self): - return self.spike.value - -Custom Synapses ---------------- - -Example: Biexponential Synapse -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class BiexponentialSynapse(brainpy.state.Synapse): - """Synapse with separate rise and decay.""" - - def __init__(self, size, tau_rise=1*u.ms, tau_decay=5*u.ms, **kwargs): - super().__init__(size, **kwargs) - - self.tau_rise = tau_rise - self.tau_decay = tau_decay - - # States - self.h = brainstate.ShortTermState(jnp.zeros(size)) # Rising phase - self.g = brainstate.ShortTermState(jnp.zeros(size)) # Decaying phase - - def reset_state(self, batch_size=None): - shape = self.size if batch_size is None else (batch_size, self.size) - self.h.value = jnp.zeros(shape) - self.g.value = jnp.zeros(shape) - - def update(self, x): - dt = brainstate.environ.get_dt() - - # Two-stage dynamics - dh = -self.h.value / self.tau_rise.to_decimal(u.ms) + x - dg = -self.g.value / self.tau_decay.to_decimal(u.ms) + self.h.value - - self.h.value += dh * dt.to_decimal(u.ms) - self.g.value += dg * dt.to_decimal(u.ms) - - return self.g.value - -Example: NMDA Synapse -~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class NMDASynapse(brainpy.state.Synapse): - """NMDA receptor with voltage dependence.""" - - def __init__(self, size, tau=100*u.ms, a=0.5/u.mM, Mg=1.0*u.mM, **kwargs): - super().__init__(size, **kwargs) - - self.tau = tau - self.a = a - self.Mg = Mg - - self.g = brainstate.ShortTermState(jnp.zeros(size)) - - def reset_state(self, batch_size=None): - shape = self.size if batch_size is None else (batch_size, self.size) - self.g.value = jnp.zeros(shape) - - def update(self, x, V_post=None): - """Update with optional postsynaptic voltage.""" - dt = brainstate.environ.get_dt() - - # Conductance dynamics - dg = -self.g.value / self.tau.to_decimal(u.ms) + x - self.g.value += dg * dt.to_decimal(u.ms) - - # Voltage-dependent magnesium block - if V_post is not None: - mg_block = 1 / (1 + self.Mg.to_decimal(u.mM) * self.a.to_decimal(1/u.mM) * jnp.exp(-0.062 * V_post.to_decimal(u.mV))) - return self.g.value * mg_block - else: - return self.g.value - -Custom Learning Rules ---------------------- - -Example: Simplified STDP -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class SimpleSTDP(brainstate.nn.Module): - """Simplified STDP learning rule.""" - - def __init__(self, n_pre, n_post, A_plus=0.01, A_minus=0.01, - tau_plus=20*u.ms, tau_minus=20*u.ms): - super().__init__() - - self.A_plus = A_plus - self.A_minus = A_minus - self.tau_plus = tau_plus - self.tau_minus = tau_minus - - # Learnable weights - self.W = brainstate.ParamState(jnp.ones((n_pre, n_post)) * 0.5) - - # Eligibility traces - self.pre_trace = brainstate.ShortTermState(jnp.zeros(n_pre)) - self.post_trace = brainstate.ShortTermState(jnp.zeros(n_post)) - - def reset_state(self, batch_size=None): - shape_pre = self.W.value.shape[0] if batch_size is None else (batch_size, self.W.value.shape[0]) - shape_post = self.W.value.shape[1] if batch_size is None else (batch_size, self.W.value.shape[1]) - self.pre_trace.value = jnp.zeros(shape_pre) - self.post_trace.value = jnp.zeros(shape_post) - - def update(self, pre_spike, post_spike): - dt = brainstate.environ.get_dt() - - # Update traces - self.pre_trace.value += -self.pre_trace.value / self.tau_plus.to_decimal(u.ms) * dt.to_decimal(u.ms) + pre_spike - self.post_trace.value += -self.post_trace.value / self.tau_minus.to_decimal(u.ms) * dt.to_decimal(u.ms) + post_spike - - # Weight updates - # LTP: pre spike finds existing post trace - dw_ltp = self.A_plus * jnp.outer(pre_spike, self.post_trace.value) - - # LTD: post spike finds existing pre trace - dw_ltd = -self.A_minus * jnp.outer(self.pre_trace.value, post_spike) - - # Update weights - self.W.value = jnp.clip(self.W.value + dw_ltp + dw_ltd, 0, 1) - - return jnp.dot(pre_spike, self.W.value) - -Custom Network Architectures ------------------------------ - -Example: Liquid State Machine -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class LiquidStateMachine(brainstate.nn.Module): - """Reservoir computing with spiking neurons.""" - - def __init__(self, n_input=100, n_reservoir=1000, n_output=10): - super().__init__() - - # Input projection (trainable) - self.input_weights = brainstate.ParamState( - brainstate.random.randn(n_input, n_reservoir) * 0.1 - ) - - # Reservoir (fixed random recurrent network) - self.reservoir = brainpy.state.LIF(n_reservoir, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - # Fixed random recurrent weights - w_reservoir = brainstate.random.randn(n_reservoir, n_reservoir) * 0.01 - mask = (brainstate.random.rand(n_reservoir, n_reservoir) < 0.1).astype(float) - self.reservoir_weights = w_reservoir * mask # Not a ParamState (fixed) - - # Readout (trainable) - self.readout = brainpy.state.Readout(n_reservoir, n_output) - - def update(self, x): - # Input to reservoir - reservoir_input = jnp.dot(x, self.input_weights.value) * u.nA - - # Reservoir recurrence - spk = self.reservoir.get_spike() - recurrent_input = jnp.dot(spk, self.reservoir_weights) * u.nA - - # Update reservoir - self.reservoir(reservoir_input + recurrent_input) - - # Readout from reservoir state - output = self.readout(self.reservoir.get_spike()) - - return output - -Custom Input Encoders ----------------------- - -Example: Temporal Contrast Encoder -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class TemporalContrastEncoder(brainstate.nn.Module): - """Encode images as spike timing based on contrast.""" - - def __init__(self, n_pixels, max_time=100, threshold=0.1): - super().__init__() - self.n_pixels = n_pixels - self.max_time = max_time - self.threshold = threshold - - def encode(self, image): - """Convert image to spike timing. - - Args: - image: Array of pixel values [0, 1] - - Returns: - spike_times: When each pixel spikes (or max_time if no spike) - """ - # Higher intensity → earlier spike - spike_times = jnp.where( - image > self.threshold, - self.max_time * (1 - image), # Invert: bright pixels spike early - self.max_time # Below threshold: no spike - ) - - return spike_times - - def decode_to_spikes(self, spike_times, current_time): - """Get spikes at current simulation time.""" - spikes = (spike_times == current_time).astype(float) - return spikes - -Best Practices --------------- - -✅ **Inherit from base classes** - - ``brainpy.state.Neuron`` for neurons - - ``brainpy.state.Synapse`` for synapses - - ``brainstate.nn.Module`` for general components - -✅ **Use ShortTermState for dynamics** - - Reset each trial - - Temporary variables - -✅ **Use ParamState for learnable parameters** - - Trained by optimizers - - Saved in checkpoints - -✅ **Implement reset_state()** - - Handle batch_size parameter - - Initialize all ShortTermStates - -✅ **Use physical units** - - All parameters with ``brainunit`` - - Convert for computation with ``.to_decimal()`` - -✅ **Follow naming conventions** - - ``V`` for voltage - - ``spike`` for spike indicator - - ``g`` for conductance - - ``w`` for weights - -Testing Custom Components --------------------------- - -.. code-block:: python - - def test_custom_neuron(): - """Test custom neuron implementation.""" - - neuron = CustomNeuron(size=10) - brainstate.nn.init_all_states(neuron) - - # Test 1: Initialization - assert neuron.V.value.shape == (10,) - assert jnp.all(neuron.V.value == 0) - - # Test 2: Response to input - strong_input = jnp.ones(10) * 10.0 * u.nA - for _ in range(100): - neuron(strong_input) - - spike_count = jnp.sum(neuron.spike.value) - assert spike_count > 0, "Neuron should spike with strong input" - - # Test 3: Batch dimension - brainstate.nn.init_all_states(neuron, batch_size=5) - assert neuron.V.value.shape == (5, 10) - - print("✅ Custom neuron tests passed") - - test_custom_neuron() - -Complete Example ----------------- - -**Putting it all together:** - -.. code-block:: python - - # Custom components - class MyNeuron(brainpy.state.Neuron): - # ... (see examples above) - pass - - class MySynapse(brainpy.state.Synapse): - # ... (see examples above) - pass - - # Use in network - class CustomNetwork(brainstate.nn.Module): - def __init__(self): - super().__init__() - - self.pre = MyNeuron(size=100) - self.post = MyNeuron(size=50) - - self.projection = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(100, 50, prob=0.1, weight=0.5*u.mS), - syn=MySynapse.desc(50), # Use custom synapse - out=brainpy.state.CUBA.desc(), - post=self.post - ) - - def update(self, inp): - spk_pre = self.pre.get_spike() - self.projection(spk_pre) - self.pre(inp) - self.post(0*u.nA) - return self.post.get_spike() - - # Use network - net = CustomNetwork() - brainstate.nn.init_all_states(net) - - for _ in range(100): - output = net(input_data) - -Summary -------- - -**Component creation checklist:** - -.. code-block:: python - - ✅ Inherit from brainpy.state.Neuron, brainpy.state.Synapse, or brainstate.nn.Module - ✅ Define __init__ with parameters - ✅ Create states (ShortTermState or ParamState) - ✅ Implement reset_state(batch_size=None) - ✅ Implement update() method - ✅ Use physical units throughout - ✅ Test with different batch sizes - -See Also --------- - -- :doc:`../core-concepts/state-management` - Understanding states -- :doc:`../core-concepts/neurons` - Built-in neuron models -- :doc:`../core-concepts/synapses` - Built-in synapse models -- :doc:`../tutorials/advanced/06-synaptic-plasticity` - Plasticity examples diff --git a/docs_state/how-to-guides/debugging-networks.rst b/docs_state/how-to-guides/debugging-networks.rst deleted file mode 100644 index eaa434eb..00000000 --- a/docs_state/how-to-guides/debugging-networks.rst +++ /dev/null @@ -1,811 +0,0 @@ -How to Debug Networks -===================== - -This guide shows you how to identify and fix common issues when developing neural networks with BrainPy. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Quick Diagnostic Checklist ---------------------------- - -When your network isn't working, check these first: - -**☐ Is the network receiving input?** - Print input values, check shapes - -**☐ Are neurons firing?** - Count spikes, check spike rates - -**☐ Are projections working?** - Verify connectivity, check weights - -**☐ Is update order correct?** - Get spikes BEFORE updating neurons - -**☐ Are states initialized?** - Call ``brainstate.nn.init_all_states()`` - -**☐ Are units correct?** - All values need physical units (mV, nA, ms) - -Common Issues and Solutions ----------------------------- - -Issue 1: No Spikes / Silent Network -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** - -- Network produces no spikes -- All neurons stay at rest potential - -**Diagnosis:** - -.. code-block:: python - - import brainpy - import brainstate - import brainunit as u - - neuron = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - brainstate.nn.init_all_states(neuron) - - # Check 1: Is input being provided? - inp = brainstate.random.rand(100) * 5.0 * u.nA - print("Input range:", inp.min(), "to", inp.max()) - - # Check 2: Are neurons updating? - V_before = neuron.V.value.copy() - neuron(inp) - V_after = neuron.V.value - print("Voltage changed:", not jnp.allclose(V_before, V_after)) - - # Check 3: Are any neurons near threshold? - print("Max voltage:", V_after.max()) - print("Threshold:", neuron.V_th.to_decimal(u.mV)) - print("Neurons above -55mV:", jnp.sum(V_after > -55)) - - # Check 4: Count spikes - for i in range(100): - neuron(inp) - spike_count = jnp.sum(neuron.spike.value) - print(f"Spikes in 100 steps: {spike_count}") - -**Common Causes:** - -1. **Input too weak:** - - .. code-block:: python - - # Too weak - inp = brainstate.random.rand(100) * 0.1 * u.nA # Not enough! - - # Better - inp = brainstate.random.rand(100) * 5.0 * u.nA # Stronger - -2. **Threshold too high:** - - .. code-block:: python - - # Check threshold - neuron = brainpy.state.LIF(100, V_th=-40*u.mV, ...) # Harder to spike - neuron = brainpy.state.LIF(100, V_th=-50*u.mV, ...) # Easier to spike - -3. **Time constant too large:** - - .. code-block:: python - - # Slow integration - neuron = brainpy.state.LIF(100, tau=100*u.ms, ...) # Very slow - - # Faster - neuron = brainpy.state.LIF(100, tau=10*u.ms, ...) # Normal speed - -4. **Missing initialization:** - - .. code-block:: python - - neuron = brainpy.state.LIF(100, ...) - # MUST initialize! - brainstate.nn.init_all_states(neuron) - -Issue 2: Runaway Activity / Explosion -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** - -- All neurons fire constantly -- Membrane potentials go to infinity -- NaN values appear - -**Diagnosis:** - -.. code-block:: python - - # Check for NaN - if jnp.any(jnp.isnan(neuron.V.value)): - print("❌ NaN detected in membrane potential!") - - # Check for explosion - if jnp.any(jnp.abs(neuron.V.value) > 1000): - print("❌ Membrane potential exploded!") - - # Check spike rate - spike_rate = jnp.mean(neuron.spike.value) - print(f"Spike rate: {spike_rate*100:.1f}%") - if spike_rate > 0.5: - print("⚠️ More than 50% of neurons firing every step!") - -**Common Causes:** - -1. **Excitation-Inhibition imbalance:** - - .. code-block:: python - - # Imbalanced (explosion!) - w_exc = 5.0 * u.mS # Too strong - w_inh = 1.0 * u.mS # Too weak - - # Balanced - w_exc = 0.5 * u.mS - w_inh = 5.0 * u.mS # Inhibition ~10× stronger - -2. **Positive feedback loop:** - - .. code-block:: python - - # Check recurrent excitation - # E → E with no inhibition can explode - - # Add inhibition - class BalancedNetwork(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.E = brainpy.state.LIF(800, ...) - self.I = brainpy.state.LIF(200, ...) - - self.E2E = ... # Excitatory recurrence - self.I2E = ... # MUST have inhibition! - -3. **Time step too large:** - - .. code-block:: python - - # Unstable - brainstate.environ.set(dt=1.0 * u.ms) # Too large - - # Stable - brainstate.environ.set(dt=0.1 * u.ms) # Standard - -4. **Wrong reversal potentials:** - - .. code-block:: python - - # WRONG: Inhibition with excitatory reversal - out_inh = brainpy.state.COBA.desc(E=0*u.mV) # Should be negative! - - # CORRECT - out_exc = brainpy.state.COBA.desc(E=0*u.mV) # Excitation - out_inh = brainpy.state.COBA.desc(E=-80*u.mV) # Inhibition - -Issue 3: Spikes Not Propagating -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** - -- Presynaptic neurons spike -- Postsynaptic neurons don't respond -- Projection seems inactive - -**Diagnosis:** - -.. code-block:: python - - # Create simple network - pre = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - post = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(10, 10, prob=0.5, weight=2.0*u.mS), - syn=brainpy.state.Expon.desc(10, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=post - ) - - brainstate.nn.init_all_states([pre, post, proj]) - - # Diagnosis - for i in range(10): - # CRITICAL: Get spikes BEFORE update - pre_spikes = pre.get_spike() - - # Strong input to pre - pre(brainstate.random.rand(10) * 10.0 * u.nA) - - # Check: Did pre spike? - if jnp.sum(pre_spikes) > 0: - print(f"Step {i}: {jnp.sum(pre_spikes)} presynaptic spikes") - - # Update projection - proj(pre_spikes) - - # Check: Did projection produce current? - print(f" Synaptic conductance: {proj.syn.g.value.max():.4f}") - - # Update post - post(0*u.nA) # Only synaptic input - - # Check: Did post spike? - post_spikes = post.get_spike() - print(f" {jnp.sum(post_spikes)} postsynaptic spikes") - -**Common Causes:** - -1. **Wrong spike timing:** - - .. code-block:: python - - # WRONG: Spikes from current step - pre(inp) # Update first - spikes = pre.get_spike() # These are NEW spikes - proj(spikes) # But projection needs OLD spikes! - - # CORRECT: Spikes from previous step - spikes = pre.get_spike() # Get OLD spikes first - proj(spikes) # Update projection - pre(inp) # Then update neurons - -2. **Weak connectivity:** - - .. code-block:: python - - # Too sparse - comm = brainstate.nn.EventFixedProb(..., prob=0.01, weight=0.1*u.mS) - - # Stronger - comm = brainstate.nn.EventFixedProb(..., prob=0.1, weight=1.0*u.mS) - -3. **Missing projection update:** - - .. code-block:: python - - # Forgot to call projection! - spk = pre.get_spike() - # proj(spk) <- MISSING! - post(0*u.nA) - -4. **Wrong postsynaptic target:** - - .. code-block:: python - - # Wrong target - proj = brainpy.state.AlignPostProj(..., post=wrong_population) - - # Correct target - proj = brainpy.state.AlignPostProj(..., post=correct_population) - -Issue 4: Shape Mismatch Errors -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptoms:** - -.. code-block:: text - - ValueError: operands could not be broadcast together - with shapes (100,) (64, 100) - -**Common Causes:** - -1. **Batch dimension mismatch:** - - .. code-block:: python - - # Network initialized with batch - brainstate.nn.init_all_states(net, batch_size=64) - # States shape: (64, 100) - - # But input has no batch - inp = jnp.zeros(100) # Shape: (100,) - WRONG! - - # Fix: Add batch dimension - inp = jnp.zeros((64, 100)) # Shape: (64, 100) - CORRECT - -2. **Forgot batch in initialization:** - - .. code-block:: python - - # Initialized without batch - brainstate.nn.init_all_states(net) # Shape: (100,) - - # But providing batched input - inp = jnp.zeros((64, 100)) # Shape: (64, 100) - - # Fix: Initialize with batch - brainstate.nn.init_all_states(net, batch_size=64) - -**Debug shape mismatches:** - -.. code-block:: python - - print(f"Input shape: {inp.shape}") - print(f"Network state shape: {net.neurons.V.value.shape}") - print(f"Expected: Both should have same batch dimension") - -Inspection Tools ----------------- - -Print State Values -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Inspect neuron states - neuron = brainpy.state.LIF(10, ...) - brainstate.nn.init_all_states(neuron) - - print("Membrane potentials:", neuron.V.value) - print("Spikes:", neuron.spike.value) - print("Shape:", neuron.V.value.shape) - - # Statistics - print(f"V range: [{neuron.V.value.min():.2f}, {neuron.V.value.max():.2f}]") - print(f"V mean: {neuron.V.value.mean():.2f}") - print(f"Spike count: {jnp.sum(neuron.spike.value)}") - -Visualize Activity -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import matplotlib.pyplot as plt - import numpy as np - - # Record activity - n_steps = 1000 - V_history = [] - spike_history = [] - - for i in range(n_steps): - neuron(inp) - V_history.append(neuron.V.value.copy()) - spike_history.append(neuron.spike.value.copy()) - - V_history = jnp.array(V_history) - spike_history = jnp.array(spike_history) - - # Plot membrane potential - plt.figure(figsize=(12, 4)) - plt.plot(V_history[:, 0]) # First neuron - plt.xlabel('Time step') - plt.ylabel('Membrane Potential (mV)') - plt.title('Neuron 0 Membrane Potential') - plt.show() - - # Plot raster - plt.figure(figsize=(12, 6)) - times, neurons = jnp.where(spike_history > 0) - plt.scatter(times, neurons, s=1, c='black') - plt.xlabel('Time step') - plt.ylabel('Neuron index') - plt.title('Spike Raster') - plt.show() - - # Firing rate over time - plt.figure(figsize=(12, 4)) - firing_rate = jnp.mean(spike_history, axis=1) * 1000 / 0.1 # Hz - plt.plot(firing_rate) - plt.xlabel('Time step') - plt.ylabel('Population Rate (Hz)') - plt.title('Population Firing Rate') - plt.show() - -Check Connectivity -~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # For sparse projections - proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(100, 50, prob=0.1, weight=0.5*u.mS), - syn=brainpy.state.Expon.desc(50, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=post_neurons - ) - - # Check connection count - print(f"Expected connections: {100 * 50 * 0.1:.0f}") - # Note: Actual connectivity may vary due to randomness - - # Check weights - # (Accessing internal connectivity structure depends on implementation) - -Monitor Training -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Track loss and metrics - train_losses = [] - val_accuracies = [] - - for epoch in range(num_epochs): - epoch_losses = [] - - for batch in train_loader: - loss = train_step(net, batch) - epoch_losses.append(float(loss)) - - avg_loss = np.mean(epoch_losses) - train_losses.append(avg_loss) - - # Validation - val_acc = evaluate(net, val_loader) - val_accuracies.append(val_acc) - - print(f"Epoch {epoch}: Loss={avg_loss:.4f}, Val Acc={val_acc:.2%}") - - # Check for issues - if np.isnan(avg_loss): - print("❌ NaN loss! Stopping training.") - break - - if avg_loss > 10 * train_losses[0]: - print("⚠️ Loss exploding!") - - # Plot training curves - plt.figure(figsize=(12, 4)) - plt.subplot(1, 2, 1) - plt.plot(train_losses) - plt.xlabel('Epoch') - plt.ylabel('Loss') - plt.title('Training Loss') - - plt.subplot(1, 2, 2) - plt.plot(val_accuracies) - plt.xlabel('Epoch') - plt.ylabel('Accuracy') - plt.title('Validation Accuracy') - plt.show() - -Advanced Debugging ------------------- - -Gradient Checking -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import braintools - - # Check if gradients are being computed - params = net.states(brainstate.ParamState) - - grads = brainstate.transform.grad( - loss_fn, - params, - return_value=True - )(net, X, y) - - # Inspect gradients - for name, grad in grads.items(): - grad_norm = jnp.linalg.norm(grad.value.flatten()) - print(f"{name}: gradient norm = {grad_norm:.6f}") - - if jnp.any(jnp.isnan(grad.value)): - print(f" ❌ NaN in gradient!") - - if grad_norm == 0: - print(f" ⚠️ Zero gradient - parameter not learning") - - if grad_norm > 1000: - print(f" ⚠️ Exploding gradient!") - -Trace Execution -~~~~~~~~~~~~~~~ - -.. code-block:: python - - def debug_step(net, inp): - """Instrumented simulation step.""" - print(f"\n--- Step Start ---") - - # Before - print(f"Input range: [{inp.min():.2f}, {inp.max():.2f}]") - print(f"V before: [{net.neurons.V.value.min():.2f}, {net.neurons.V.value.max():.2f}]") - - # Execute - output = net(inp) - - # After - print(f"V after: [{net.neurons.V.value.min():.2f}, {net.neurons.V.value.max():.2f}]") - print(f"Spikes: {jnp.sum(net.neurons.spike.value)}") - print(f"Output range: [{output.min():.2f}, {output.max():.2f}]") - - # Checks - if jnp.any(jnp.isnan(net.neurons.V.value)): - print("❌ NaN detected!") - import pdb; pdb.set_trace() # Drop into debugger - - print(f"--- Step End ---\n") - return output - - # Use for debugging - for i in range(10): - output = debug_step(net, input_data) - -Assertion Checks -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class SafeNetwork(brainstate.nn.Module): - """Network with built-in checks.""" - - def __init__(self, n_neurons=100): - super().__init__() - self.neurons = brainpy.state.LIF(n_neurons, ...) - - def update(self, inp): - # Pre-checks - assert inp.shape[-1] == 100, f"Wrong input size: {inp.shape}" - assert not jnp.any(jnp.isnan(inp)), "NaN in input!" - assert not jnp.any(jnp.isinf(inp)), "Inf in input!" - - # Execute - self.neurons(inp) - output = self.neurons.get_spike() - - # Post-checks - assert not jnp.any(jnp.isnan(self.neurons.V.value)), "NaN in membrane potential!" - assert jnp.all(jnp.abs(self.neurons.V.value) < 1000), "Voltage explosion!" - - return output - -Unit Testing -~~~~~~~~~~~~ - -.. code-block:: python - - def test_neuron_spikes(): - """Test that neuron spikes with strong input.""" - neuron = brainpy.state.LIF(1, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - brainstate.nn.init_all_states(neuron) - - # Strong constant input should cause spiking - strong_input = jnp.array([20.0]) * u.nA - - spike_count = 0 - for _ in range(100): - neuron(strong_input) - spike_count += int(neuron.spike.value[0]) - - assert spike_count > 0, "Neuron didn't spike with strong input!" - assert spike_count < 100, "Neuron spiked every step (check reset!)" - - print(f"✅ Neuron test passed ({spike_count} spikes)") - - def test_projection(): - """Test that projection propagates spikes.""" - pre = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - post = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(10, 10, prob=1.0, weight=5.0*u.mS), # 100% connectivity - syn=brainpy.state.Expon.desc(10, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=post - ) - - brainstate.nn.init_all_states([pre, post, proj]) - - # Make pre spike - pre(jnp.ones(10) * 20.0 * u.nA) - - # Projection should activate - spk = pre.get_spike() - assert jnp.sum(spk) > 0, "Pre didn't spike!" - - proj(spk) - - # Check synaptic conductance increased - assert proj.syn.g.value.max() > 0, "Synapse didn't activate!" - - print("✅ Projection test passed") - - # Run tests - test_neuron_spikes() - test_projection() - -Debugging Checklist -------------------- - -When your network doesn't work: - -**1. Check Initialization** - -.. code-block:: python - - ☐ Called brainstate.nn.init_all_states()? - ☐ Correct batch_size parameter? - ☐ All submodules initialized? - -**2. Check Input** - -.. code-block:: python - - ☐ Input shape matches network? - ☐ Input has units (nA, mV, etc.)? - ☐ Input magnitude reasonable? - ☐ Input not all zeros? - -**3. Check Neurons** - -.. code-block:: python - - ☐ Threshold reasonable (e.g., -50 mV)? - ☐ Reset potential below threshold? - ☐ Time constant reasonable (5-20 ms)? - ☐ Neurons actually spiking? - -**4. Check Projections** - -.. code-block:: python - - ☐ Connectivity probability > 0? - ☐ Weights reasonable magnitude? - ☐ Correct update order (spikes before update)? - ☐ Projection actually called? - -**5. Check Balance** - -.. code-block:: python - - ☐ Inhibition stronger than excitation (~10×)? - ☐ Reversal potentials correct (E=0, I=-80)? - ☐ E/I ratio appropriate (4:1)? - -**6. Check Training** - -.. code-block:: python - - ☐ Loss decreasing? - ☐ Gradients non-zero? - ☐ No NaN in gradients? - ☐ Learning rate appropriate? - -Common Error Messages ---------------------- - -"operands could not be broadcast" -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Meaning:** Shape mismatch - -**Fix:** Check batch dimensions - -.. code-block:: python - - print(f"Shapes: {x.shape} vs {y.shape}") - -"RESOURCE_EXHAUSTED: Out of memory" -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Meaning:** GPU/CPU memory full - -**Fix:** Reduce batch size or network size - -.. code-block:: python - - # Reduce batch - brainstate.nn.init_all_states(net, batch_size=16) # Instead of 64 - -"Concrete value required" -~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Meaning:** JIT can't handle dynamic values - -**Fix:** Use static shapes - -.. code-block:: python - - # Dynamic (bad for JIT) - n = len(data) # Changes each call - - # Static (good for JIT) - n = 100 # Fixed value - -"Invalid device" -~~~~~~~~~~~~~~~~ - -**Meaning:** Trying to use unavailable device - -**Fix:** Check available devices - -.. code-block:: python - - import jax - print(jax.devices()) - -Best Practices --------------- - -✅ **Test small first** - Debug with 10 neurons before scaling to 10,000 - -✅ **Visualize early** - Plot activity to see problems immediately - -✅ **Check incrementally** - Test each component before combining - -✅ **Use assertions** - Catch problems early with runtime checks - -✅ **Print liberally** - Add diagnostic prints during development - -✅ **Keep backups** - Save working versions before major changes - -✅ **Start simple** - Begin with minimal network, add complexity gradually - -✅ **Write tests** - Unit test individual components - -❌ **Don't debug by guessing** - Use systematic diagnosis - -❌ **Don't skip initialization** - Always call init_all_states - -❌ **Don't ignore warnings** - They often indicate real problems - -Summary -------- - -**Debugging workflow:** - -1. **Identify symptom** (no spikes, explosion, etc.) -2. **Isolate component** (neurons, projections, input) -3. **Inspect state** (print values, plot activity) -4. **Form hypothesis** (what might be wrong?) -5. **Test fix** (make one change at a time) -6. **Verify** (ensure problem solved) - -**Quick diagnostic code:** - -.. code-block:: python - - # Comprehensive diagnostic - def diagnose_network(net, inp): - print("=== Network Diagnostic ===") - - # Input - print(f"Input shape: {inp.shape}") - print(f"Input range: [{inp.min():.2f}, {inp.max():.2f}]") - - # States - if hasattr(net, 'neurons'): - V = net.neurons.V.value - print(f"Voltage shape: {V.shape}") - print(f"Voltage range: [{V.min():.2f}, {V.max():.2f}]") - - # Simulation - output = net(inp) - - # Results - if hasattr(net, 'neurons'): - spk_count = jnp.sum(net.neurons.spike.value) - print(f"Spikes: {spk_count}") - - print(f"Output shape: {output.shape}") - print(f"Output range: [{output.min():.2f}, {output.max():.2f}]") - - # Checks - if jnp.any(jnp.isnan(output)): - print("❌ NaN in output!") - if jnp.all(output == 0): - print("⚠️ Output all zeros!") - - print("=========================") - return output - -See Also --------- - -- :doc:`../core-concepts/state-management` - Understanding state system -- :doc:`../core-concepts/projections` - Projection architecture -- :doc:`performance-optimization` - Optimization tips diff --git a/docs_state/how-to-guides/gpu-tpu-usage.rst b/docs_state/how-to-guides/gpu-tpu-usage.rst deleted file mode 100644 index ad44e03d..00000000 --- a/docs_state/how-to-guides/gpu-tpu-usage.rst +++ /dev/null @@ -1,826 +0,0 @@ -How to Use GPU and TPU -====================== - -This guide shows you how to leverage GPU and TPU acceleration for faster simulations and training with BrainPy. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Quick Start ------------ - -**Check available devices:** - -.. code-block:: python - - import jax - print("Available devices:", jax.devices()) - print("Default backend:", jax.default_backend()) - -**BrainPy automatically uses available accelerators** - no code changes needed! - -.. code-block:: python - - import brainpy - import brainstate - - # This automatically runs on GPU if available - net = brainpy.state.LIF(10000, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - brainstate.nn.init_all_states(net) - - for _ in range(1000): - net(brainstate.random.rand(10000) * 2.0 * u.nA) - -Installation ------------- - -CPU-Only (Default) -~~~~~~~~~~~~~~~~~~ - -.. code-block:: bash - - pip install brainpy[cpu] - -GPU (CUDA 12) -~~~~~~~~~~~~~ - -.. code-block:: bash - - # CUDA 12 - pip install brainpy[cuda12] - - # Or CUDA 11 - pip install brainpy[cuda11] - -**Requirements:** - -- NVIDIA GPU (compute capability ≥ 3.5) -- CUDA Toolkit installed -- cuDNN libraries - -TPU (Google Cloud) -~~~~~~~~~~~~~~~~~~ - -.. code-block:: bash - - pip install brainpy[tpu] - -**Requirements:** - -- Google Cloud TPU instance -- TPU runtime configured - -Verify Installation -~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import jax - import jax.numpy as jnp - - # Check JAX can see GPU/TPU - print("Devices:", jax.devices()) - - # Test computation - x = jnp.ones((1000, 1000)) - y = jnp.dot(x, x) - print("✅ JAX computation works!") - - # Check device placement - print("Result device:", y.device()) - -Expected output (GPU): - -.. code-block:: text - - Devices: [cuda(id=0)] - ✅ JAX computation works! - Result device: cuda:0 - -Understanding Device Placement -------------------------------- - -Automatic Placement -~~~~~~~~~~~~~~~~~~~ - -**JAX automatically places computations on the best available device:** - -1. TPU (if available) -2. GPU (if available) -3. CPU (fallback) - -.. code-block:: python - - import brainpy as bp - import brainstate - - # Automatically uses GPU if available - net = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net) - - # All operations run on GPU - net(input_data) - -Manual Device Selection -~~~~~~~~~~~~~~~~~~~~~~~ - -Force computation on specific device: - -.. code-block:: python - - import jax - - # Run on specific GPU - with jax.default_device(jax.devices('gpu')[0]): - net = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net) - result = net(input_data) - - # Run on CPU - with jax.default_device(jax.devices('cpu')[0]): - net_cpu = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net_cpu) - result_cpu = net_cpu(input_data) - -Check Data Location -~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Check where data lives - neuron = brainpy.state.LIF(100, ...) - brainstate.nn.init_all_states(neuron) - - print("Voltage device:", neuron.V.value.device()) - # Output: cuda:0 (if on GPU) - -Optimizing for GPU -------------------- - -Use JIT Compilation -~~~~~~~~~~~~~~~~~~~ - -**Essential for GPU performance!** - -.. code-block:: python - - import brainstate - - net = brainpy.state.LIF(10000, ...) - brainstate.nn.init_all_states(net) - - # WITHOUT JIT (slow on GPU) - for _ in range(1000): - net(input_data) # Many small kernel launches - - # WITH JIT (fast on GPU) - @brainstate.transform.jit - def simulate_step(net, inp): - return net(inp) - - # Warmup (compilation) - _ = simulate_step(net, input_data) - - # Fast execution - for _ in range(1000): - output = simulate_step(net, input_data) - -**Speedup:** 10-100× with JIT on GPU - -Batch Operations -~~~~~~~~~~~~~~~~ - -**Process multiple trials in parallel:** - -.. code-block:: python - - # Single trial (underutilizes GPU) - net = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net) # Shape: (1000,) - - # Multiple trials in parallel (efficient GPU usage) - net_batched = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net_batched, batch_size=64) # Shape: (64, 1000) - - # GPU processes all 64 trials simultaneously - inp = brainstate.random.rand(64, 1000) * 2.0 * u.nA - output = net_batched(inp) - -**GPU Utilization:** - -- Small batches (1-10): ~10-30% GPU usage -- Medium batches (32-128): ~60-80% GPU usage -- Large batches (256+): ~90-100% GPU usage - -Appropriate Problem Size -~~~~~~~~~~~~~~~~~~~~~~~~ - -**GPU overhead is worth it for large problems:** - -.. list-table:: When to Use GPU - :header-rows: 1 - - * - Network Size - - GPU Speedup - - Recommendation - * - < 1,000 neurons - - 0.5-2× - - Use CPU - * - 1,000-10,000 - - 2-10× - - GPU beneficial - * - 10,000-100,000 - - 10-50× - - GPU strongly recommended - * - > 100,000 - - 50-100× - - GPU essential - -Minimize Data Transfer -~~~~~~~~~~~~~~~~~~~~~~ - -**Avoid moving data between CPU and GPU:** - -.. code-block:: python - - # BAD: Frequent CPU-GPU transfers - for i in range(1000): - inp_cpu = np.random.rand(1000) # On CPU - inp_gpu = jnp.array(inp_cpu) # Transfer to GPU - output_gpu = net(inp_gpu) # Compute on GPU - output_cpu = np.array(output_gpu) # Transfer to CPU - # CPU-GPU transfer dominates time! - - # GOOD: Keep data on GPU - @brainstate.transform.jit - def simulate_step(net, key): - inp = brainstate.random.uniform(key, (1000,)) * 2.0 # Generated on GPU - return net(inp) # Stays on GPU - - key = brainstate.random.split_key() - for i in range(1000): - output = simulate_step(net, key) # All on GPU - -Use Sparse Operations -~~~~~~~~~~~~~~~~~~~~~ - -**Sparse connectivity is crucial for large networks:** - -.. code-block:: python - - # Dense (memory intensive on GPU) - dense_proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.Linear(10000, 10000), # 400MB just for weights! - syn=brainpy.state.Expon.desc(10000, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=post_neurons - ) - - # Sparse (memory efficient) - sparse_proj = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb( - pre_size=10000, - post_size=10000, - prob=0.01, # 1% connectivity - weight=0.5*u.mS - ), # Only 4MB for weights! - syn=brainpy.state.Expon.desc(10000, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=post_neurons - ) - -Multi-GPU Usage ---------------- - -Data Parallelism -~~~~~~~~~~~~~~~~ - -**Run different trials on different GPUs:** - -.. code-block:: python - - import jax - - # Check available GPUs - gpus = jax.devices('gpu') - print(f"Found {len(gpus)} GPUs") - - # Split work across GPUs - def run_on_gpu(gpu_id, n_trials): - with jax.default_device(gpus[gpu_id]): - net = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net, batch_size=n_trials) - - results = [] - for _ in range(100): - output = net(input_data) - results.append(output) - - return results - - # Run on multiple GPUs in parallel - from concurrent.futures import ThreadPoolExecutor - - with ThreadPoolExecutor(max_workers=len(gpus)) as executor: - futures = [ - executor.submit(run_on_gpu, i, 32) - for i in range(len(gpus)) - ] - all_results = [f.result() for f in futures] - -Using JAX pmap -~~~~~~~~~~~~~~ - -**Parallel map across devices:** - -.. code-block:: python - - from jax import pmap - import jax.numpy as jnp - - # Create model - net = brainpy.state.LIF(1000, ...) - - @pmap - def parallel_simulate(inputs): - """Run on multiple devices in parallel.""" - brainstate.nn.init_all_states(net) - return net(inputs) - - # Split inputs across devices - n_devices = len(jax.devices()) - inputs = jnp.ones((n_devices, 1000)) # One batch per device - - # Run in parallel - outputs = parallel_simulate(inputs) - # outputs.shape = (n_devices, output_size) - -TPU-Specific Optimization --------------------------- - -TPU Characteristics -~~~~~~~~~~~~~~~~~~~ - -**TPUs are optimized for:** - -✅ Large matrix multiplications (e.g., dense layers) - -✅ High batch sizes (128+) - -✅ Float32 operations (bf16 also good) - -❌ Small operations (overhead dominates) - -❌ Sparse operations (less optimized than GPU) - -❌ Dynamic shapes (requires recompilation) - -Optimal TPU Usage -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Configure for TPU - import brainstate - - # Large batches for TPU - batch_size = 256 # TPUs like large batches - - net = brainpy.state.LIF(1000, ...) - brainstate.nn.init_all_states(net, batch_size=batch_size) - - # JIT is essential - @brainstate.transform.jit - def train_step(net, inputs, labels): - # Dense operations work well - # Avoid sparse operations on TPU - return loss - - # Static shapes (avoid dynamic) - inputs = jnp.ones((batch_size, 1000)) # Fixed shape - - # Run - for batch in data_loader: - loss = train_step(net, batch_inputs, batch_labels) - -TPU Pods -~~~~~~~~ - -**Multi-TPU training:** - -.. code-block:: python - - # TPU pods provide multiple TPU cores - devices = jax.devices('tpu') - print(f"TPU cores: {len(devices)}") - - # Use pmap for data parallelism - @pmap - def parallel_step(inputs): - return net(inputs) - - # Split across TPU cores - inputs_per_core = jnp.reshape(inputs, (len(devices), -1, 1000)) - outputs = parallel_step(inputs_per_core) - -Performance Benchmarking ------------------------- - -Measure Speedup -~~~~~~~~~~~~~~~ - -.. code-block:: python - - import time - import jax - - def benchmark_device(device_type, n_neurons=10000, n_steps=1000): - """Benchmark simulation on specific device.""" - - # Select device - if device_type == 'cpu': - device = jax.devices('cpu')[0] - elif device_type == 'gpu': - device = jax.devices('gpu')[0] - else: - device = jax.devices('tpu')[0] - - with jax.default_device(device): - # Create network - net = brainpy.state.LIF(n_neurons, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - brainstate.nn.init_all_states(net) - - @brainstate.transform.jit - def step(net, inp): - return net(inp) - - # Warmup - inp = brainstate.random.rand(n_neurons) * 2.0 * u.nA - _ = step(net, inp) - - # Benchmark - start = time.time() - for _ in range(n_steps): - inp = brainstate.random.rand(n_neurons) * 2.0 * u.nA - output = step(net, inp) - elapsed = time.time() - start - - return elapsed - - # Compare devices - cpu_time = benchmark_device('cpu', n_neurons=10000, n_steps=1000) - gpu_time = benchmark_device('gpu', n_neurons=10000, n_steps=1000) - - print(f"CPU time: {cpu_time:.2f}s") - print(f"GPU time: {gpu_time:.2f}s") - print(f"Speedup: {cpu_time/gpu_time:.1f}×") - -Profile GPU Usage -~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - # Monitor GPU memory - import jax - - # Get memory info (NVIDIA GPUs) - try: - from jax.lib import xla_bridge - print("GPU memory allocated:", xla_bridge.get_backend().platform_memory_stats()) - except: - print("Memory stats not available") - - # Profile with TensorBoard (advanced) - with jax.profiler.trace("/tmp/tensorboard"): - for _ in range(100): - output = net(input_data) - - # View with: tensorboard --logdir=/tmp/tensorboard - -Memory Management ------------------ - -Check GPU Memory -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import jax - - # Check total memory - for device in jax.devices('gpu'): - try: - # This may not work on all systems - print(f"Device: {device}") - print(f"Memory: {device.memory_stats()}") - except: - print("Memory stats not available") - -Estimate Memory Requirements -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - def estimate_memory_mb(n_neurons, n_synapses, batch_size=1, dtype_bytes=4): - """Estimate GPU memory needed. - - Args: - n_neurons: Number of neurons - n_synapses: Number of synapses - batch_size: Batch size - dtype_bytes: 4 for float32, 2 for float16 - """ - # Neuron states (V, spike, etc.) × batch - neuron_memory = n_neurons * 3 * batch_size * dtype_bytes - - # Synapse states (g, x, etc.) - synapse_memory = n_synapses * 2 * dtype_bytes - - # Weights - weight_memory = n_synapses * dtype_bytes - - total_bytes = neuron_memory + synapse_memory + weight_memory - total_mb = total_bytes / (1024 * 1024) - - return total_mb - - # Example - mem_mb = estimate_memory_mb( - n_neurons=100000, - n_synapses=100000 * 100000 * 0.01, # 1% connectivity - batch_size=32 - ) - print(f"Estimated memory: {mem_mb:.1f} MB ({mem_mb/1024:.2f} GB)") - -Clear GPU Memory -~~~~~~~~~~~~~~~~ - -.. code-block:: python - - import jax - - # JAX manages memory automatically - # But you can force garbage collection - - import gc - - # Delete large arrays - del large_array - del network - - # Force garbage collection - gc.collect() - - # Clear JAX compilation cache (if needed) - jax.clear_caches() - -Common Issues and Solutions ----------------------------- - -Issue: Out of Memory -~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** `RESOURCE_EXHAUSTED: Out of memory` - -**Solutions:** - -1. **Reduce batch size:** - - .. code-block:: python - - # Try smaller batch - brainstate.nn.init_all_states(net, batch_size=16) # Instead of 64 - -2. **Use sparse connectivity:** - - .. code-block:: python - - # Reduce connectivity - comm = brainstate.nn.EventFixedProb(..., prob=0.01) # Instead of 0.1 - -3. **Use float16:** - - .. code-block:: python - - # Lower precision (experimental) - jax.config.update('jax_default_dtype_bits', '32') # Default - # Note: BrainPy primarily uses float32 - -4. **Process in chunks:** - - .. code-block:: python - - # Split large population - for i in range(0, n_neurons, chunk_size): - chunk_output = process_chunk(neurons[i:i+chunk_size]) - -Issue: Slow First Run -~~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** First iteration very slow - -**Explanation:** JIT compilation happens on first call - -**Solution:** Warm up before timing - -.. code-block:: python - - @brainstate.transform.jit - def step(net, inp): - return net(inp) - - # Warmup (compile) - _ = step(net, dummy_input) - - # Now fast - for real_input in data: - output = step(net, real_input) - -Issue: GPU Not Being Used -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** Computation on CPU despite GPU available - -**Check:** - -.. code-block:: python - - import jax - print("Devices:", jax.devices()) - print("Default backend:", jax.default_backend()) - - # Should show GPU - -**Solutions:** - -1. Check installation: `pip list | grep jax` -2. Reinstall with GPU support: `pip install brainpy[cuda12]` -3. Check CUDA installation: `nvidia-smi` - -Issue: Version Mismatch -~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** `RuntimeError: CUDA error` - -**Check versions:** - -.. code-block:: bash - - # Check CUDA version - nvcc --version - - # Check JAX version - python -c "import jax; print(jax.__version__)" - -**Solution:** Match JAX CUDA version with system CUDA - -.. code-block:: bash - - # For CUDA 12.x - pip install brainpy[cuda12] - - # For CUDA 11.x - pip install brainpy[cuda11] - -Best Practices --------------- - -✅ **Use JIT compilation** - Essential for GPU performance - -✅ **Batch operations** - Process multiple trials in parallel - -✅ **Keep data on device** - Avoid CPU-GPU transfers - -✅ **Use sparse connectivity** - For biological-scale networks - -✅ **Profile before optimizing** - Identify real bottlenecks - -✅ **Warm up JIT** - Compile before timing - -✅ **Monitor memory** - Estimate before running large models - -✅ **Static shapes** - Avoid dynamic shapes (causes recompilation) - -❌ **Don't use GPU for small problems** - Overhead dominates - -❌ **Don't transfer data unnecessarily** - Keep on GPU - -❌ **Don't use dense connectivity for large networks** - Memory explosion - -Example: Complete GPU Workflow -------------------------------- - -.. code-block:: python - - import brainpy as bp - import brainstate - import brainunit as u - import braintools - import jax - import time - - # 1. Check GPU availability - print("Devices:", jax.devices()) - assert jax.default_backend() == 'gpu', "GPU not available!" - - # 2. Create large network - class LargeNetwork(brainstate.nn.Module): - def __init__(self, n_exc=8000, n_inh=2000): - super().__init__() - - self.E = brainpy.state.LIF(n_exc, V_rest=-65*u.mV, V_th=-50*u.mV, tau=15*u.ms) - self.I = brainpy.state.LIF(n_inh, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - # Sparse connectivity (GPU efficient) - self.E2E = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob=0.02, weight=0.5*u.mS), - syn=brainpy.state.Expon.desc(n_exc, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=self.E - ) - # ... more projections - - def update(self, inp_e, inp_i): - spk_e = self.E.get_spike() - spk_i = self.I.get_spike() - - self.E2E(spk_e) - # ... update all projections - - self.E(inp_e) - self.I(inp_i) - - return spk_e, spk_i - - # 3. Initialize with large batch - net = LargeNetwork() - batch_size = 64 # Process 64 trials in parallel - brainstate.nn.init_all_states(net, batch_size=batch_size) - - # 4. JIT compile - @brainstate.transform.jit - def simulate_step(net, inp_e, inp_i): - return net(inp_e, inp_i) - - # 5. Warmup (compilation) - print("Compiling...") - inp_e = brainstate.random.rand(batch_size, 8000) * 1.0 * u.nA - inp_i = brainstate.random.rand(batch_size, 2000) * 1.0 * u.nA - _ = simulate_step(net, inp_e, inp_i) - print("✅ Compilation complete") - - # 6. Run simulation - print("Running simulation...") - n_steps = 1000 - - start = time.time() - for _ in range(n_steps): - inp_e = brainstate.random.rand(batch_size, 8000) * 1.0 * u.nA - inp_i = brainstate.random.rand(batch_size, 2000) * 1.0 * u.nA - spk_e, spk_i = simulate_step(net, inp_e, inp_i) - - elapsed = time.time() - start - - print(f"✅ Simulation complete") - print(f" Time: {elapsed:.2f}s") - print(f" Throughput: {n_steps/elapsed:.1f} steps/s") - print(f" Speed: {batch_size * n_steps / elapsed:.1f} trials/s") - -Summary -------- - -**Key Points:** - -- BrainPy automatically uses GPU/TPU when available -- JIT compilation is essential for GPU performance -- Batch operations maximize GPU utilization -- Keep data on device to avoid transfer overhead -- Use sparse connectivity for large networks -- GPU beneficial for networks > 1,000 neurons - -**Quick Reference:** - -.. code-block:: python - - # Check device - import jax - print(jax.devices()) - - # JIT for GPU - @brainstate.transform.jit - def step(net, inp): - return net(inp) - - # Batch for GPU - brainstate.nn.init_all_states(net, batch_size=64) - - # Sparse for memory - comm = brainstate.nn.EventFixedProb(..., prob=0.02) - -See Also --------- - -- :doc:`../tutorials/advanced/07-large-scale-simulations` - Optimization techniques -- :doc:`performance-optimization` - General performance tips -- JAX documentation: https://jax.readthedocs.io/ diff --git a/docs_state/how-to-guides/performance-optimization.rst b/docs_state/how-to-guides/performance-optimization.rst deleted file mode 100644 index 5691926b..00000000 --- a/docs_state/how-to-guides/performance-optimization.rst +++ /dev/null @@ -1,340 +0,0 @@ -How to Optimize Performance -============================ - -This guide shows you how to make your BrainPy simulations run faster. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Quick Wins ----------- - -**Top 5 optimizations (80% of speedup):** - -1. ✅ **Use JIT compilation** - 10-100× speedup -2. ✅ **Use sparse connectivity** - 10-100× memory reduction -3. ✅ **Batch operations** - 2-10× speedup on GPU -4. ✅ **Use GPU/TPU** - 10-100× speedup for large networks -5. ✅ **Minimize Python loops** - Use JAX operations instead - -JIT Compilation ---------------- - -**Essential for performance!** - -.. code-block:: python - - import brainstate - - # Slow (no JIT) - def slow_step(net, inp): - return net(inp) - - # Fast (with JIT) - @brainstate.transform.jit - def fast_step(net, inp): - return net(inp) - - # Warmup (compilation) - _ = fast_step(net, inp) - - # 10-100× faster than slow_step - output = fast_step(net, inp) - -**Rules for JIT:** -- Static shapes (no dynamic array sizes) -- Pure functions (no side effects) -- Avoid Python loops over data - -Sparse Connectivity -------------------- - -**Biological networks are sparse (~1-10% connectivity)** - -.. code-block:: python - - # Dense: 10,000 × 10,000 = 100M connections (400MB) - comm_dense = brainstate.nn.Linear(10000, 10000) - - # Sparse: 10,000 × 10,000 × 0.01 = 1M connections (4MB) - comm_sparse = brainstate.nn.EventFixedProb( - 10000, 10000, - prob=0.01, # 1% connectivity - weight=0.5*u.mS - ) - -**Memory savings:** 100× for 1% connectivity - -Batching --------- - -**Process multiple trials in parallel:** - -.. code-block:: python - - # Sequential: 10 trials one by one - for trial in range(10): - brainstate.nn.init_all_states(net) - run_trial(net) - - # Parallel: 10 trials simultaneously - brainstate.nn.init_all_states(net, batch_size=10) - run_batched(net) # 5-10× faster on GPU - -**Optimal batch sizes:** -- CPU: 1-16 -- GPU: 32-256 -- TPU: 128-512 - -GPU Usage ---------- - -**Automatic when available:** - -.. code-block:: python - - import jax - print(jax.devices()) # Check for GPU - - # BrainPy automatically uses GPU - net = brainpy.state.LIF(10000, ...) - # Runs on GPU if available - -**See:** :doc:`gpu-tpu-usage` for details - -Avoid Python Loops ------------------- - -**Replace Python loops with JAX operations:** - -.. code-block:: python - - # SLOW: Python loop - result = [] - for i in range(1000): - result.append(net(inp)) - - # FAST: JAX loop - def body_fun(i): - return net(inp) - - results = brainstate.transform.for_loop(body_fun, jnp.arange(1000)) - -Use Appropriate Precision --------------------------- - -**Float32 is usually sufficient:** - -.. code-block:: python - - # Default (float32) - fast - weights = jnp.ones((1000, 1000)) # 4 bytes/element - - # Float64 - 2× slower, 2× memory - weights = jnp.ones((1000, 1000), dtype=jnp.float64) # 8 bytes/element - -Minimize State Storage ----------------------- - -**Don't accumulate history:** - -.. code-block:: python - - # BAD: Stores all history in Python list - history = [] - for t in range(10000): - output = net(inp) - history.append(output) # Memory leak! - - # GOOD: Process on the fly - for t in range(10000): - output = net(inp) - metrics = compute_metrics(output) # Don't store raw data - -Optimize Network Architecture ------------------------------- - -**1. Use simpler neuron models when possible:** - -.. code-block:: python - - # Complex (slow but realistic) - neuron = brainpy.state.HH(1000, ...) # Hodgkin-Huxley - - # Simple (fast) - neuron = brainpy.state.LIF(1000, ...) # Leaky Integrate-and-Fire - -**2. Use CUBA instead of COBA when possible:** - -.. code-block:: python - - # Slower (conductance-based) - out = brainpy.state.COBA.desc(E=0*u.mV) - - # Faster (current-based) - out = brainpy.state.CUBA.desc() - -**3. Reduce connectivity:** - -.. code-block:: python - - # Dense - prob = 0.1 # 10% connectivity - - # Sparse - prob = 0.02 # 2% connectivity (5× fewer connections) - -Profile Before Optimizing --------------------------- - -**Identify actual bottlenecks:** - -.. code-block:: python - - import time - - # Time different components - start = time.time() - for _ in range(100): - net(inp) - print(f"Network update: {time.time() - start:.2f}s") - - start = time.time() - for _ in range(100): - output = process_output(net.get_spike()) - print(f"Output processing: {time.time() - start:.2f}s") - -**Don't optimize blindly - measure first!** - -Performance Checklist ---------------------- - -**For maximum performance:** - -.. code-block:: python - - ✅ JIT compiled (@brainstate.transform.jit) - ✅ Sparse connectivity (EventFixedProb with prob < 0.1) - ✅ Batched (batch_size ≥ 32 on GPU) - ✅ GPU enabled (check jax.devices()) - ✅ Static shapes (no dynamic array sizes) - ✅ Minimal history storage - ✅ Appropriate neuron models (LIF vs HH) - ✅ Float32 precision - -Common Bottlenecks ------------------- - -**Issue 1: First run very slow** - → JIT compilation happens on first call (warmup) - -**Issue 2: CPU-GPU transfers** - → Keep data on GPU between operations - -**Issue 3: Small batch sizes** - → Increase batch_size for better GPU utilization - -**Issue 4: Python loops** - → Replace with JAX operations (for_loop, vmap) - -**Issue 5: Dense connectivity** - → Use sparse (EventFixedProb) for large networks - -Complete Optimization Example ------------------------------- - -.. code-block:: python - - import brainpy as bp - import brainstate - import brainunit as u - import jax - - # Optimized network - class OptimizedNetwork(brainstate.nn.Module): - def __init__(self, n_neurons=10000): - super().__init__() - - # Simple neuron model - self.neurons = brainpy.state.LIF(n_neurons, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - - # Sparse connectivity - self.recurrent = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb( - n_neurons, n_neurons, - prob=0.01, # Sparse! - weight=0.5*u.mS - ), - syn=brainpy.state.Expon.desc(n_neurons, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), # Simple output - post=self.neurons - ) - - def update(self, inp): - spk = self.neurons.get_spike() - self.recurrent(spk) - self.neurons(inp) - return spk - - # Initialize - net = OptimizedNetwork() - brainstate.nn.init_all_states(net, batch_size=64) # Batched - - # JIT compile - @brainstate.transform.jit - def simulate_step(net, inp): - return net(inp) - - # Warmup - inp = brainstate.random.rand(64, 10000) * 2.0 * u.nA - _ = simulate_step(net, inp) - - # Fast simulation - import time - start = time.time() - for _ in range(1000): - output = simulate_step(net, inp) - elapsed = time.time() - start - - print(f"Optimized: {1000/elapsed:.1f} steps/s") - print(f"Throughput: {64*1000/elapsed:.1f} trials/s") - -Benchmark Results ------------------ - -**Typical speedups from optimization:** - -.. list-table:: - :header-rows: 1 - - * - Optimization - - Speedup - - Cumulative - * - Baseline (Python loops, dense) - - 1× - - 1× - * - + JIT compilation - - 10-50× - - 10-50× - * - + Sparse connectivity - - 2-10× - - 20-500× - * - + GPU - - 5-20× - - 100-10,000× - * - + Batching - - 2-5× - - 200-50,000× - -**Real example:** 10,000 neuron network -- Baseline (CPU, no JIT): 0.5 steps/s -- Optimized (GPU, JIT, sparse, batched): 5,000 steps/s -- **Total speedup: 10,000×** - -See Also --------- - -- :doc:`../tutorials/advanced/07-large-scale-simulations` -- :doc:`gpu-tpu-usage` -- :doc:`debugging-networks` diff --git a/docs_state/how-to-guides/save-load-models.rst b/docs_state/how-to-guides/save-load-models.rst deleted file mode 100644 index 6790e632..00000000 --- a/docs_state/how-to-guides/save-load-models.rst +++ /dev/null @@ -1,890 +0,0 @@ -How to Save and Load Models -============================ - -This guide shows you how to save and load BrainPy models for checkpointing, resuming training, and deployment. - -.. contents:: Table of Contents - :local: - :depth: 2 - -Quick Start ------------ - -**Save a trained model:** - -.. code-block:: python - - import brainpy as bp - import brainstate - import pickle - - # After training... - state_dict = { - 'params': net.states(brainstate.ParamState), - 'epoch': current_epoch, - } - - with open('model.pkl', 'wb') as f: - pickle.dump(state_dict, f) - -**Load a model:** - -.. code-block:: python - - # Create model with same architecture - net = MyNetwork() - brainstate.nn.init_all_states(net) - - # Load saved state - with open('model.pkl', 'rb') as f: - state_dict = pickle.load(f) - - # Restore parameters - for name, state in state_dict['params'].items(): - net.states(brainstate.ParamState)[name].value = state.value - -Understanding What to Save ---------------------------- - -State Types -~~~~~~~~~~~ - -BrainPy has three state types with different persistence requirements: - -**ParamState (Always save)** - - Learnable weights and biases - - Required to restore trained model - - Examples: synaptic weights, neural biases - -**LongTermState (Usually save)** - - Persistent statistics and counters - - Not updated by gradients - - Examples: running averages, spike counts - -**ShortTermState (Never save)** - - Temporary dynamics that reset each trial - - Will be re-initialized anyway - - Examples: membrane potentials, synaptic conductances - -Recommended Approach -~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - def save_checkpoint(net, optimizer, epoch, filepath): - """Save model checkpoint.""" - state_dict = { - # Required: model parameters - 'params': net.states(brainstate.ParamState), - - # Optional but recommended: long-term states - 'long_term': net.states(brainstate.LongTermState), - - # Training metadata - 'epoch': epoch, - 'optimizer_state': optimizer.state_dict(), # If continuing training - - # Model configuration (helpful for loading) - 'config': { - 'n_input': net.n_input, - 'n_hidden': net.n_hidden, - 'n_output': net.n_output, - # ... other hyperparameters - } - } - - with open(filepath, 'wb') as f: - pickle.dump(state_dict, f) - - print(f"✅ Saved checkpoint to {filepath}") - -Basic Save/Load ---------------- - -Using Pickle (Simple) -~~~~~~~~~~~~~~~~~~~~~ - -**Advantages:** -- Simple and straightforward -- Works with any Python object -- Good for quick prototyping - -**Disadvantages:** -- Python-specific format -- Version compatibility issues -- Not human-readable - -.. code-block:: python - - import pickle - import brainpy as bp - import brainstate - - # Define your model - class SimpleNet(brainstate.nn.Module): - def __init__(self, n_neurons=100): - super().__init__() - self.lif = brainpy.state.LIF(n_neurons, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms) - self.fc = brainstate.nn.Linear(n_neurons, 10) - - def update(self, x): - self.lif(x) - return self.fc(self.lif.get_spike()) - - # Train model - net = SimpleNet() - brainstate.nn.init_all_states(net) - # ... training code ... - - # Save - params = net.states(brainstate.ParamState) - with open('simple_net.pkl', 'wb') as f: - pickle.dump(params, f) - - # Load - net_new = SimpleNet() - brainstate.nn.init_all_states(net_new) - - with open('simple_net.pkl', 'rb') as f: - loaded_params = pickle.load(f) - - # Restore parameters - for name, state in loaded_params.items(): - net_new.states(brainstate.ParamState)[name].value = state.value - -Using NumPy (Arrays Only) -~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Advantages:** -- Language-agnostic -- Efficient storage -- Widely supported - -**Disadvantages:** -- Only saves arrays (not structure) -- Need to manually track parameter names - -.. code-block:: python - - import numpy as np - - # Save parameters as .npz - params = net.states(brainstate.ParamState) - param_dict = {name: np.array(state.value) for name, state in params.items()} - np.savez('model_params.npz', **param_dict) - - # Load parameters - loaded = np.load('model_params.npz') - for name, array in loaded.items(): - net.states(brainstate.ParamState)[name].value = jnp.array(array) - -Checkpointing During Training ------------------------------- - -Periodic Checkpoints -~~~~~~~~~~~~~~~~~~~~ - -Save at regular intervals during training. - -.. code-block:: python - - import braintools - - # Training setup - net = MyNetwork() - optimizer = braintools.optim.Adam(lr=1e-3) - optimizer.register_trainable_weights(net.states(brainstate.ParamState)) - - save_interval = 5 # Save every 5 epochs - checkpoint_dir = './checkpoints' - import os - os.makedirs(checkpoint_dir, exist_ok=True) - - # Training loop - for epoch in range(num_epochs): - # Training step - for batch in train_loader: - loss = train_step(net, optimizer, batch) - - # Periodic save - if (epoch + 1) % save_interval == 0: - checkpoint_path = f'{checkpoint_dir}/epoch_{epoch+1}.pkl' - save_checkpoint(net, optimizer, epoch, checkpoint_path) - - print(f"Epoch {epoch+1}: Loss={loss:.4f}, Checkpoint saved") - -Best Model Checkpoint -~~~~~~~~~~~~~~~~~~~~~ - -Save only when validation performance improves. - -.. code-block:: python - - best_val_loss = float('inf') - best_model_path = 'best_model.pkl' - - for epoch in range(num_epochs): - # Training - train_loss = train_epoch(net, optimizer, train_loader) - - # Validation - val_loss = validate(net, val_loader) - - # Save if best - if val_loss < best_val_loss: - best_val_loss = val_loss - save_checkpoint(net, optimizer, epoch, best_model_path) - print(f"✅ New best model! Val loss: {val_loss:.4f}") - - print(f"Epoch {epoch+1}: Train={train_loss:.4f}, Val={val_loss:.4f}") - -Resuming Training -~~~~~~~~~~~~~~~~~ - -Continue training from a checkpoint. - -.. code-block:: python - - def load_checkpoint(filepath, net, optimizer=None): - """Load checkpoint and restore state.""" - with open(filepath, 'rb') as f: - state_dict = pickle.load(f) - - # Restore model parameters - params = net.states(brainstate.ParamState) - for name, state in state_dict['params'].items(): - if name in params: - params[name].value = state.value - - # Restore long-term states - if 'long_term' in state_dict: - long_term = net.states(brainstate.LongTermState) - for name, state in state_dict['long_term'].items(): - if name in long_term: - long_term[name].value = state.value - - # Restore optimizer state - if optimizer is not None and 'optimizer_state' in state_dict: - optimizer.load_state_dict(state_dict['optimizer_state']) - - start_epoch = state_dict.get('epoch', 0) + 1 - return start_epoch - - # Resume training - net = MyNetwork() - brainstate.nn.init_all_states(net) - optimizer = braintools.optim.Adam(lr=1e-3) - optimizer.register_trainable_weights(net.states(brainstate.ParamState)) - - # Load checkpoint - start_epoch = load_checkpoint('checkpoint_epoch_50.pkl', net, optimizer) - - # Continue training from where we left off - for epoch in range(start_epoch, num_epochs): - train_step(net, optimizer, train_loader) - -Advanced Saving Strategies ---------------------------- - -Versioned Checkpoints -~~~~~~~~~~~~~~~~~~~~~ - -Keep multiple checkpoints without overwriting. - -.. code-block:: python - - from datetime import datetime - - def save_versioned_checkpoint(net, epoch, base_dir='checkpoints'): - """Save checkpoint with timestamp.""" - timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') - filename = f'model_epoch{epoch}_{timestamp}.pkl' - filepath = os.path.join(base_dir, filename) - - state_dict = { - 'params': net.states(brainstate.ParamState), - 'epoch': epoch, - 'timestamp': timestamp, - } - - with open(filepath, 'wb') as f: - pickle.dump(state_dict, f) - - return filepath - -Keep Last N Checkpoints -~~~~~~~~~~~~~~~~~~~~~~~~ - -Automatically delete old checkpoints to save disk space. - -.. code-block:: python - - import glob - - def save_with_cleanup(net, epoch, checkpoint_dir='checkpoints', keep_last=5): - """Save checkpoint and keep only last N.""" - - # Save new checkpoint - filepath = f'{checkpoint_dir}/epoch_{epoch:04d}.pkl' - save_checkpoint(net, None, epoch, filepath) - - # Get all checkpoints - checkpoints = sorted(glob.glob(f'{checkpoint_dir}/epoch_*.pkl')) - - # Delete old ones - if len(checkpoints) > keep_last: - for old_checkpoint in checkpoints[:-keep_last]: - os.remove(old_checkpoint) - print(f"Removed old checkpoint: {old_checkpoint}") - -Conditional Saving -~~~~~~~~~~~~~~~~~~ - -Save based on custom criteria. - -.. code-block:: python - - class CheckpointManager: - """Manage model checkpoints with custom logic.""" - - def __init__(self, checkpoint_dir, keep_best=True, keep_last=3): - self.checkpoint_dir = checkpoint_dir - self.keep_best = keep_best - self.keep_last = keep_last - self.best_metric = float('inf') - os.makedirs(checkpoint_dir, exist_ok=True) - - def save(self, net, epoch, metric, is_better=None): - """Save checkpoint based on metric. - - Args: - net: Network to save - epoch: Current epoch - metric: Validation metric - is_better: Function to compare metrics (default: lower is better) - """ - if is_better is None: - is_better = lambda new, old: new < old - - # Save if best - if self.keep_best and is_better(metric, self.best_metric): - self.best_metric = metric - filepath = f'{self.checkpoint_dir}/best_model.pkl' - save_checkpoint(net, None, epoch, filepath) - print(f"💾 Saved best model (metric: {metric:.4f})") - - # Save periodic - filepath = f'{self.checkpoint_dir}/epoch_{epoch:04d}.pkl' - save_checkpoint(net, None, epoch, filepath) - - # Cleanup old checkpoints - self._cleanup() - - def _cleanup(self): - """Keep only last N checkpoints.""" - checkpoints = sorted(glob.glob(f'{self.checkpoint_dir}/epoch_*.pkl')) - if len(checkpoints) > self.keep_last: - for old in checkpoints[:-self.keep_last]: - os.remove(old) - - # Usage - manager = CheckpointManager('./checkpoints', keep_best=True, keep_last=3) - - for epoch in range(num_epochs): - train_loss = train_epoch(net, optimizer, train_loader) - val_loss = validate(net, val_loader) - - manager.save(net, epoch, metric=val_loss) - -Model Export for Deployment ----------------------------- - -Minimal Model File -~~~~~~~~~~~~~~~~~~ - -Save only what's needed for inference. - -.. code-block:: python - - def export_for_inference(net, filepath, metadata=None): - """Export minimal model for inference.""" - - export_dict = { - 'params': net.states(brainstate.ParamState), - 'config': { - # Only architecture info, no training state - 'model_type': net.__class__.__name__, - # ... architecture hyperparameters - } - } - - if metadata: - export_dict['metadata'] = metadata - - with open(filepath, 'wb') as f: - pickle.dump(export_dict, f) - - # Report size - size_mb = os.path.getsize(filepath) / (1024 * 1024) - print(f"📦 Exported model: {size_mb:.2f} MB") - - # Export trained model - export_for_inference( - net, - 'deployed_model.pkl', - metadata={ - 'description': 'LIF network for digit classification', - 'accuracy': 0.95, - 'date': datetime.now().isoformat() - } - ) - -Loading for Inference -~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - def load_for_inference(filepath, model_class): - """Load model for inference only.""" - - with open(filepath, 'rb') as f: - export_dict = pickle.load(f) - - # Create model from config - config = export_dict['config'] - net = model_class(**config) # Must match saved config - brainstate.nn.init_all_states(net) - - # Load parameters - params = net.states(brainstate.ParamState) - for name, state in export_dict['params'].items(): - params[name].value = state.value - - return net, export_dict.get('metadata') - - # Load and use - net, metadata = load_for_inference('deployed_model.pkl', MyNetwork) - print(f"Loaded model: {metadata['description']}") - - # Run inference - brainstate.nn.init_all_states(net) - output = net(input_data) - -Saving Model Architecture --------------------------- - -Configuration-Based -~~~~~~~~~~~~~~~~~~~ - -Save hyperparameters to recreate model. - -.. code-block:: python - - class ConfigurableNetwork(brainstate.nn.Module): - """Network that can be created from config.""" - - def __init__(self, config): - super().__init__() - self.config = config - - # Build from config - self.input_layer = brainstate.nn.Linear( - config['n_input'], - config['n_hidden'] - ) - self.hidden = brainpy.state.LIF( - config['n_hidden'], - V_rest=config['V_rest'], - V_th=config['V_th'], - tau=config['tau'] - ) - # ... more layers - - @classmethod - def from_config(cls, config): - """Create model from config dict.""" - return cls(config) - - def get_config(self): - """Get configuration dict.""" - return self.config.copy() - - # Save with config - config = { - 'n_input': 784, - 'n_hidden': 128, - 'n_output': 10, - 'V_rest': -65.0, - 'V_th': -50.0, - 'tau': 10.0 - } - - net = ConfigurableNetwork(config) - # ... train ... - - # Save both params and config - checkpoint = { - 'config': net.get_config(), - 'params': net.states(brainstate.ParamState) - } - - with open('model_with_config.pkl', 'wb') as f: - pickle.dump(checkpoint, f) - - # Load from config - with open('model_with_config.pkl', 'rb') as f: - checkpoint = pickle.load(f) - - net_new = ConfigurableNetwork.from_config(checkpoint['config']) - brainstate.nn.init_all_states(net_new) - - for name, state in checkpoint['params'].items(): - net_new.states(brainstate.ParamState)[name].value = state.value - -Handling Model Updates ----------------------- - -Version Compatibility -~~~~~~~~~~~~~~~~~~~~~ - -Handle changes in model architecture. - -.. code-block:: python - - VERSION = '2.0' - - def save_with_version(net, filepath): - """Save model with version info.""" - checkpoint = { - 'version': VERSION, - 'params': net.states(brainstate.ParamState), - 'config': net.get_config() - } - - with open(filepath, 'wb') as f: - pickle.dump(checkpoint, f) - - def load_with_migration(filepath, model_class): - """Load model with version migration.""" - with open(filepath, 'rb') as f: - checkpoint = pickle.load(f) - - version = checkpoint.get('version', '1.0') - - # Migrate old versions - if version == '1.0': - print("Migrating from v1.0 to v2.0...") - checkpoint = migrate_v1_to_v2(checkpoint) - - # Create model - net = model_class.from_config(checkpoint['config']) - brainstate.nn.init_all_states(net) - - # Load parameters - for name, state in checkpoint['params'].items(): - if name in net.states(brainstate.ParamState): - net.states(brainstate.ParamState)[name].value = state.value - else: - print(f"⚠️ Skipping unknown parameter: {name}") - - return net - - def migrate_v1_to_v2(checkpoint): - """Migrate checkpoint from v1.0 to v2.0.""" - # Example: rename parameter - if 'old_param_name' in checkpoint['params']: - checkpoint['params']['new_param_name'] = checkpoint['params'].pop('old_param_name') - - checkpoint['version'] = '2.0' - return checkpoint - -Partial Loading -~~~~~~~~~~~~~~~ - -Load only some parameters (e.g., for transfer learning). - -.. code-block:: python - - def load_partial(filepath, net, param_filter=None): - """Load only specified parameters. - - Args: - filepath: Checkpoint file - net: Network to load into - param_filter: Function that takes param name and returns True to load - """ - with open(filepath, 'rb') as f: - checkpoint = pickle.load(f) - - if param_filter is None: - param_filter = lambda name: True - - loaded_count = 0 - skipped_count = 0 - - for name, state in checkpoint['params'].items(): - if param_filter(name): - if name in net.states(brainstate.ParamState): - net.states(brainstate.ParamState)[name].value = state.value - loaded_count += 1 - else: - print(f"⚠️ Parameter not found in model: {name}") - skipped_count += 1 - else: - skipped_count += 1 - - print(f"✅ Loaded {loaded_count} parameters, skipped {skipped_count}") - - # Example: Load only encoder parameters - load_partial( - 'pretrained.pkl', - net, - param_filter=lambda name: name.startswith('encoder.') - ) - -Common Patterns ---------------- - -Pattern 1: Training Session Manager -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class TrainingSession: - """Manage full training session with checkpointing.""" - - def __init__(self, net, optimizer, checkpoint_dir='./checkpoints'): - self.net = net - self.optimizer = optimizer - self.checkpoint_dir = checkpoint_dir - self.epoch = 0 - self.best_metric = float('inf') - - os.makedirs(checkpoint_dir, exist_ok=True) - - def save(self, metric=None): - """Save current state.""" - checkpoint = { - 'params': self.net.states(brainstate.ParamState), - 'optimizer': self.optimizer.state_dict(), - 'epoch': self.epoch, - 'best_metric': self.best_metric - } - - # Regular checkpoint - filepath = f'{self.checkpoint_dir}/checkpoint_latest.pkl' - with open(filepath, 'wb') as f: - pickle.dump(checkpoint, f) - - # Best checkpoint - if metric is not None and metric < self.best_metric: - self.best_metric = metric - best_path = f'{self.checkpoint_dir}/checkpoint_best.pkl' - with open(best_path, 'wb') as f: - pickle.dump(checkpoint, f) - - def restore(self, filepath=None): - """Restore from checkpoint.""" - if filepath is None: - filepath = f'{self.checkpoint_dir}/checkpoint_latest.pkl' - - with open(filepath, 'rb') as f: - checkpoint = pickle.load(f) - - # Restore state - for name, state in checkpoint['params'].items(): - self.net.states(brainstate.ParamState)[name].value = state.value - - self.optimizer.load_state_dict(checkpoint['optimizer']) - self.epoch = checkpoint['epoch'] - self.best_metric = checkpoint['best_metric'] - - print(f"✅ Restored from epoch {self.epoch}") - -Pattern 2: Model Zoo -~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: python - - class ModelZoo: - """Collection of pre-trained models.""" - - def __init__(self, zoo_dir='./model_zoo'): - self.zoo_dir = zoo_dir - os.makedirs(zoo_dir, exist_ok=True) - - def save_model(self, net, name, metadata=None): - """Add model to zoo.""" - model_path = f'{self.zoo_dir}/{name}.pkl' - export_dict = { - 'params': net.states(brainstate.ParamState), - 'config': net.get_config(), - 'metadata': metadata or {} - } - - with open(model_path, 'wb') as f: - pickle.dump(export_dict, f) - - print(f"📦 Added {name} to model zoo") - - def load_model(self, name, model_class): - """Load model from zoo.""" - model_path = f'{self.zoo_dir}/{name}.pkl' - - with open(model_path, 'rb') as f: - export_dict = pickle.load(f) - - net = model_class.from_config(export_dict['config']) - brainstate.nn.init_all_states(net) - - for param_name, state in export_dict['params'].items(): - net.states(brainstate.ParamState)[param_name].value = state.value - - return net, export_dict['metadata'] - - def list_models(self): - """List available models.""" - models = glob.glob(f'{self.zoo_dir}/*.pkl') - return [os.path.basename(m).replace('.pkl', '') for m in models] - - # Usage - zoo = ModelZoo() - - # Save trained models - zoo.save_model(net1, 'mnist_classifier', {'accuracy': 0.98}) - zoo.save_model(net2, 'fashion_classifier', {'accuracy': 0.92}) - - # List and load - print("Available models:", zoo.list_models()) - net, metadata = zoo.load_model('mnist_classifier', MyNetwork) - print(f"Loaded model with accuracy: {metadata['accuracy']}") - -Troubleshooting ---------------- - -Issue: Pickle version mismatch -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** `AttributeError` or `ModuleNotFoundError` when loading - -**Solution:** Use protocol version 4 or lower for compatibility - -.. code-block:: python - - # Save with specific protocol - with open('model.pkl', 'wb') as f: - pickle.dump(state_dict, f, protocol=4) - -Issue: JAX array serialization -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** Can't pickle JAX arrays directly - -**Solution:** Convert to NumPy before saving - -.. code-block:: python - - import numpy as np - - # Convert to NumPy for saving - params_np = { - name: np.array(state.value) - for name, state in net.states(brainstate.ParamState).items() - } - - with open('model.pkl', 'wb') as f: - pickle.dump(params_np, f) - - # Convert back when loading - for name, array in params_np.items(): - net.states(brainstate.ParamState)[name].value = jnp.array(array) - -Issue: Model architecture changed -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -**Symptom:** Parameter names don't match - -**Solution:** Use partial loading with error handling - -.. code-block:: python - - def safe_load(checkpoint_path, net): - """Load with error handling.""" - with open(checkpoint_path, 'rb') as f: - checkpoint = pickle.load(f) - - current_params = net.states(brainstate.ParamState) - loaded_params = checkpoint['params'] - - # Check compatibility - missing = set(current_params.keys()) - set(loaded_params.keys()) - unexpected = set(loaded_params.keys()) - set(current_params.keys()) - - if missing: - print(f"⚠️ Missing parameters: {missing}") - if unexpected: - print(f"⚠️ Unexpected parameters: {unexpected}") - - # Load matching parameters - for name in current_params.keys() & loaded_params.keys(): - current_params[name].value = loaded_params[name].value - - print(f"✅ Loaded {len(current_params.keys() & loaded_params.keys())} parameters") - -Best Practices --------------- - -✅ **Always save configuration** - Include hyperparameters for reproducibility - -✅ **Version your checkpoints** - Track model version for compatibility - -✅ **Save metadata** - Include training metrics, date, description - -✅ **Regular backups** - Save periodically during long training - -✅ **Keep best model** - Separate best and latest checkpoints - -✅ **Test loading** - Verify checkpoint can be loaded before continuing - -✅ **Use relative paths** - Make checkpoints portable - -✅ **Document format** - Comment what's in your checkpoint files - -❌ **Don't save ShortTermState** - It resets anyway - -❌ **Don't save everything** - Minimize checkpoint size - -❌ **Don't overwrite** - Keep multiple checkpoints for safety - -Summary -------- - -**Quick reference:** - -.. code-block:: python - - # Save - checkpoint = { - 'params': net.states(brainstate.ParamState), - 'epoch': epoch, - 'config': net.get_config() - } - with open('checkpoint.pkl', 'wb') as f: - pickle.dump(checkpoint, f) - - # Load - with open('checkpoint.pkl', 'rb') as f: - checkpoint = pickle.load(f) - - net = MyNetwork.from_config(checkpoint['config']) - brainstate.nn.init_all_states(net) - - for name, state in checkpoint['params'].items(): - net.states(brainstate.ParamState)[name].value = state.value - -See Also --------- - -- :doc:`../core-concepts/state-management` - Understanding states -- :doc:`../tutorials/advanced/05-snn-training` - Training models -- :doc:`gpu-tpu-usage` - Accelerated training diff --git a/docs_state/index.rst b/docs_state/index.rst index 9c91bd1c..e84482a3 100644 --- a/docs_state/index.rst +++ b/docs_state/index.rst @@ -1,7 +1,7 @@ ``brainpy.state`` documentation ===================================== -`brainpy.state` provides a new ``State``-based programming paradigm for building and simulating spiking neural networks. +``brainpy.state`` provides a new ``state``-based programming paradigm for building and simulating spiking neural networks. Compared to ``brainpy.dyn``, ``brainpy.state`` provides: @@ -14,7 +14,7 @@ Compared to ``brainpy.dyn``, ``brainpy.state`` provides: .. note:: ``brainpy.state`` is written based on `brainstate `_. - This documentation is for the latest version 3.x. + This documentation is for the latest ``brainpy.state``. @@ -70,7 +70,7 @@ Learn more .. card:: :material-regular:`library_books;2em` Core Concepts :class-card: sd-text-black sd-bg-light - :link: quickstart/concepts-overview.html + :link: quickstart/overview.html .. grid-item:: :columns: 6 6 6 4 @@ -131,9 +131,7 @@ See also the ecosystem :caption: Tutorials quickstart/index.rst - core-concepts/index.rst tutorials/index.rst - how-to-guides/index.rst examples/gallery.rst diff --git a/docs_state/quickstart/5min-tutorial.ipynb b/docs_state/quickstart/5min-tutorial.ipynb index 1a77a653..44506ce0 100644 --- a/docs_state/quickstart/5min-tutorial.ipynb +++ b/docs_state/quickstart/5min-tutorial.ipynb @@ -1,12 +1,12 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "# 5-Minute Tutorial: Getting Started\n", "\n", - "Welcome to ``brainpy.state.state``! This quick tutorial will get you up and running with your first neural simulation in just a few minutes.\n", + "Welcome to ``brainpy.state``! This quick tutorial will get you up and running with your first neural simulation in just a few minutes.\n", "\n", "## What You'll Learn\n", "\n", @@ -17,8 +17,8 @@ ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Step 1: Import Libraries\n", "\n", @@ -26,13 +26,15 @@ ] }, { + "cell_type": "code", + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2025-10-10T07:17:12.262478Z", "start_time": "2025-10-10T07:17:12.257706Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import jax\n", "\n", @@ -41,13 +43,11 @@ "import brainunit as u\n", "import braintools\n", "import matplotlib.pyplot as plt\n" - ], - "outputs": [], - "execution_count": 3 + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Step 2: Create Your First Neuron\n", "\n", @@ -55,13 +55,23 @@ ] }, { + "cell_type": "code", + "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2025-10-10T07:17:12.278071Z", "start_time": "2025-10-10T07:17:12.273488Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created a LIF neuron!\n" + ] + } + ], "source": [ "# Set simulation time step\n", "brainstate.environ.set(dt=0.1 * u.ms)\n", @@ -76,21 +86,11 @@ ")\n", "\n", "print(\"Created a LIF neuron!\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created a LIF neuron!\n" - ] - } - ], - "execution_count": 4 + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Step 3: Simulate the Neuron\n", "\n", @@ -98,13 +98,23 @@ ] }, { + "cell_type": "code", + "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2025-10-10T07:18:40.403662Z", "start_time": "2025-10-10T07:18:40.322794Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simulation complete! Recorded 2000 time steps.\n" + ] + } + ], "source": [ "# Initialize neuron state\n", "brainstate.nn.init_all_states(neuron)\n", @@ -115,7 +125,7 @@ "times = u.math.arange(0. * u.ms, duration, dt)\n", "\n", "# Input current (constant)\n", - "I_input = 2.0 * u.nA\n", + "I_input = 20.0 * u.mA\n", "\n", "# Run simulation and record membrane potential\n", "def step_run(t):\n", @@ -126,21 +136,11 @@ "voltages, spikes = brainstate.transform.for_loop(step_run, times)\n", "\n", "print(f\"Simulation complete! Recorded {len(times)} time steps.\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Simulation complete! Recorded 2000 time steps.\n" - ] - } - ], - "execution_count": 8 + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Step 4: Visualize the Results\n", "\n", @@ -148,13 +148,34 @@ ] }, { + "cell_type": "code", + "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2025-10-10T07:18:43.855438Z", "start_time": "2025-10-10T07:18:43.755841Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of spikes: 17\n", + "Average firing rate: 85.00 Hz\n" + ] + } + ], "source": [ "# Convert to appropriate units for plotting\n", "times_plot = times.to_decimal(u.ms)\n", @@ -175,35 +196,11 @@ "firing_rate = n_spikes / (duration.to_decimal(u.second))\n", "print(f\"Number of spikes: {n_spikes}\")\n", "print(f\"Average firing rate: {firing_rate:.2f} Hz\")" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of spikes: 3\n", - "Average firing rate: 15.00 Hz\n" - ] - } - ], - "execution_count": 9 + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Step 5: Create a Network of Neurons\n", "\n", @@ -211,13 +208,23 @@ ] }, { + "cell_type": "code", + "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2025-10-10T07:19:06.974038Z", "start_time": "2025-10-10T07:19:06.958649Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created network with 100 neurons\n" + ] + } + ], "source": [ "class SimpleEINet(brainstate.nn.Module):\n", " def __init__(self, n_exc=80, n_inh=20):\n", @@ -268,21 +275,11 @@ "# Create network\n", "net = SimpleEINet(n_exc=80, n_inh=20)\n", "print(f\"Created network with {net.num} neurons\")" - ], - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created network with 100 neurons\n" - ] - } - ], - "execution_count": 10 + ] }, { - "metadata": {}, "cell_type": "markdown", + "metadata": {}, "source": [ "## Step 6: Simulate the Network\n", "\n", @@ -290,48 +287,28 @@ ] }, { + "cell_type": "code", + "execution_count": 59, "metadata": { "ExecuteTime": { "end_time": "2025-10-10T07:19:11.531447Z", "start_time": "2025-10-10T07:19:10.189416Z" } }, - "cell_type": "code", - "source": [ - "# Initialize network states\n", - "brainstate.nn.init_all_states(net)\n", - "\n", - "# Simulation parameters\n", - "duration = 500. * u.ms\n", - "times = u.math.arange(0. * u.ms, duration, dt)\n", - "I_ext = 1.5 * u.nA # External input current\n", - "\n", - "# Run simulation\n", - "spike_history = brainstate.transform.for_loop(\n", - " lambda t: net.update(I_ext),\n", - " times,\n", - " pbar=brainstate.transform.ProgressBar(10)\n", - ")\n", - "\n", - "print(\"Network simulation complete!\")" - ], "outputs": [ { "data": { - "text/plain": [ - " 0%| | 0/5000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total spikes: 1586\n", + "Average firing rate: 31.72 Hz\n" + ] + } + ], "source": [ "import jax\n", "spike_history = jax.block_until_ready(spike_history)\n", @@ -394,31 +409,7 @@ "avg_rate = total_spikes / (net.num * duration.to_decimal(u.second))\n", "print(f\"Total spikes: {total_spikes}\")\n", "print(f\"Average firing rate: {avg_rate:.2f} Hz\")" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "
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- }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total spikes: 0\n", - "Average firing rate: 0.00 Hz\n" - ] - } - ], - "execution_count": 14 + ] }, { "cell_type": "markdown", @@ -456,7 +447,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -470,7 +461,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/quickstart/concepts-overview.rst b/docs_state/quickstart/concepts-overview.rst deleted file mode 100644 index 087b25d1..00000000 --- a/docs_state/quickstart/concepts-overview.rst +++ /dev/null @@ -1,267 +0,0 @@ -Core Concepts Overview -====================== - -``brainpy.state`` introduces a modern, state-based architecture built on top of ``brainstate``. This overview will -help you understand the key concepts and design philosophy. - -What's New ----------- - -``brainpy.state`` has been completely rewritten to provide: - -- **State-based programming**: Built on ``brainstate`` for efficient state management -- **Modular architecture**: Clear separation of concerns (communication, dynamics, outputs) -- **Physical units**: Integration with ``brainunit`` for scientifically accurate simulations -- **Modern API**: Cleaner, more intuitive interfaces -- **Better performance**: Optimized JIT compilation and memory management - -Key Architectural Components ------------------------------ - -``brainpy.state`` is organized around several core concepts: - -1. State Management -~~~~~~~~~~~~~~~~~~~ - -Everything in ``brainpy.state`` revolves around **states**. States are variables that persist across time steps: - -- ``brainstate.State``: Base state container -- ``brainstate.ParamState``: Trainable parameters -- ``brainstate.ShortTermState``: Temporary variables - -States enable: - -- Automatic differentiation for training -- Efficient memory management -- Batching and parallelization - -2. Neurons -~~~~~~~~~~ - -Neurons are the fundamental computational units: - -.. code-block:: python - - import brainpy - import brainunit as u - - # Create a population of 100 LIF neurons - neurons = brainpy.state.LIF(100, tau=10*u.ms, V_th=-50*u.mV) - -Key neuron models: - -- ``brainpy.state.IF``: Integrate-and-Fire -- ``brainpy.state.LIF``: Leaky Integrate-and-Fire -- ``brainpy.state.LIFRef``: LIF with refractory period -- ``brainpy.state.ALIF``: Adaptive LIF - -3. Synapses -~~~~~~~~~~~ - -Synapses model the dynamics of neural connections: - -.. code-block:: python - - # Exponential synapse - synapse = brainpy.state.Expon(100, tau=5*u.ms) - - # Alpha synapse (more realistic) - synapse = brainpy.state.Alpha(100, tau=5*u.ms) - -Synapse models: - -- ``brainpy.state.Expon``: Single exponential decay -- ``brainpy.state.Alpha``: Double exponential (alpha function) -- ``brainpy.state.AMPA``: Excitatory receptor dynamics -- ``brainpy.state.GABAa``: Inhibitory receptor dynamics - -4. Projections -~~~~~~~~~~~~~~ - -Projections connect neural populations: - -.. code-block:: python - - projection = brainpy.state.AlignPostProj( - comm=brainstate.nn.EventFixedProb(N_pre, N_post, prob=0.1, weight=0.5), - syn=brainpy.state.Expon.desc(N_post, tau=5*u.ms), - out=brainpy.state.CUBA.desc(), - post=neurons - ) - -The projection architecture separates: - -- **Communication**: How spikes are transmitted (connectivity, weights) -- **Synaptic dynamics**: How synapses respond (temporal filtering) -- **Output mechanism**: How synaptic currents affect neurons (CUBA/COBA) - -5. Networks -~~~~~~~~~~~ - -Networks combine neurons and projections: - -.. code-block:: python - - import brainstate - - class EINet(brainstate.nn.Module): - def __init__(self): - super().__init__() - self.E = brainpy.state.LIF(800) - self.I = brainpy.state.LIF(200) - self.E2E = brainpy.state.AlignPostProj(...) - self.E2I = brainpy.state.AlignPostProj(...) - # ... more projections - - def update(self, input): - # Define network dynamics - pass - -Computational Model -------------------- - -Time-Stepped Simulation -~~~~~~~~~~~~~~~~~~~~~~~ - -BrainPy uses discrete time steps for simulation: - -.. code-block:: python - - import brainstate - import brainunit as u - - # Set simulation time step - brainstate.environ.set(dt=0.1 * u.ms) - - # Run simulation - times = u.math.arange(0*u.ms, 1000*u.ms, brainstate.environ.get_dt()) - results = brainstate.transform.for_loop(network.update, times) - -JIT Compilation -~~~~~~~~~~~~~~~ - -BrainPy leverages JAX for Just-In-Time compilation: - -.. code-block:: python - - @brainstate.transform.jit - def simulate(): - return network.update(input) - - # First call compiles, subsequent calls are fast - result = simulate() - -Benefits: - -- Near-C performance -- Automatic GPU/TPU dispatch -- Optimized memory usage - -Physical Units -~~~~~~~~~~~~~~ - -BrainPy 3.0 integrates ``brainunit`` for scientific accuracy: - -.. code-block:: python - - import brainunit as u - - # Define parameters with units - tau = 10 * u.ms - V_threshold = -50 * u.mV - current = 5 * u.nA - - # Units are checked automatically - neurons = brainpy.state.LIF(100, tau=tau, V_th=V_threshold) - -This prevents unit-related bugs and makes code self-documenting. - -Training and Learning ---------------------- - -BrainPy 3.0 supports gradient-based training: - -.. code-block:: python - - import braintools - - # Define optimizer - optimizer = braintools.optim.Adam(lr=1e-3) - optimizer.register_trainable_weights(net.states(brainstate.ParamState)) - - # Define loss function - def loss_fn(): - predictions = brainstate.transform.for_loop(net.update, inputs) - return loss(predictions, targets) - - # Training step - @brainstate.transform.jit - def train_step(): - grads, loss = brainstate.transform.grad( - loss_fn, - net.states(brainstate.ParamState), - return_value=True - )() - optimizer.update(grads) - return loss - -Key features: - -- Surrogate gradients for spiking neurons -- Automatic differentiation -- Various optimizers (Adam, SGD, etc.) - -Ecosystem Components --------------------- - -``brainpy.state`` is part of a larger ecosystem: - -brainstate -~~~~~~~~~~ - -The foundation for state management and compilation: - -- State-based IR construction -- JIT compilation -- Program augmentation (batching, etc.) - -brainunit -~~~~~~~~~ - -Physical units system: - -- SI units support -- Automatic unit checking -- Unit conversions - -braintools -~~~~~~~~~~ - -Utilities and tools: - -- Optimizers (``braintools.optim``) -- Initialization (``braintools.init``) -- Metrics and losses (``braintools.metric``) -- Surrogate gradients (``braintools.surrogate``) -- Visualization (``braintools.visualize``) - -Design Philosophy ------------------ - -``brainpy.state`` follows these principles: - -1. **Explicit over implicit**: Clear, readable code -2. **Modular composition**: Build complex models from simple components -3. **Performance by default**: JIT compilation and optimization built-in -4. **Scientific accuracy**: Physical units and biologically realistic models -5. **Extensibility**: Easy to add custom components - -Next Steps ----------- - -Now that you understand the core concepts: - -- Try the :doc:`5-minute tutorial <5min-tutorial>` to get hands-on experience -- Read the :doc:`detailed core concepts <../core-concepts/architecture>` documentation -- Explore :doc:`basic tutorials <../tutorials/basic/01-lif-neuron>` to learn each component -- Check out the :doc:`examples gallery <../examples/gallery>` for real-world models diff --git a/docs_state/quickstart/core-concepts/architecture.ipynb b/docs_state/quickstart/core-concepts/architecture.ipynb new file mode 100644 index 00000000..7404dc22 --- /dev/null +++ b/docs_state/quickstart/core-concepts/architecture.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Architecture Overview\n", + "\n", + "``brainpy.state`` represents a complete architectural redesign built on top of the ``brainstate`` framework. This document explains the design principles and architectural components that make ``brainpy.state`` powerful and flexible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Design Philosophy\n", + "\n", + "``brainpy.state`` is built around several core principles:\n", + "\n", + "**State-Based Programming**\n", + " All dynamical variables are managed as explicit states, enabling automatic differentiation, efficient compilation, and clear data flow.\n", + "\n", + "**Modular Composition**\n", + " Complex models are built by composing simple, reusable components. Each component has a well-defined interface and responsibility.\n", + "\n", + "**Scientific Accuracy**\n", + " Integration with ``brainunit`` ensures physical correctness and prevents unit-related errors.\n", + "\n", + "**Performance by Default**\n", + " JIT compilation and optimization are built into the framework, not an afterthought.\n", + "\n", + "**Extensibility**\n", + " Adding new neuron models, synapse types, or learning rules is straightforward and follows clear patterns." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Architectural Layers\n", + "\n", + "brainpy.state is organized into several layers:\n", + "\n", + "```text\n", + "┌─────────────────────────────────────────┐\n", + "│ User Models & Networks │ ← Your code\n", + "├─────────────────────────────────────────┤\n", + "│ BrainPy Components Layer │ ← Neurons, Synapses, Projections\n", + "├─────────────────────────────────────────┤\n", + "│ BrainState Framework │ ← State management, compilation\n", + "├─────────────────────────────────────────┤\n", + "│ JAX + XLA Backend │ ← JIT compilation, autodiff\n", + "└─────────────────────────────────────────┘\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. JAX + XLA Backend\n", + "\n", + "The foundation layer provides:\n", + "\n", + "- Just-In-Time (JIT) compilation\n", + "- Automatic differentiation\n", + "- Hardware acceleration (CPU/GPU/TPU)\n", + "- Functional transformations (vmap, grad, etc.)\n", + "\n", + "### 2. BrainState Framework\n", + "\n", + "Built on JAX, ``brainstate`` provides:\n", + "\n", + "- State management system\n", + "- Module composition\n", + "- Compilation and optimization\n", + "- Program transformations (for_loop, etc.)\n", + "\n", + "### 3. BrainPy Components\n", + "\n", + "High-level neuroscience-specific components:\n", + "\n", + "- Neuron models (LIF, ALIF, etc.)\n", + "- Synapse models (Expon, Alpha, etc.)\n", + "- Projection architectures\n", + "- Learning rules and plasticity\n", + "\n", + "### 4. User Models\n", + "\n", + "Your custom networks and experiments built using BrainPy components." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## State Management System\n", + "\n", + "### The Foundation: brainstate.State\n", + "\n", + "Everything in ``brainpy.state`` revolves around **states**:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import braintools\n", + "import brainunit as u\n", + "import jax.numpy as jnp\n", + "\n", + "# Create a state\n", + "voltage = brainstate.State(0.0) # Single value\n", + "weights = brainstate.State([[0.1, 0.2], [0.3, 0.4]]) # Matrix" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "States are special containers that:\n", + "\n", + "- Track their values across time\n", + "- Support automatic differentiation\n", + "- Enable efficient compilation\n", + "- Handle batching automatically" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### State Types\n", + "\n", + "BrainPy uses different state types for different purposes:\n", + "\n", + "**ParamState** - Trainable Parameters\n", + " Used for weights, time constants, and other trainable parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "class MyNeuron(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.tau = brainstate.ParamState(10.0) # Trainable\n", + " self.weight = brainstate.ParamState([[0.1, 0.2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**ShortTermState** - Temporary Variables\n", + " Used for membrane potentials, synaptic currents, and other dynamics." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "class MyNeuron(brainstate.nn.Module):\n", + " def __init__(self, size):\n", + " super().__init__()\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size)) # Dynamic\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### State Initialization\n", + "\n", + "States can be initialized with various strategies:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# Define example size and shape\n", + "size = 100 # Number of neurons\n", + "shape = (100, 50) # Weight matrix shape\n", + "\n", + "# Constant initialization\n", + "V = brainstate.ShortTermState(\n", + " braintools.init.Constant(-65.0, unit=u.mV)(size)\n", + ")\n", + "\n", + "# Normal distribution\n", + "V = brainstate.ShortTermState(\n", + " braintools.init.Normal(-65.0, 5.0, unit=u.mV)(size)\n", + ")\n", + "\n", + "# Uniform distribution\n", + "weights = brainstate.ParamState(\n", + " braintools.init.Uniform(0.0, 1.0)(shape)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Module System\n", + "\n", + "### Base Class: brainstate.nn.Module\n", + "\n", + "All BrainPy components inherit from ``brainstate.nn.Module``:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "class MyComponent(brainstate.nn.Module):\n", + " def __init__(self, size):\n", + " super().__init__()\n", + " # Initialize states\n", + " self.state1 = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.param1 = brainstate.ParamState(jnp.ones(size))\n", + "\n", + " def update(self, input):\n", + " # Define dynamics\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Benefits of Module:\n", + "\n", + "- Automatic state registration\n", + "- Nested module support\n", + "- State collection and filtering\n", + "- Serialization support" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module Composition\n", + "\n", + "Modules can contain other modules:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "class Network(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " self.synapse = brainpy.state.Expon(100, tau=5*u.ms)\n", + " # self.projection = brainpy.state.AlignPostProj(...) # Example - requires more setup\n", + "\n", + " def update(self, input):\n", + " # Compose behavior\n", + " # self.projection(spikes) # Example\n", + " self.neurons(input)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Component Architecture\n", + "\n", + "### Neurons\n", + "\n", + "Neurons model the dynamics of neural populations:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "class Neuron(brainstate.nn.Module):\n", + " def __init__(self, size, **kwargs):\n", + " super().__init__()\n", + " # Membrane potential\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", + " # Spike output\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def update(self, input_current):\n", + " # Update membrane potential\n", + " # Generate spikes\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Key responsibilities:\n", + "\n", + "- Maintain membrane potential\n", + "- Generate spikes when threshold is crossed\n", + "- Reset after spiking\n", + "- Integrate input currents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Synapses\n", + "\n", + "Synapses model temporal filtering of spike trains:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "class Synapse(brainstate.nn.Module):\n", + " def __init__(self, size, tau, **kwargs):\n", + " super().__init__()\n", + " # Synaptic conductance/current\n", + " self.g = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.tau = tau\n", + "\n", + " def update(self, spike_input):\n", + " # Update synaptic variable\n", + " # Return filtered output\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Key responsibilities:\n", + "\n", + "- Filter spike inputs temporally\n", + "- Model synaptic dynamics (exponential, alpha, etc.)\n", + "- Provide smooth currents to postsynaptic neurons" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Projections: The Comm-Syn-Out Pattern\n", + "\n", + "Projections connect populations using a three-stage architecture:\n", + "\n", + "```text\n", + "Presynaptic Spikes → [Comm] → [Syn] → [Out] → Postsynaptic Neurons\n", + " │ │ │\n", + " Connectivity │ Current\n", + " & Weights Dynamics Injection\n", + "```\n", + "\n", + "**Communication (Comm)**\n", + " Handles spike transmission, connectivity, and weights." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "# Define population sizes\n", + "pre_size = 100\n", + "post_size = 50\n", + "\n", + "# Define prob and weight\n", + "prob=0.1\n", + "weight=0.5\n", + "\n", + "comm = brainstate.nn.EventFixedProb(\n", + " pre_size, post_size, prob, weight\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Synaptic Dynamics (Syn)**\n", + " Temporal filtering of transmitted spikes." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "post_size = 50 # Postsynaptic population size\n", + "\n", + "syn = brainpy.state.Expon.desc(post_size, tau=5*u.ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Output Mechanism (Out)**\n", + " How synaptic variables affect postsynaptic neurons." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Current-based output\n", + "out = brainpy.state.CUBA.desc() \n", + "\n", + "# Or conductance-based output\n", + "out = brainpy.state.COBA.desc(E=0*u.mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Complete Projection**" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Define postsynaptic neurons\n", + "postsynaptic_neurons = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Create complete projection\n", + "projection = brainpy.state.AlignPostProj(\n", + " comm=comm,\n", + " syn=syn,\n", + " out=out,\n", + " post=postsynaptic_neurons\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This separation provides:\n", + "\n", + "- Clear responsibility boundaries\n", + "- Easy component swapping\n", + "- Reusable building blocks\n", + "- Better testing and debugging" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compilation and Execution\n", + "\n", + "### Time-Stepped Simulation\n", + "\n", + "BrainPy uses discrete time steps:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "21a5af1809d8460f8ff309173e9c3646", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/10000 [00:00 0:\n", + " grads, loss = brainstate.transform.grad(\n", + " loss_fn,\n", + " grad_states=params,\n", + " return_value=True\n", + " )()\n", + " print(f\"Loss: {loss}\")\n", + " # Update parameters with optimizer (if defined)\n", + " # optimizer.update(grads)\n", + "else:\n", + " # If no trainable parameters, just compute loss\n", + " loss = loss_fn()\n", + " print(f\"Loss (no trainable params): {loss}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Physical Units System\n", + "\n", + "### Integration with brainunit\n", + "\n", + "``brainpy.state`` integrates ``brainunit`` for scientific accuracy:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Define with units\n", + "tau = 10 * u.ms\n", + "threshold = -50 * u.mV\n", + "current = 5 * u.nA\n", + "\n", + "# Units are checked automatically\n", + "neuron = brainpy.state.LIF(100, tau=tau, V_th=threshold)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Benefits:\n", + "\n", + "- Prevents unit errors (e.g., ms vs s)\n", + "- Self-documenting code\n", + "- Automatic unit conversions\n", + "- Scientific correctness" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unit Operations" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# Arithmetic with units\n", + "total_time = 100 * u.ms + 0.5 * u.second # → 600 ms\n", + "\n", + "# Unit conversion\n", + "time_in_seconds = (100 * u.ms).to_decimal(u.second) # → 0.1\n", + "\n", + "# Unit checking (automatic in BrainPy operations)\n", + "voltage = -65 * u.mV\n", + "current = 2 * u.nA\n", + "resistance = voltage / current # Automatically gives MΩ" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Ecosystem Integration\n", + "\n", + "``brainpy.state`` integrates tightly with its ecosystem:\n", + "\n", + "### braintools\n", + "\n", + "Utilities and tools:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "# Optimizers\n", + "optimizer = braintools.optim.Adam(lr=1e-3)\n", + "\n", + "# Initializers\n", + "init = braintools.init.KaimingNormal()\n", + "\n", + "# Surrogate gradients\n", + "spike_fn = braintools.surrogate.ReluGrad()\n", + "\n", + "# Metrics (example with dummy data)\n", + "# pred = jnp.array([0.1, 0.9])\n", + "# target = jnp.array([0, 1])\n", + "# loss = braintools.metric.cross_entropy(pred, target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### brainunit\n", + "\n", + "Physical units:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# All standard SI units\n", + "time = 10 * u.ms\n", + "voltage = -65 * u.mV\n", + "current = 2 * u.nA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### brainstate\n", + "\n", + "Core framework (used automatically):" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate\n", + "\n", + "# Module system\n", + "class Net(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " pass\n", + "\n", + "# Compilation\n", + "@brainstate.transform.jit\n", + "def fn():\n", + " return 0\n", + "\n", + "# Transformations\n", + "# result = brainstate.transform.for_loop(...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Flow Example\n", + "\n", + "Here's how data flows through a typical ``brainpy.state`` simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# 1. Define network\n", + "class EINetwork(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.E = brainpy.state.LIF(800, V_rest=-65*u.mV, V_th=-50*u.mV, tau=15*u.ms)\n", + " self.I = brainpy.state.LIF(200, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " \n", + " # Example projections (simplified - full setup requires more code)\n", + " # self.E2E = brainpy.state.AlignPostProj(...)\n", + " # self.E2I = brainpy.state.AlignPostProj(...)\n", + " # self.I2E = brainpy.state.AlignPostProj(...)\n", + " # self.I2I = brainpy.state.AlignPostProj(...)\n", + "\n", + " def update(self, input):\n", + " # Get spikes from last time step\n", + " e_spikes = self.E.get_spike()\n", + " i_spikes = self.I.get_spike()\n", + "\n", + " # Update projections (spikes → synaptic currents)\n", + " # self.E2E(e_spikes) # Updates E2E.syn.g\n", + " # self.E2I(e_spikes)\n", + " # self.I2E(i_spikes)\n", + " # self.I2I(i_spikes)\n", + "\n", + " # Update neurons (currents → new V and spikes)\n", + " self.E(input[:800] if len(input) >= 800 else input)\n", + " self.I(input[800:] if len(input) > 800 else jnp.zeros(200) * u.nA)\n", + "\n", + " return e_spikes, i_spikes\n", + "\n", + "# 2. Initialize\n", + "net = EINetwork()\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "# 3. Compile\n", + "@brainstate.transform.jit\n", + "def step(input):\n", + " return net.update(input)\n", + "\n", + "# 4. Simulate (commented out for quick execution)\n", + "# times = u.math.arange(0*u.ms, 1000*u.ms, 0.1*u.ms)\n", + "# results = brainstate.transform.for_loop(step, times)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "State Flow:\n", + "\n", + "```text\n", + "Time t:\n", + "┌──────────────────────────────────────────┐\n", + "│ States at t-1: │\n", + "│ E.V[t-1], E.spike[t-1] │\n", + "│ I.V[t-1], I.spike[t-1] │\n", + "│ E2E.syn.g[t-1], ... │\n", + "└──────────────────────────────────────────┘\n", + " ↓\n", + "┌──────────────────────────────────────────┐\n", + "│ Projection Updates: │\n", + "│ E2E.syn.g[t] = f(g[t-1], E.spike[t-1])│\n", + "│ ... (other projections) │\n", + "└──────────────────────────────────────────┘\n", + " ↓\n", + "┌──────────────────────────────────────────┐\n", + "│ Neuron Updates: │\n", + "│ E.V[t] = f(V[t-1], Σ currents[t]) │\n", + "│ E.spike[t] = E.V[t] >= V_th │\n", + "│ ... (other neurons) │\n", + "└──────────────────────────────────────────┘\n", + " ↓\n", + "Time t+1...\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Considerations\n", + "\n", + "### Memory Management\n", + "\n", + "- States are preallocated\n", + "- In-place updates when possible\n", + "- Efficient batching support\n", + "- Automatic garbage collection\n", + "\n", + "### Compilation Strategy\n", + "\n", + "- Compile simulation loops\n", + "- Batch operations when possible\n", + "- Use ``for_loop`` for long sequences\n", + "- Leverage JAX's XLA optimization\n", + "\n", + "### Hardware Acceleration\n", + "\n", + "- Automatic GPU dispatch for large arrays\n", + "- TPU support for massive parallelism\n", + "- Efficient CPU fallback for small problems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "``brainpy.state`` 's architecture provides:\n", + "\n", + "✅ **Clear Abstractions**: Neurons, synapses, and projections with well-defined roles\n", + "\n", + "✅ **State Management**: Explicit, efficient handling of dynamical variables\n", + "\n", + "✅ **Modularity**: Compose complex models from simple components\n", + "\n", + "✅ **Performance**: JIT compilation and hardware acceleration\n", + "\n", + "✅ **Scientific Accuracy**: Integrated physical units\n", + "\n", + "✅ **Extensibility**: Easy to add custom components\n", + "\n", + "✅ **Modern Design**: Built on proven frameworks (JAX, brainstate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next Steps\n", + "\n", + "- Learn about specific components: [neurons](neurons.ipynb), [synapses](synapses.ipynb), [projections](projections.ipynb)\n", + "- Understand state management in depth: [state-management](state-management.ipynb)\n", + "- See practical examples in the [tutorials](../tutorials/basic/01-lif-neuron.ipynb)\n", + "- Explore the ecosystem: [brainstate docs](https://brainstate.readthedocs.io/)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/core-concepts/index.rst b/docs_state/quickstart/core-concepts/index.rst similarity index 94% rename from docs_state/core-concepts/index.rst rename to docs_state/quickstart/core-concepts/index.rst index 1090e61e..92e14c70 100644 --- a/docs_state/core-concepts/index.rst +++ b/docs_state/quickstart/core-concepts/index.rst @@ -40,10 +40,10 @@ Each concept builds upon the previous ones, so we recommend reading them in orde :hidden: :maxdepth: 1 - architecture.rst - neurons.rst - synapses.rst - projections.rst - state-management.rst + architecture.ipynb + neurons.ipynb + synapses.ipynb + projections.ipynb + state-management.ipynb diff --git a/docs_state/quickstart/core-concepts/neurons.ipynb b/docs_state/quickstart/core-concepts/neurons.ipynb new file mode 100644 index 00000000..74b6147d --- /dev/null +++ b/docs_state/quickstart/core-concepts/neurons.ipynb @@ -0,0 +1,1223 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neurons\n", + "\n", + "Neurons are the fundamental computational units in `brainpy.state`. This document explains how neurons work, what models are available, and how to use and create them.\n", + "\n", + "## Overview\n", + "\n", + "In `brainpy.state`, neurons model the dynamics of neural populations. Each neuron model:\n", + "\n", + "- Maintains **membrane potential** (voltage)\n", + "- Integrates **input currents**\n", + "- Generates **spikes** when threshold is crossed\n", + "- **Resets** after spiking (various strategies)\n", + "\n", + "All neuron models inherit from the base `Neuron` class and follow consistent interfaces.\n", + "\n", + "## Basic Usage\n", + "\n", + "### Creating Neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainunit as u\n", + "\n", + "# Create a population of 100 LIF neurons\n", + "neurons = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initializing States\n", + "\n", + "Before simulation, initialize neuron states:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import brainstate\n", + "\n", + "# Initialize all states to default values\n", + "brainstate.nn.init_all_states(neurons)\n", + "\n", + "# Or with specific batch in_in_size\n", + "brainstate.nn.init_all_states(neurons, batch_in_in_size=32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running Neurons\n", + "\n", + "Update neurons by calling them with input current:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Voltage shape: (100,)\n", + "Spikes shape: (100,)\n" + ] + } + ], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "# Single time step - provide input for all neurons\n", + "# Create input current array matching neuron population in_in_size\n", + "input_current = jnp.ones(100) * 2.0 * u.nA # 100 neurons, each gets 2.0 nA\n", + "\n", + "# Access results\n", + "voltage = neurons.V.value # Membrane potential\n", + "spikes = neurons.get_spike() # Spike output\n", + "\n", + "print(f\"Voltage shape: {voltage.shape}\")\n", + "print(f\"Spikes shape: {spikes.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Available Neuron Models\n", + "\n", + "### IF (Integrate-and-Fire)\n", + "\n", + "The simplest spiking neuron model.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "\n", + "$$\n", + "\\tau \\frac{dV}{dt} = -V + R \\cdot I(t)\n", + "$$\n", + "**Spike condition:** If :$ V \\geq V_{th} $, emit spike and reset.\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "IF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=20. * msecond,\n", + " V_th=1. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import brainpy\n", + "import brainunit as u\n", + "\n", + "# IF neuron - simple parameters\n", + "neuron = brainpy.state.IF(\n", + " in_size=100,\n", + " V_th=1. * u.mV, # Spike threshold \n", + " tau=20. * u.ms, # Membrane time constant\n", + " R=1. * u.ohm # Input resistance\n", + ")\n", + "\n", + "# Initialize the neuron\n", + "import brainstate\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parameters:**\n", + "\n", + "- `in_size`: Number of neurons\n", + "- `V_rest`: Resting potential\n", + "- `V_th`: Spike threshold\n", + "- `V_reset`: Reset potential after spike\n", + "- `tau`: Membrane time constant\n", + "- `R`: Input resistance\n", + "\n", + "**Use cases:**\n", + "\n", + "- Simple rate coding\n", + "- Fast simulations\n", + "- Theoretical studies\n", + "\n", + "### LIF (Leaky Integrate-and-Fire)\n", + "\n", + "The most commonly used spiking neuron model.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "\n", + "$$\n", + "\\tau \\frac{dV}{dt} = -(V - V_{rest}) + R \\cdot I(t)\n", + "$$\n", + "**Spike condition:** If :$V \\geq V_{th}$, emit spike and reset.\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_initializer=Normal(mean=-65.0, std=5.0),\n", + " V=HiddenState(\n", + " value=float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import braintools\n", + "import brainunit as u\n", + "\n", + "neuron = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " R=1. * u.ohm,\n", + " V_initializer=braintools.init.Normal(-65., 5., unit=u.mV)\n", + ")\n", + "\n", + "# Initialize the neuron\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parameters:**\n", + "\n", + "All IF parameters, plus:\n", + "\n", + "- `V_initializer`: How to initialize membrane potential\n", + "\n", + "**Key Features:**\n", + "\n", + "- Leak toward resting potential\n", + "- Realistic temporal integration\n", + "- Well-studied dynamics\n", + "\n", + "**Use cases:**\n", + "\n", + "- General spiking neural networks\n", + "- Cortical neuron modeling\n", + "- Learning and training\n", + "\n", + "### LIFRef (LIF with Refractory Period)\n", + "\n", + "LIF neuron with absolute refractory period.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "Same as LIF, but after spiking:\n", + "\n", + "- Neuron is \"frozen\" for refractory period\n", + "- No integration during refractory period\n", + "- More biologically realistic\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIFRef(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " tau_ref=2. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " ),\n", + " last_spike_time=ShortTermState(\n", + " value=~float32[100] * msecond\n", + " )\n", + ")" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "neuron = brainpy.state.LIFRef(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " tau_ref=2. * u.ms, # Refractory period\n", + " R=1. * u.ohm\n", + ")\n", + "\n", + "# Initialize the neuron\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Additional Parameters:**\n", + "\n", + "- `tau_ref`: Refractory period duration\n", + "\n", + "**Key Features:**\n", + "\n", + "- Absolute refractory period\n", + "- Prevents immediate re-firing\n", + "- More realistic spike timing\n", + "\n", + "**Use cases:**\n", + "\n", + "- Precise temporal coding\n", + "- Biological realism\n", + "- Rate regulation\n", + "\n", + "### ALIF (Adaptive Leaky Integrate-and-Fire)\n", + "\n", + "LIF with spike-frequency adaptation.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\tau \\frac{dV}{dt} &= -(V - V_{rest}) - R \\cdot w + R \\cdot I(t) \\\\\n", + "\\tau_w \\frac{dw}{dt} &= -w\n", + "\\end{aligned}\n", + "$$\n", + "When spike occurs: :$w \\leftarrow w + \\beta$\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ALIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " tau_a=200. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " beta=0.1 * namp,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " a_initializer=Constant(value=0.0),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " ),\n", + " a=HiddenState(\n", + " value=ShapedArray(float32[100], weak_type=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "neuron = brainpy.state.ALIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " tau_a=200. * u.ms, # Adaptation time constant\n", + " beta=0.1 * u.nA, # Spike-triggered adaptation\n", + " R=1. * u.ohm\n", + ")\n", + "\n", + "# Initialize the neuron\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Additional Parameters:**\n", + "\n", + "- `tau_w`: Adaptation time constant\n", + "- `beta`: Adaptation increment per spike\n", + "\n", + "**Key Features:**\n", + "\n", + "- Spike-frequency adaptation\n", + "- Reduced firing with sustained input\n", + "- More complex dynamics\n", + "\n", + "**Use cases:**\n", + "\n", + "- Cortical neuron modeling\n", + "- Sensory adaptation\n", + "- Complex temporal patterns\n", + "\n", + "## Reset Modes\n", + "\n", + "BrainPy supports different reset behaviors after spiking:\n", + "\n", + "### Soft Reset (Default)\n", + "\n", + "Subtract threshold from membrane potential:\n", + "\n", + "\n", + "$$\n", + "V \\leftarrow V - V_{th}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neuron = brainpy.state.LIF(\n", + " in_size=100, \n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " spk_reset='soft'\n", + ")\n", + "\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Properties:**\n", + "\n", + "- Preserves extra charge above threshold\n", + "- Allows rapid re-firing\n", + "- Common in machine learning\n", + "\n", + "### Hard Reset\n", + "\n", + "Reset to fixed potential:\n", + "\n", + "\n", + "$$\n", + "V \\leftarrow V_{reset}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=hard,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neuron = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " spk_reset='hard'\n", + ")\n", + "\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Properties:**\n", + "\n", + "- Discards extra charge\n", + "- More biologically realistic\n", + "- Prevents immediate re-firing\n", + "\n", + "### Choosing Reset Mode\n", + "\n", + "- **Soft reset**: Machine learning, rate coding, fast oscillations\n", + "- **Hard reset**: Biological modeling, temporal coding, realism\n", + "\n", + "## Spike Functions\n", + "\n", + "For training spiking neural networks, use surrogate gradients:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=-65. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neuron = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " spk_fun=braintools.surrogate.ReluGrad()\n", + ")\n", + "\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Available surrogate functions:\n", + "\n", + "- `ReluGrad()`: ReLU-like gradient\n", + "- `SigmoidGrad()`: Sigmoid-like gradient\n", + "- `GaussianGrad()`: Gaussian-like gradient\n", + "- `SuperSpike()`: SuperSpike surrogate\n", + "\n", + "See Tutorial 3 for training details.\n", + "\n", + "## Advanced Features\n", + "\n", + "### Initialization Strategies\n", + "\n", + "Different ways to initialize membrane potential:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10. * msecond,\n", + " V_th=-50. * mvolt,\n", + " V_rest=-65. * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Uniform(low=-70.0, high=-60.0),\n", + " V=HiddenState(\n", + " value=float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Constant initialization\n", + "neuron1 = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " tau=10. * u.ms,\n", + " V_initializer=braintools.init.Constant(-65., unit=u.mV)\n", + ")\n", + "brainstate.nn.init_all_states(neuron1)\n", + "\n", + "# Normal distribution\n", + "neuron2 = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " tau=10. * u.ms,\n", + " V_initializer=braintools.init.Normal(-65., 5., unit=u.mV)\n", + ")\n", + "brainstate.nn.init_all_states(neuron2)\n", + "\n", + "# Uniform distribution\n", + "neuron3 = brainpy.state.LIF(\n", + " in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " tau=10. * u.ms,\n", + " V_initializer=braintools.init.Uniform(-70., -60., unit=u.mV)\n", + ")\n", + "brainstate.nn.init_all_states(neuron3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Accessing Neuron States" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Membrane potential (with units)\n", + "voltage = neuron.V.value # Quantity with units\n", + "\n", + "# Spike output (binary or real-valued)\n", + "spikes = neuron.get_spike()\n", + "\n", + "# Access underlying array (without units)\n", + "voltage_array = neuron.V.value.to_decimal(u.mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Batched Simulation\n", + "\n", + "Simulate multiple trials in parallel:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "# Initialize with batch dimension\n", + "brainstate.nn.init_all_states(neuron, batch_in_in_size=32)\n", + "\n", + "# Input shape: (batch_in_size, in_size)\n", + "# For 32 batches of 100 neurons each\n", + "input_current = jnp.ones((32, 100)) * 2.0 * u.nA\n", + "neuron(input_current)\n", + "\n", + "# Output shape: (batch_in_size, in_size)\n", + "spikes = neuron.get_spike()\n", + "print(f\"Spikes shape: {spikes.shape}\") # Should be (32, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Complete Example\n", + "\n", + "Here's a complete example simulating a LIF neuron:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "import jax.numpy as jnp\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Set time step\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Create neuron\n", + "neuron = brainpy.state.LIF(\n", + " in_size=1,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " spk_reset='hard'\n", + ")\n", + "\n", + "# Initialize\n", + "brainstate.nn.init_all_states(neuron)\n", + "\n", + "# Simulation parameters\n", + "duration = 200. * u.ms\n", + "dt = brainstate.environ.get_dt()\n", + "times = u.math.arange(0. * u.ms, duration, dt)\n", + "\n", + "# Input current (step input)\n", + "def get_input(t):\n", + " if t > 50*u.ms:\n", + " return jnp.ones(1) * 20.0 * u.mA # Array of in_in_size 1\n", + " else:\n", + " return jnp.zeros(1) * u.mA # Array of in_in_size 1\n", + "\n", + "# Run simulation\n", + "voltages = []\n", + "spikes = []\n", + "\n", + "for t in times:\n", + " neuron(get_input(t))\n", + " voltages.append(neuron.V.value)\n", + " spikes.append(neuron.get_spike())\n", + "\n", + "# Plot results\n", + "voltages = u.math.asarray(voltages)\n", + "times_plot = times.to_decimal(u.ms)\n", + "voltages_plot = voltages.to_decimal(u.mV).squeeze() # Remove in_in_size dimension\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(times_plot, voltages_plot)\n", + "plt.axhline(y=-50, color='r', linestyle='--', label='Threshold')\n", + "plt.xlabel('Time (ms)')\n", + "plt.ylabel('Membrane Potential (mV)')\n", + "plt.title('LIF Neuron Response')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating Custom Neurons\n", + "\n", + "You can create custom neuron models by inheriting from `Neuron`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate\n", + "from brainpy.state import Neuron\n", + "\n", + "class MyNeuron(Neuron):\n", + " def __init__(self, in_size, tau, V_th, **kwargs):\n", + " super().__init__(in_size, **kwargs)\n", + "\n", + " # Store parameters\n", + " self.tau = tau\n", + " self.V_th = V_th\n", + "\n", + " # Initialize states\n", + " self.V = brainstate.ShortTermState(\n", + " braintools.init.Constant(0., unit=u.mV)(in_size)\n", + " )\n", + " self.spike = brainstate.ShortTermState(\n", + " jnp.zeros(in_size)\n", + " )\n", + "\n", + " def update(self, x):\n", + " # Get time step\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Update membrane potential (custom dynamics)\n", + " dV = (-self.V.value + x) / self.tau * dt\n", + " V_new = self.V.value + dV\n", + "\n", + " # Check for spikes\n", + " spike = (V_new >= self.V_th).astype(float)\n", + "\n", + " # Reset\n", + " V_new = jnp.where(spike > 0, 0. * u.mV, V_new)\n", + "\n", + " # Update states\n", + " self.V.value = V_new\n", + " self.spike.value = spike\n", + "\n", + " return spike\n", + "\n", + " def get_spike(self):\n", + " return self.spike.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usage:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "neuron = MyNeuron(in_size=100, tau=10*u.ms, V_th=1*u.mV)\n", + "brainstate.nn.init_all_states(neuron)\n", + "\n", + "# Create appropriate input current\n", + "input_current = jnp.ones(100) * 0.5 * u.nA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Tips\n", + "\n", + "1. **Use JIT compilation** for repeated simulations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "@brainstate.transform.jit\n", + "def simulate_step(input):\n", + " neuron(input)\n", + " return neuron.V.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Batch multiple trials** for parallelism:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MyNeuron(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=InvSquareGrad(alpha=100.0),\n", + " tau=10 * msecond,\n", + " V_th=1 * mvolt,\n", + " V=ShortTermState(\n", + " value=~float32[100] * mvolt\n", + " ),\n", + " spike=ShortTermState(\n", + " value=ShapedArray(float32[100])\n", + " )\n", + ")" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brainstate.nn.init_all_states(neuron, batch_in_in_size=100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **Use appropriate data types**:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Float32 is usually sufficient and faster\n", + "brainstate.environ.set(dtype=jnp.float32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. **Preallocate arrays** when recording:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_steps = 2000 # Example: 2000 time steps\n", + "neuron_in_in_size = 100 # Example: 100 neurons\n", + "voltages = jnp.zeros((n_steps, neuron_in_in_size))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "neuron = brainpy.state.LIF(\n", + " in_in_size=100, \n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " tau=10. * u.ms,\n", + " spk_reset='soft'\n", + ")\n", + "# Use soft reset for higher firing rates\n", + "\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "neuron = brainpy.state.LIF(100, tau=10*u.ms, spk_reset='soft')\n", + "# Use soft reset for higher firing rates" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "neuron = brainpy.state.LIFRef(\n", + " in_in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " tau_ref=2. * u.ms,\n", + " spk_reset='hard'\n", + ")\n", + "# Use refractory period for precise timing\n", + "\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "neuron = brainpy.state.LIFRef(\n", + " 100,\n", + " tau=10*u.ms,\n", + " tau_ref=2*u.ms,\n", + " spk_reset='hard'\n", + ")\n", + "# Use refractory period for precise timing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "neuron = brainpy.state.ALIF(\n", + " in_in_size=100,\n", + " V_rest=-65. * u.mV,\n", + " V_th=-50. * u.mV,\n", + " V_reset=-65. * u.mV,\n", + " tau=10. * u.ms,\n", + " tau_w=200. * u.ms,\n", + " a=0.01,\n", + " b=0.1 * u.nA,\n", + " spk_reset='soft'\n", + ")\n", + "# Adaptation creates bursting patterns\n", + "\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "neuron = brainpy.state.ALIF(\n", + " 100,\n", + " tau=10*u.ms,\n", + " tau_a=200*u.ms,\n", + " beta=0.01,\n", + " spk_reset='soft'\n", + ")\n", + "# Adaptation creates bursting patterns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "Neurons in `brainpy.state`:\n", + "\n", + "✅ **Multiple models**: IF, LIF, LIFRef, ALIF\n", + "\n", + "✅ **Physical units**: All parameters with proper units\n", + "\n", + "✅ **Flexible reset**: Soft or hard reset modes\n", + "\n", + "✅ **Training-ready**: Surrogate gradients for learning\n", + "\n", + "✅ **High performance**: JIT compilation and batching\n", + "\n", + "✅ **Extensible**: Easy to create custom models\n", + "\n", + "## Next Steps\n", + "\n", + "- Learn about synapses to connect neurons\n", + "- Explore projections for network connectivity\n", + "- Follow tutorials for hands-on practice\n", + "- See docs for network examples" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/quickstart/core-concepts/projections.ipynb b/docs_state/quickstart/core-concepts/projections.ipynb new file mode 100644 index 00000000..db0067ee --- /dev/null +++ b/docs_state/quickstart/core-concepts/projections.ipynb @@ -0,0 +1,1348 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Projections: Connecting Neural Populations\n", + "\n", + "Projections are `brainpy.state` 's mechanism for connecting neural populations.\n", + "They implement the **Communication-Synapse-Output (Comm-Syn-Out)** architecture,\n", + "which separates connectivity, synaptic dynamics, and output computation into modular components.\n", + "\n", + "This guide provides a comprehensive understanding of projections in `brainpy.state`.\n", + "\n", + "**Table of Contents**\n", + "\n", + "## Overview\n", + "\n", + "### What are Projections?\n", + "\n", + "A **projection** connects a presynaptic population to a postsynaptic population through:\n", + "\n", + "1. **Communication (Comm)**: How spikes propagate through connections\n", + "2. **Synapse (Syn)**: Temporal filtering and synaptic dynamics\n", + "3. **Output (Out)**: How synaptic currents affect postsynaptic neurons\n", + "\n", + "**Key benefits:**\n", + "\n", + "- Modular design (swap components independently)\n", + "- Biologically realistic (separate connectivity and dynamics)\n", + "- Efficient (optimized sparse operations)\n", + "- Flexible (combine components in different ways)\n", + "\n", + "### The Comm-Syn-Out Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Presynaptic Communication Synapse Output Postsynaptic\n", + "Population ──► (Connectivity) ──► (Dynamics) ──► (Current) ──► Population\n", + "\n", + "Spikes ──► Weight matrix ──► g(t) ──► I_syn ──► Neurons\n", + " Sparse/Dense Expon/Alpha CUBA/COBA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Flow:**\n", + "\n", + "1. Presynaptic spikes arrive\n", + "2. Communication: Spikes propagate through connectivity matrix\n", + "3. Synapse: Temporal dynamics filter the signal\n", + "4. Output: Convert to current/conductance\n", + "5. Postsynaptic neurons receive input\n", + "\n", + "### Types of Projections\n", + "\n", + "BrainPy provides two main projection types:\n", + "\n", + "**AlignPostProj**\n", + " - Align synaptic states with postsynaptic neurons\n", + " - Most common for standard neural networks\n", + " - Efficient memory layout\n", + "\n", + "**AlignPreProj**\n", + " - Align synaptic states with presynaptic neurons\n", + " - Useful for certain learning rules\n", + " - Different memory organization\n", + "\n", + "For most use cases, use `AlignPostProj`.\n", + "\n", + "## Communication Layer\n", + "\n", + "The Communication layer defines **how spikes propagate** through connections.\n", + "\n", + "### Dense Connectivity\n", + "\n", + "All neurons potentially connected (though weights may be zero).\n", + "\n", + "**Use case:** Small networks, fully connected layers" + ] + }, + { + "cell_type": "code", + "execution_count": 232, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "import braintools\n", + "\n", + "# Dense linear transformation\n", + "comm = brainstate.nn.Linear(\n", + " 100, # in_size\n", + " 50, # out_size\n", + " w_init=braintools.init.KaimingNormal(),\n", + " b_init=None # No bias for synapses\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Memory: O(n_pre × n_post)\n", + "- Computation: Full matrix multiplication\n", + "- Best for: Small networks, fully connected architectures\n", + "\n", + "### Sparse Connectivity\n", + "\n", + "Only a subset of connections exist (biologically realistic).\n", + "\n", + "**Use case:** Large networks, biological connectivity patterns\n", + "\n", + "#### Event-Based Fixed Probability\n", + "\n", + "Connect neurons with fixed probability." + ] + }, + { + "cell_type": "code", + "execution_count": 233, + "metadata": {}, + "outputs": [], + "source": [ + "# Sparse random connectivity (2% connection probability)\n", + "comm = brainstate.nn.EventFixedProb(\n", + " 1000, # pre_size\n", + " 800, # post_size\n", + " conn_num=0.02, # 2% connectivity\n", + " conn_weight=0.5 # Synaptic weight (unitless for event-based)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Memory: O(n_pre × n_post × prob)\n", + "- Computation: Only active connections\n", + "- Best for: Large-scale networks, biological models\n", + "\n", + "#### Event-Based All-to-All\n", + "\n", + "All neurons connected (but stored sparsely)." + ] + }, + { + "cell_type": "code", + "execution_count": 234, + "metadata": {}, + "outputs": [], + "source": [ + "# All-to-all sparse (event-driven)\n", + "comm = brainstate.nn.AllToAll(\n", + " 100, # pre_size\n", + " 100, # post_size\n", + " 0.3 # Unitless weight\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Event-Based One-to-One\n", + "\n", + "One-to-one mapping (same size populations)." + ] + }, + { + "cell_type": "code", + "execution_count": 235, + "metadata": {}, + "outputs": [], + "source": [ + "size=100\n", + "weight=1.0\n", + "\n", + "# One-to-one connections\n", + "comm = brainstate.nn.OneToOne(\n", + " size,\n", + " weight # Unitless weight\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use case:** Feedforward pathways, identity mappings\n", + "\n", + "### Comparison Table\n", + "\n", + "\n", + " * - Type\n", + " - Memory\n", + " - Speed\n", + " - Use Case\n", + " - Example\n", + " * - Linear (Dense)\n", + " - High (O(n²))\n", + " - Fast (optimized)\n", + " - Small networks\n", + " - Fully connected\n", + " * - EventFixedProb\n", + " - Low (O(n²p))\n", + " - Very fast\n", + " - Large networks\n", + " - Cortical connectivity\n", + " * - EventAll2All\n", + " - Medium\n", + " - Fast\n", + " - Medium networks\n", + " - Recurrent layers\n", + " * - EventOne2One\n", + " - Minimal (O(n))\n", + " - Fastest\n", + " - Feedforward\n", + " - Sensory pathways\n", + "\n", + "## Synapse Layer\n", + "\n", + "The Synapse layer defines **temporal dynamics** of synaptic transmission.\n", + "\n", + "### Exponential Synapse\n", + "\n", + "Single exponential decay (most common).\n", + "\n", + "**Dynamics:**\n", + "\n", + "\n", + "$$\n", + "\\tau \\frac{dg}{dt} = -g + \\sum_k \\delta(t - t_k)\n", + "$$\n", + "**Implementation:**" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "metadata": {}, + "outputs": [], + "source": [ + "# Exponential synapse with 5ms time constant\n", + "syn = brainpy.state.Expon.desc(\n", + " size=100, # Postsynaptic population size\n", + " tau=5.0 * u.ms # Decay time constant\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Single time constant\n", + "- Fast computation\n", + "- Good for most applications\n", + "\n", + "**When to use:** Default choice for most models\n", + "\n", + "### Alpha Synapse\n", + "\n", + "Dual exponential with rise and decay.\n", + "\n", + "**Dynamics:**\n", + "\n", + "\n", + "$$\n", + "\\tau \\frac{dg}{dt} = -g + h\n", + "\\tau \\frac{dh}{dt} = -h + \\sum_k \\delta(t - t_k)\n", + "$$\n", + "**Implementation:**" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "# Alpha synapse\n", + "syn = brainpy.state.Alpha.desc(\n", + " size=100,\n", + " tau=10.0 * u.ms # Characteristic time\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Realistic rise time\n", + "- Smoother response\n", + "- Slightly slower computation\n", + "\n", + "**When to use:** When rise time matters, more biological realism\n", + "\n", + "### NMDA Synapse\n", + "\n", + "Voltage-dependent NMDA receptors.\n", + "\n", + "**Dynamics:**\n", + "\n", + "\n", + "$$\n", + "g_{NMDA} = \\frac{g}{1 + \\eta [Mg^{2+}] e^{-\\gamma V}}\n", + "$$\n", + "**Implementation:**" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [], + "source": [ + "# NMDA receptor\n", + "syn = brainpy.state.BioNMDA(\n", + " in_size=100,\n", + " T_dur=100.0 * u.ms, # Slow decay\n", + " T=2.0 * u.ms, # Fast rise\n", + " alpha1=0.5 / u.mM, # Mg²⁺ sensitivity\n", + " g_initializer=1.2 * u.mM # Mg²⁺ concentration\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Voltage-dependent\n", + "- Slow kinetics\n", + "- Important for plasticity\n", + "\n", + "**When to use:** Long-term potentiation, working memory models\n", + "\n", + "### AMPA Synapse\n", + "\n", + "Fast glutamatergic transmission." + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "# AMPA receptor (fast excitation)\n", + "syn = brainpy.state.AMPA.desc(\n", + " size=100,\n", + " tau=2.0 * u.ms # Fast decay (~2ms)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**When to use:** Fast excitatory transmission\n", + "\n", + "### GABA Synapse\n", + "\n", + "Inhibitory transmission.\n", + "\n", + "**GABAa (fast):**" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [], + "source": [ + "# GABAa receptor (fast inhibition)\n", + "syn = brainpy.state.GABAa.desc(\n", + " size=100,\n", + " tau=6.0 * u.ms # ~6ms decay\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**GABAb (slow):**" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": {}, + "outputs": [], + "source": [ + "# GABAb receptor (slow inhibition)\n", + "syn = brainpy.state.GABAa(\n", + " in_size=100,\n", + " T_dur=150.0 * u.ms, # Very slow\n", + " T=3.5 * u.ms\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**When to use:**\n", + "- GABAa: Fast inhibition, cortical networks\n", + "- GABAb: Slow inhibition, rhythm generation\n", + "\n", + "### Custom Synapses\n", + "\n", + "Create custom synaptic dynamics by subclassing `Synapse`." + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "class DoubleExpSynapse(brainpy.state.Synapse):\n", + " \"\"\"Custom synapse with two time constants.\"\"\"\n", + "\n", + " def __init__(self, size, tau_fast=2*u.ms, tau_slow=10*u.ms, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + " self.tau_fast = tau_fast\n", + " self.tau_slow = tau_slow\n", + "\n", + " # State variables\n", + " self.g_fast = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.g_slow = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.g_fast.value = jnp.zeros(shape)\n", + " self.g_slow.value = jnp.zeros(shape)\n", + "\n", + " def update(self, x):\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Fast component\n", + " dg_fast = -self.g_fast.value / self.tau_fast.to_decimal(u.ms)\n", + " self.g_fast.value += dg_fast * dt.to_decimal(u.ms) + x * 0.7\n", + "\n", + " # Slow component\n", + " dg_slow = -self.g_slow.value / self.tau_slow.to_decimal(u.ms)\n", + " self.g_slow.value += dg_slow * dt.to_decimal(u.ms) + x * 0.3\n", + "\n", + " return self.g_fast.value + self.g_slow.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Output Layer\n", + "\n", + "The Output layer defines **how synaptic conductance affects neurons**.\n", + "\n", + "### CUBA (Current-Based)\n", + "\n", + "Synaptic conductance directly becomes current.\n", + "\n", + "**Model:**\n", + "\n", + "\n", + "$$\n", + "I_{syn} = g_{syn}\n", + "$$\n", + "**Implementation:**" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [], + "source": [ + "# Define population sizes\n", + "pre_size = 100\n", + "post_size = 50\n", + "\n", + "# Define connectivity parameters\n", + "conn_num = 0.1\n", + "conn_weight = 0.5\n", + "\n", + "comm = brainstate.nn.EventFixedProb(\n", + " pre_size, post_size, conn_num, conn_weight\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Simple and fast\n", + "- No voltage dependence\n", + "- Good for rate-based models\n", + "\n", + "**When to use:**\n", + "- Abstract models\n", + "- When voltage dependence not important\n", + "- Faster computation needed\n", + "\n", + "### COBA (Conductance-Based)\n", + "\n", + "Synaptic conductance with reversal potential.\n", + "\n", + "**Model:**\n", + "\n", + "\n", + "$$\n", + "I_{syn} = g_{syn} (E_{syn} - V_{post})\n", + "$$\n", + "**Implementation:**" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [], + "source": [ + "# Excitatory conductance-based\n", + "out_exc = brainpy.state.COBA.desc(E=0.0 * u.mV)\n", + "\n", + "# Inhibitory conductance-based\n", + "out_inh = brainpy.state.COBA.desc(E=-80.0 * u.mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Characteristics:**\n", + "\n", + "- Voltage-dependent\n", + "- Biologically realistic\n", + "- Self-limiting (saturates near reversal)\n", + "\n", + "**When to use:**\n", + "- Biologically detailed models\n", + "- When voltage dependence matters\n", + "- Shunting inhibition needed\n", + "\n", + "### MgBlock (NMDA)\n", + "\n", + "Voltage-dependent magnesium block for NMDA." + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [], + "source": [ + "# NMDA with Mg²⁺ block\n", + "out_nmda = brainpy.state.MgBlock.desc(\n", + " E=0.0 * u.mV,\n", + " cc_Mg=1.2 * u.mM,\n", + " alpha=0.062 / u.mV,\n", + " beta=3.57\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**When to use:** NMDA receptors, voltage-dependent plasticity\n", + "\n", + "## Complete Projection Examples\n", + "\n", + "### Example 1: Simple Feedforward" + ] + }, + { + "cell_type": "code", + "execution_count": 246, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainstate\n", + "import brainunit as u\n", + "import jax.numpy as jnp\n", + "\n", + "# Create populations\n", + "pre = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "post = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Create projection: 100 → 50 neurons\n", + "proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(\n", + " 100, # pre_size\n", + " 50, # post_size\n", + " conn_num=0.1, # 10% connectivity\n", + " conn_weight=0.5 # Weight\n", + " ),\n", + " syn=brainpy.state.Expon.desc(\n", + " in_size=50, # Postsynaptic size\n", + " tau=5.0 * u.ms\n", + " ),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post # Postsynaptic population\n", + ")\n", + "\n", + "# Initialize\n", + "brainstate.nn.init_all_states([pre, post, proj])\n", + "\n", + "# Simulate\n", + "def step(t, i, inp):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " # Get presynaptic spikes\n", + " pre_spikes = pre.get_spike()\n", + "\n", + " # Update projection\n", + " proj(pre_spikes)\n", + "\n", + " # Update neurons\n", + " pre(inp)\n", + " post(0.0 * u.nA) # Projection provides input\n", + "\n", + " return pre.get_spike(), post.get_spike()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2: Excitatory-Inhibitory Network" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [], + "source": [ + "class EINetwork(brainstate.nn.Module):\n", + " def __init__(self, n_exc=800, n_inh=200):\n", + " super().__init__()\n", + "\n", + " # Populations\n", + " self.E = brainpy.state.LIF(n_exc, V_rest=-65*u.mV, V_th=-50*u.mV, tau=15*u.ms)\n", + " self.I = brainpy.state.LIF(n_inh, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + " # E → E projection (AMPA, excitatory)\n", + " self.E2E = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, conn_num=0.02, conn_weight=0.6*u.mS),\n", + " syn=brainpy.state.AMPA.desc(n_exc, tau=2.0*u.ms),\n", + " out=brainpy.state.COBA.desc(E=0.0*u.mV),\n", + " post=self.E\n", + " )\n", + "\n", + " # E → I projection (AMPA, excitatory)\n", + " self.E2I = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, conn_num=0.02, conn_weight=0.6*u.mS),\n", + " syn=brainpy.state.AMPA.desc(n_inh, tau=2.0*u.ms),\n", + " out=brainpy.state.COBA.desc(E=0.0*u.mV),\n", + " post=self.I\n", + " )\n", + "\n", + " # I → E projection (GABAa, inhibitory)\n", + " self.I2E = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, conn_num=0.02, conn_weight=6.7*u.mS),\n", + " syn=brainpy.state.GABAa.desc(n_exc, tau=6.0*u.ms),\n", + " out=brainpy.state.COBA.desc(E=-80.0*u.mV),\n", + " post=self.E\n", + " )\n", + "\n", + " # I → I projection (GABAa, inhibitory)\n", + " self.I2I = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, conn_num=0.02, conn_weight=6.7*u.mS),\n", + " syn=brainpy.state.GABAa.desc(n_inh, tau=6.0*u.ms),\n", + " out=brainpy.state.COBA.desc(E=-80.0*u.mV),\n", + " post=self.I\n", + " )\n", + "\n", + " def update(self, t, i, inp_e, inp_i):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " # Get spikes BEFORE updating neurons\n", + " spk_e = self.E.get_spike()\n", + " spk_i = self.I.get_spike()\n", + "\n", + " # Update all projections\n", + " self.E2E(spk_e)\n", + " self.E2I(spk_e)\n", + " self.I2E(spk_i)\n", + " self.I2I(spk_i)\n", + "\n", + " # Update neurons (projections provide synaptic input)\n", + " self.E(inp_e)\n", + " self.I(inp_i)\n", + "\n", + " return spk_e, spk_i" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 3: Multi-Timescale Synapses\n", + "\n", + "Combine AMPA (fast) and NMDA (slow) for realistic excitation." + ] + }, + { + "cell_type": "code", + "execution_count": 248, + "metadata": {}, + "outputs": [], + "source": [ + "class DualExcitatory(brainstate.nn.Module):\n", + " \"\"\"E → E with both AMPA and NMDA.\"\"\"\n", + "\n", + " def __init__(self, n_pre=100, n_post=100):\n", + " super().__init__()\n", + "\n", + " self.post = brainpy.state.LIF(n_post, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + " # Fast AMPA component\n", + " self.ampa_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_pre, n_post, conn_num=0.1, conn_weight=0.3*u.mS),\n", + " syn=brainpy.state.AMPA.desc(n_post, tau=2.0*u.ms),\n", + " out=brainpy.state.COBA.desc(E=0.0*u.mV),\n", + " post=self.post\n", + " )\n", + "\n", + " # Slow NMDA component\n", + " self.nmda_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_pre, n_post, conn_num=0.1, conn_weight=0.3*u.mS),\n", + " syn=brainpy.state.NMDA.desc(n_post, tau_decay=100.0*u.ms, tau_rise=2.0*u.ms),\n", + " out=brainpy.state.MgBlock.desc(E=0.0*u.mV, cc_Mg=1.2*u.mM),\n", + " post=self.post\n", + " )\n", + "\n", + " def update(self, t, i, pre_spikes):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " # Both projections share same presynaptic spikes\n", + " self.ampa_proj(pre_spikes)\n", + " self.nmda_proj(pre_spikes)\n", + "\n", + " # Post receives combined input\n", + " self.post(0.0 * u.nA)\n", + "\n", + " return self.post.get_spike()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Topics\n", + "\n", + "### Delay Projections\n", + "\n", + "Add synaptic delays to projections." + ] + }, + { + "cell_type": "code", + "execution_count": 249, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "\n", + "# Define post_neurons for demonstration\n", + "post_neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# To implement delay, use a separate Delay module\n", + "delay_time = 5.0 * u.ms\n", + "\n", + "# Create a network with delay\n", + "class DelayedProjection(brainstate.nn.Module):\n", + " def __init__(self, pre_size, post_size):\n", + " super().__init__()\n", + " \n", + " # Delay buffer for spikes\n", + " self.delay = brainstate.nn.Delay(\n", + " jax.ShapeDtypeStruct((pre_size,), bool), \n", + " delay_time\n", + " )\n", + " \n", + " # Standard projection\n", + " self.proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(pre_size, post_size, conn_num=0.1, conn_weight=0.5),\n", + " syn=brainpy.state.Expon.desc(post_size, tau=5.0*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post_neurons\n", + " )\n", + " \n", + " def update(self, pre_spikes):\n", + " # Retrieve delayed spikes\n", + " delayed_spikes = self.delay.retrieve_at_step(\n", + " u.math.asarray(delay_time / brainstate.environ.get_dt(), dtype=int)\n", + " )\n", + " # Update projection with delayed spikes\n", + " self.proj(delayed_spikes)\n", + " # Store current spikes in delay buffer\n", + " self.delay(pre_spikes)\n", + "\n", + "# Example usage:\n", + "# delayed_proj = DelayedProjection(100, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Use cases:**\n", + "- Biologically realistic transmission delays\n", + "- Axonal conduction delays\n", + "- Synchronization studies\n", + "\n", + "### Heterogeneous Weights\n", + "\n", + "Different weights for different connections." + ] + }, + { + "cell_type": "code", + "execution_count": 250, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "# Custom weight matrix\n", + "n_pre, n_post = 100, 50\n", + "weights = jnp.abs(brainstate.random.randn(n_pre, n_post)) * 0.5\n", + "\n", + "# Note: EventJitFPHomoLinear may not support heterogeneous weights\n", + "# For custom weights, consider using Linear or custom communication layer\n", + "# comm = brainstate.nn.Linear(n_pre, n_post, w_init=lambda key, shape: weights)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Learning Synapses\n", + "\n", + "Combine with plasticity (see ../tutorials/advanced/06-synaptic-plasticity)." + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [], + "source": [ + "# Projection with learnable weights\n", + "class PlasticProjection(brainstate.nn.Module):\n", + " def __init__(self, n_pre, n_post):\n", + " super().__init__()\n", + "\n", + " # Initialize weights as parameters\n", + " self.weights = brainstate.ParamState(\n", + " jnp.ones((n_pre, n_post)) * 0.5 * u.mS\n", + " )\n", + "\n", + " self.proj = brainpy.state.AlignPostProj(\n", + " comm=CustomComm(self.weights), # Use learnable weights\n", + " syn=brainpy.state.Expon.desc(n_post, tau=5.0*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post_neurons\n", + " )\n", + "\n", + " def update_weights(self, dw):\n", + " \"\"\"Update weights based on learning rule.\"\"\"\n", + " self.weights.value += dw" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "### Choosing Communication Type\n", + "\n", + "**Use EventFixedProb when:**\n", + "- Large networks (>1000 neurons)\n", + "- Sparse connectivity (<10%)\n", + "- Biological models\n", + "\n", + "**Use Linear when:**\n", + "- Small networks (<1000 neurons)\n", + "- Fully connected layers\n", + "- Training with gradients\n", + "\n", + "**Use EventOne2One when:**\n", + "- Same-size populations\n", + "- Feedforward pathways\n", + "- Identity mappings\n", + "\n", + "### Choosing Synapse Type\n", + "\n", + "**Use Expon when:**\n", + "- Default choice for most models\n", + "- Fast computation needed\n", + "- Simple dynamics sufficient\n", + "\n", + "**Use Alpha when:**\n", + "- Rise time is important\n", + "- More biological realism\n", + "- Smoother responses\n", + "\n", + "**Use AMPA/NMDA/GABA when:**\n", + "- Specific receptor types matter\n", + "- Pharmacological studies\n", + "- Detailed biological models\n", + "\n", + "### Choosing Output Type\n", + "\n", + "**Use CUBA when:**\n", + "- Abstract models\n", + "- Training with gradients\n", + "- Speed is critical\n", + "\n", + "**Use COBA when:**\n", + "- Biological realism needed\n", + "- Voltage dependence matters\n", + "- Shunting inhibition required\n", + "\n", + "### Performance Tips\n", + "\n", + "1. **Sparse over Dense:** Use sparse connectivity for large networks\n", + "2. **Batch initialization:** Initialize all modules together\n", + "3. **JIT compile:** Wrap simulation loop with `@brainstate.transform.jit`\n", + "4. **Appropriate precision:** Use float32 unless high precision needed\n", + "5. **Minimize communication:** Group projections with same connectivity\n", + "\n", + "### Common Patterns\n", + "\n", + "**Pattern 1: Dale's Principle**\n", + "\n", + "Neurons are either excitatory OR inhibitory (not both)." + ] + }, + { + "cell_type": "code", + "execution_count": 252, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[LIF(\n", + " in_size=(800,),\n", + " out_size=(800,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[800] * mvolt\n", + " )\n", + " ),\n", + " LIF(\n", + " in_size=(200,),\n", + " out_size=(200,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[200] * mvolt\n", + " )\n", + " )]" + ] + }, + "execution_count": 252, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set simulation timestep if not already set\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Separate excitatory and inhibitory populations\n", + "E = brainpy.state.LIF(800, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "I = brainpy.state.LIF(200, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Initialize states\n", + "brainstate.nn.init_all_states([E, I])\n", + "\n", + "# E always excitatory (E=0mV)\n", + "# I always inhibitory (E=-80mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Pattern 2: Balanced Networks**\n", + "\n", + "Excitation balanced by inhibition." + ] + }, + { + "cell_type": "code", + "execution_count": 253, + "metadata": {}, + "outputs": [], + "source": [ + "# Strong inhibition to balance excitation\n", + "w_exc = 0.6 * u.mS\n", + "w_inh = 6.7 * u.mS # ~10× stronger\n", + "\n", + "# More E neurons than I (4:1 ratio)\n", + "n_exc = 800\n", + "n_inh = 200" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Pattern 3: Recurrent Loops**\n", + "\n", + "Self-connections for persistent activity." + ] + }, + { + "cell_type": "code", + "execution_count": 254, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AlignPostProj(\n", + " name=AlignPostProj30,\n", + " modules=(),\n", + " merging=True,\n", + " comm=EventFixedNumConn(\n", + " in_size=(800,),\n", + " out_size=(800,),\n", + " efferent_target=post,\n", + " conn_num=16,\n", + " seed=None,\n", + " allow_multi_conn=True,\n", + " weight=ParamState(\n", + " value=~float32[] * msiemens\n", + " ),\n", + " conn=FixedPostNumConn(float32[800, 800], nse=12800)\n", + " ),\n", + " syn=Expon(\n", + " in_size=(800,),\n", + " out_size=(800,),\n", + " tau=5 * msecond,\n", + " g_initializer=Constant(value=0.0 * msiemens),\n", + " g=HiddenState(\n", + " value=~float32[800] * msiemens\n", + " )\n", + " ),\n", + " out=COBA(\n", + " E=0 * mvolt\n", + " ),\n", + " post=LIF(\n", + " in_size=(800,),\n", + " out_size=(800,),\n", + " before_updates={\n", + " \"(, (800,), {'tau': 5 * msecond}) // (, (), {'E': 0 * mvolt})\": _AlignPost(\n", + " syn=Expon(...),\n", + " out=COBA(...)\n", + " )\n", + " },\n", + " current_inputs={\n", + " 'AlignPostProj30': COBA(...)\n", + " },\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[800] * mvolt\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 254, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Set simulation timestep if not already set\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Define E population for demonstration\n", + "E = brainpy.state.LIF(800, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "n_exc = 800\n", + "\n", + "# Initialize states\n", + "brainstate.nn.init_all_states(E)\n", + "\n", + "# Excitatory recurrence (working memory)\n", + "E2E = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, conn_num=0.02, conn_weight=0.5*u.mS),\n", + " syn=brainpy.state.Expon.desc(n_exc, tau=5*u.ms),\n", + " out=brainpy.state.COBA.desc(E=0*u.mV),\n", + " post=E\n", + ")\n", + "\n", + "# Initialize projection states\n", + "brainstate.nn.init_all_states(E2E)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Troubleshooting\n", + "\n", + "### Issue: Spikes not propagating\n", + "\n", + "**Symptoms:** Postsynaptic neurons don't receive input\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Check spike timing: Call `get_spike()` BEFORE updating\n", + "2. Verify connectivity: Check `prob` and `weight`\n", + "3. Check update order: Projections before neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 255, + "metadata": {}, + "outputs": [], + "source": [ + "# Set simulation timestep\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Define neurons and projection for demonstration\n", + "pre = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "post = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 50, conn_num=0.1, conn_weight=0.5),\n", + " syn=brainpy.state.Expon.desc(50, tau=5.0*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post\n", + ")\n", + "\n", + "# Initialize all states\n", + "brainstate.nn.init_all_states([pre, post, proj])\n", + "\n", + "# Define input current\n", + "inp = jnp.ones(100) * 5.0 * u.nA\n", + "\n", + "# CORRECT order - in update function context\n", + "def correct_update(t, i):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " spk = pre.get_spike() # Get spikes from previous step\n", + " proj(spk) # Update projection\n", + " pre(inp) # Update neurons\n", + " return spk\n", + "\n", + "# Example: run one step\n", + "result = correct_update(0.0*u.ms, 0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue: Network silent or exploding\n", + "\n", + "**Symptoms:** No activity or runaway firing\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Balance E/I weights (I should be ~10× stronger)\n", + "2. Check reversal potentials (E=0mV, I=-80mV)\n", + "3. Verify threshold and reset values\n", + "4. Add external input" + ] + }, + { + "cell_type": "code", + "execution_count": 256, + "metadata": {}, + "outputs": [], + "source": [ + "# Balanced weights\n", + "w_exc = 0.5 * u.mS\n", + "w_inh = 5.0 * u.mS # Strong inhibition\n", + "\n", + "# Proper reversal potentials\n", + "out_exc = brainpy.state.COBA.desc(E=0.0 * u.mV)\n", + "out_inh = brainpy.state.COBA.desc(E=-80.0 * u.mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue: Slow simulation\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Use sparse connectivity (EventFixedProb)\n", + "2. Use JIT compilation\n", + "3. Use CUBA instead of COBA (if appropriate)\n", + "4. Reduce connectivity or neurons" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "# Fast configuration\n", + "@brainstate.transform.jit\n", + "def simulate_step(net, t, i, inp):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " return net.update(t, i, inp, inp)\n", + "\n", + "# Sparse connectivity\n", + "comm = brainstate.nn.EventFixedProb(1000, 1000, conn_num=0.02, conn_weight=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further Reading\n", + "\n", + "- ../tutorials/basic/03-network-connections - Network connections tutorial\n", + "- architecture - Overall BrainPy architecture\n", + "- synapses - Detailed synapse models\n", + "- ../tutorials/advanced/06-synaptic-plasticity - Learning in projections\n", + "- ../tutorials/advanced/07-large-scale-simulations - Scaling projections\n", + "\n", + "## Summary\n", + "\n", + "**Key takeaways:**\n", + "\n", + "✅ Projections use Comm-Syn-Out architecture\n", + "\n", + "✅ Communication: Dense (Linear) or Sparse (EventFixedProb)\n", + "\n", + "✅ Synapse: Temporal dynamics (Expon, Alpha, AMPA, GABA, NMDA)\n", + "\n", + "✅ Output: Current-based (CUBA) or Conductance-based (COBA)\n", + "\n", + "✅ Choose components based on scale, realism, and performance needs\n", + "\n", + "✅ Follow Dale's principle and balanced E/I patterns\n", + "\n", + "✅ Get spikes BEFORE updating for correct propagation\n", + "\n", + "**Quick reference:**" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [], + "source": [ + "# Define postsynaptic neurons for template\n", + "post_neurons = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "n_pre = 100\n", + "n_post = 50\n", + "\n", + "# Standard projection template\n", + "proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(n_pre, n_post, conn_num=0.1, conn_weight=0.5*u.mS),\n", + " syn=brainpy.state.Expon.desc(n_post, tau=5.0*u.ms),\n", + " out=brainpy.state.COBA.desc(E=0.0*u.mV),\n", + " post=post_neurons\n", + ")\n", + "\n", + "# Usage in network\n", + "# def update(self, t, i):\n", + "# with brainstate.environ.context(t=t, i=i):\n", + "# spk = self.pre.get_spike() # Get spikes first\n", + "# self.proj(spk) # Update projection\n", + "# self.pre(inp) # Update neurons\n", + "# self.post(0*u.nA)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/quickstart/core-concepts/state-management.ipynb b/docs_state/quickstart/core-concepts/state-management.ipynb new file mode 100644 index 00000000..f017289c --- /dev/null +++ b/docs_state/quickstart/core-concepts/state-management.ipynb @@ -0,0 +1,1599 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# State Management: The Foundation of ``brainpy.state``\n", + "\n", + "State management is the core architectural change in `brainpy.state`. Understanding states is\n", + "essential for using BrainPy effectively. This guide provides comprehensive coverage of the state\n", + "system built on `brainstate`.\n", + "\n", + "**Table of Contents**\n", + "\n", + "## Overview\n", + "\n", + "### What is State?\n", + "\n", + "**State** is any variable that persists across function calls and can change over time. In neural simulations:\n", + "\n", + "- Membrane potentials\n", + "- Synaptic conductances\n", + "- Spike trains\n", + "- Learnable weights\n", + "- Temporary buffers\n", + "\n", + "**Key insight:** `brainpy.state` makes states **explicit** rather than implicit. Every stateful variable is declared and tracked.\n", + "\n", + "### Why Explicit State Management?\n", + "\n", + "**Problems with implicit state (BrainPy 2.x):**\n", + "\n", + "- Hard to track what changes when\n", + "- Difficult to serialize/checkpoint\n", + "- Unclear initialization procedures\n", + "- Conflicts with JAX functional programming\n", + "\n", + "**Benefits of explicit state (`brainpy.state`):**\n", + "\n", + "✅ Clear variable lifecycle\n", + "\n", + "✅ Easy checkpointing and loading\n", + "\n", + "✅ Functional programming compatible\n", + "\n", + "✅ Better debugging and introspection\n", + "\n", + "✅ Automatic differentiation support\n", + "\n", + "✅ Type safety and validation\n", + "\n", + "### The State Hierarchy\n", + "\n", + "BrainPy uses different state types for different purposes:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "State (base class)\n", + "│\n", + "├── ParamState ← Learnable parameters (weights, biases)\n", + "├── ShortTermState ← Temporary dynamics (V, g, spikes)\n", + "└── LongTermState ← Persistent but non-learnable (statistics)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each type has different semantics and handling:\n", + "\n", + "- **ParamState**: Updated by optimizers, saved in checkpoints\n", + "- **ShortTermState**: Reset each trial, not saved\n", + "- **LongTermState**: Saved but not trained\n", + "\n", + "## State Types\n", + "\n", + "### ParamState: Learnable Parameters\n", + "\n", + "**Use for:** Weights, biases, trainable parameters\n", + "\n", + "**Characteristics:**\n", + "\n", + "- Updated by gradient descent\n", + "- Saved in model checkpoints\n", + "- Persistent across trials\n", + "- Registered with optimizers\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate\n", + "import jax.numpy as jnp\n", + "\n", + "class LinearLayer(brainstate.nn.Module):\n", + " def __init__(self, in_size, out_size):\n", + " super().__init__()\n", + "\n", + " # Learnable weight matrix\n", + " self.W = brainstate.ParamState(\n", + " brainstate.random.randn(in_size, out_size) * 0.01\n", + " )\n", + "\n", + " # Learnable bias vector\n", + " self.b = brainstate.ParamState(\n", + " jnp.zeros(out_size)\n", + " )\n", + "\n", + " def update(self, x):\n", + " # Use parameters in computation\n", + " return jnp.dot(x, self.W.value) + self.b.value\n", + "\n", + "# Access all parameters\n", + "layer = LinearLayer(100, 50)\n", + "params = layer.states(brainstate.ParamState)\n", + "# Returns: {'W': ParamState(...), 'b': ParamState(...)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Common uses:**\n", + "\n", + "- Synaptic weights\n", + "- Neural biases\n", + "- Time constants (if learning them)\n", + "- Connectivity matrices (if plastic)\n", + "\n", + "### ShortTermState: Temporary Dynamics\n", + "\n", + "**Use for:** Variables that reset each trial\n", + "\n", + "**Characteristics:**\n", + "\n", + "- Reset at trial start\n", + "- Not saved in checkpoints\n", + "- Represent current dynamics\n", + "- Fastest state type\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainunit as u\n", + "\n", + "class LIFNeuron(brainstate.nn.Module):\n", + " def __init__(self, size):\n", + " super().__init__()\n", + "\n", + " self.size = size\n", + " self.V_rest = -65.0 * u.mV\n", + " self.V_th = -50.0 * u.mV\n", + "\n", + " # Membrane potential (resets each trial)\n", + " self.V = brainstate.ShortTermState(\n", + " jnp.ones(size) * self.V_rest.to_decimal(u.mV)\n", + " )\n", + "\n", + " # Spike indicator (resets each trial)\n", + " self.spike = brainstate.ShortTermState(\n", + " jnp.zeros(size)\n", + " )\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " \"\"\"Called at trial start.\"\"\"\n", + " if batch_size is None:\n", + " self.V.value = jnp.ones(self.size) * self.V_rest.to_decimal(u.mV)\n", + " self.spike.value = jnp.zeros(self.size)\n", + " else:\n", + " self.V.value = jnp.ones((batch_size, self.size)) * self.V_rest.to_decimal(u.mV)\n", + " self.spike.value = jnp.zeros((batch_size, self.size))\n", + "\n", + " def update(self, I):\n", + " # Update membrane potential (simplified example)\n", + " new_V = self.V.value + I.to_decimal(u.mV) * 0.1\n", + " new_spike = (new_V >= self.V_th.to_decimal(u.mV)).astype(float)\n", + " \n", + " self.V.value = new_V\n", + " self.spike.value = new_spike" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Common uses:**\n", + "\n", + "- Membrane potentials\n", + "- Synaptic conductances\n", + "- Spike indicators\n", + "- Refractory counters\n", + "- Temporary buffers\n", + "\n", + "### LongTermState: Persistent Non-Learnable\n", + "\n", + "**Use for:** Statistics, counters, persistent metadata\n", + "\n", + "**Characteristics:**\n", + "\n", + "- Not reset each trial\n", + "- Saved in checkpoints\n", + "- Not updated by optimizers\n", + "- Accumulates over time\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class NeuronWithStatistics(brainstate.nn.Module):\n", + " def __init__(self, size):\n", + " super().__init__()\n", + "\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " # Running spike count (persists across trials)\n", + " self.total_spikes = brainstate.LongTermState(\n", + " jnp.zeros(size, dtype=jnp.int32)\n", + " )\n", + "\n", + " # Running average firing rate\n", + " self.avg_rate = brainstate.LongTermState(\n", + " jnp.zeros(size)\n", + " )\n", + "\n", + " def update(self, I):\n", + " # ... update dynamics ...\n", + " # (Simplified example)\n", + " self.spike.value = (self.V.value > 0).astype(float)\n", + "\n", + " # Accumulate statistics\n", + " self.total_spikes.value += self.spike.value.astype(jnp.int32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Common uses:**\n", + "\n", + "- Spike counters\n", + "- Running averages\n", + "- Homeostatic variables\n", + "- Simulation metadata\n", + "- Custom statistics\n", + "\n", + "## State Initialization\n", + "\n", + "### Automatic Initialization\n", + "\n", + "BrainPy provides `init_all_states()` for automatic initialization.\n", + "\n", + "**Basic usage:**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "MyNetwork(\n", + " neuron=LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[32,100] * mvolt\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import brainstate\n", + "import brainpy as bp\n", + "import brainunit as u\n", + "\n", + "# Define a simple network for demonstration\n", + "class MyNetwork(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.neuron = bp.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " \n", + " def update(self, inp):\n", + " return self.neuron(inp)\n", + "\n", + "# Create network\n", + "net = MyNetwork()\n", + "\n", + "# Initialize all states (single trial)\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "# Initialize with batch dimension\n", + "brainstate.nn.init_all_states(net, batch_size=32)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**What it does:**\n", + "\n", + "1. Finds all modules in the hierarchy\n", + "2. Calls `reset_state()` on each module\n", + "3. Handles nested structures automatically\n", + "4. Sets up batch dimensions if requested\n", + "\n", + "**Example with network:**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EINetwork(\n", + " E=LIF(\n", + " in_size=(800,),\n", + " out_size=(800,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[10,800] * mvolt\n", + " )\n", + " ),\n", + " I=LIF(\n", + " in_size=(200,),\n", + " out_size=(200,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[10,200] * mvolt\n", + " )\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "class EINetwork(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.E = bp.state.LIF(800, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " self.I = bp.state.LIF(200, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " # ... projections ...\n", + "\n", + "net = EINetwork()\n", + "\n", + "# This initializes E, I, and all projections\n", + "brainstate.nn.init_all_states(net, batch_size=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Manual Initialization\n", + "\n", + "For custom initialization, override `reset_state()`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class CustomNeuron(brainstate.nn.Module):\n", + " def __init__(self, size, V_init_range=(-70, -60)):\n", + " super().__init__()\n", + " self.size = size\n", + " self.V_init_range = V_init_range\n", + "\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " \"\"\"Custom initialization: random voltage in range.\"\"\"\n", + "\n", + " # Generate random initial voltages\n", + " low, high = self.V_init_range\n", + " if batch_size is None:\n", + " init_V = brainstate.random.uniform(low, high, size=self.size)\n", + " else:\n", + " init_V = brainstate.random.uniform(low, high, size=(batch_size, self.size))\n", + "\n", + " self.V.value = init_V" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Best practices:**\n", + "\n", + "- Always check `batch_size` parameter\n", + "- Handle both single and batched cases\n", + "- Initialize all ShortTermStates\n", + "- Don't initialize ParamStates (they're learnable)\n", + "- Don't initialize LongTermStates (they persist)\n", + "\n", + "### Initializers for Parameters\n", + "\n", + "Use `braintools.init` for parameter initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import braintools.init as init\n", + "\n", + "class Network(brainstate.nn.Module):\n", + " def __init__(self, in_size, out_size):\n", + " super().__init__()\n", + "\n", + " # Xavier/Glorot initialization\n", + " self.W1 = brainstate.ParamState(\n", + " init.XavierNormal()(shape=(in_size, 100))\n", + " )\n", + "\n", + " # Kaiming/He initialization (for ReLU)\n", + " self.W2 = brainstate.ParamState(\n", + " init.KaimingNormal()(shape=(100, out_size))\n", + " )\n", + "\n", + " # Zero initialization\n", + " self.b = brainstate.ParamState(\n", + " init.Constant(0.0)(shape=(out_size,))\n", + " )\n", + "\n", + " # Orthogonal initialization (for RNNs)\n", + " self.W_rec = brainstate.ParamState(\n", + " init.Orthogonal()(shape=(100, 100))\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Available initializers:**\n", + "\n", + "- `Constant(value)` - Fill with constant\n", + "- `Normal(mean, std)` - Gaussian distribution\n", + "- `Uniform(low, high)` - Uniform distribution\n", + "- `XavierNormal()` - Xavier/Glorot normal\n", + "- `XavierUniform()` - Xavier/Glorot uniform\n", + "- `KaimingNormal()` - He normal (for ReLU)\n", + "- `KaimingUniform()` - He uniform\n", + "- `Orthogonal()` - Orthogonal matrix (for RNNs)\n", + "- `Identity()` - Identity matrix\n", + "\n", + "## State Access and Manipulation\n", + "\n", + "### Reading State Values\n", + "\n", + "Access the current value with `.value`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(100,)\n" + ] + } + ], + "source": [ + "neuron = bp.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "brainstate.nn.init_all_states(neuron)\n", + "\n", + "# Read current membrane potential\n", + "current_V = neuron.V.value\n", + "\n", + "# Read shape\n", + "print(current_V.shape) # (100,)\n", + "\n", + "# Read specific neurons\n", + "V_neuron_0 = neuron.V.value[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Writing State Values\n", + "\n", + "Update state by assigning to `.value`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Set new value (entire array)\n", + "neuron.V.value = jnp.ones(100) * -60.0\n", + "\n", + "# Update subset\n", + "neuron.V.value = neuron.V.value.at[0:10].set(-55.0)\n", + "\n", + "# Increment\n", + "neuron.V.value = neuron.V.value + 0.1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Important:** Always assign to `.value`, not the state object itself!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Example values\n", + "new_V = jnp.ones(100) * -60.0\n", + "\n", + "# CORRECT\n", + "neuron.V.value = new_V" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Collecting States\n", + "\n", + "Get all states of a specific type from a module." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Define a simple network\n", + "example_net = MyNetwork()\n", + "\n", + "# Get all parameters\n", + "params = example_net.states(brainstate.ParamState)\n", + "# Returns: dict with parameter names as keys\n", + "\n", + "# Get all short-term states\n", + "short_term = example_net.states(brainstate.ShortTermState)\n", + "\n", + "# Get all states (any type)\n", + "all_states = example_net.states()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "class SimpleNet(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.W = brainstate.ParamState(jnp.ones((10, 10)))\n", + " self.V = brainstate.ShortTermState(jnp.zeros(10))\n", + "\n", + "net = SimpleNet()\n", + "\n", + "params = net.states(brainstate.ParamState)\n", + "# {'W': ParamState(...)}\n", + "\n", + "states = net.states(brainstate.ShortTermState)\n", + "# {'V': ShortTermState(...)}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## State in Training\n", + "\n", + "### Gradient Computation\n", + "\n", + "Use `brainstate.transform.grad()` to compute gradients w.r.t. parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example data for demonstration\n", + "X = jnp.ones((10, 10)) # 10 samples, 10 features\n", + "y = jnp.ones((10, 10)) # 10 targets\n", + "\n", + "def loss_fn(params, net, X, y):\n", + " \"\"\"Loss function parameterized by params.\"\"\"\n", + " # params is automatically used by net\n", + " output = net(X)\n", + " return jnp.mean((output - y) ** 2)\n", + "\n", + "# Get parameters\n", + "params = net.states(brainstate.ParamState)\n", + "\n", + "# Compute gradients (if parameters exist)\n", + "if len(params) > 0:\n", + " grads = brainstate.transform.grad(loss_fn, params)(net, X, y)\n", + " # grads has same structure as params\n", + " # grads = {'W': gradient_for_W, 'b': gradient_for_b, ...}\n", + "else:\n", + " print(\"No trainable parameters in this network\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Key points:**\n", + "\n", + "- Gradients computed only for ParamState\n", + "- ShortTermState treated as constants\n", + "- Gradient structure matches parameter structure\n", + "\n", + "### Optimizer Updates\n", + "\n", + "Register parameters with optimizer and update." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import braintools\n", + "\n", + "# Create optimizer (use 'lr' not 'learning_rate')\n", + "optimizer = braintools.optim.Adam(lr=1e-3)\n", + "\n", + "# Register trainable parameters\n", + "params = net.states(brainstate.ParamState)\n", + "if len(params) > 0:\n", + " optimizer.register_trainable_weights(params)\n", + "\n", + "# Training loop (example structure)\n", + "for epoch in range(num_epochs):\n", + " for batch in data_loader:\n", + " X, y = batch\n", + "\n", + " # Compute gradients\n", + " grads = brainstate.transform.grad(\n", + " loss_fn,\n", + " params,\n", + " return_value=False\n", + " )(net, X, y)\n", + "\n", + " # Update parameters\n", + " optimizer.update(grads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**The optimizer automatically:**\n", + "\n", + "- Updates all registered parameters\n", + "- Applies learning rate\n", + "- Handles momentum/adaptive rates\n", + "- Maintains optimizer state (momentum buffers, etc.)\n", + "\n", + "### State Persistence\n", + "\n", + "Training doesn't reset ShortTermState between batches (unless you do it manually)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Training with state reset each example\n", + "# (Pseudocode - demonstrates the pattern)\n", + "\n", + "import braintools\n", + "\n", + "# Prepare dummy data\n", + "data_loader = [(jnp.ones(100) * u.nA, jnp.zeros(100)) for _ in range(5)]\n", + "\n", + "# Training loop with state reset each example\n", + "for X, y in data_loader:\n", + " # Reset dynamics for new example\n", + " brainstate.nn.init_all_states(net)\n", + "\n", + " # Forward pass (dynamics evolve)\n", + " output = net(X)\n", + "\n", + " # Backward pass\n", + " params = net.states(brainstate.ParamState)\n", + " if len(params) > 0:\n", + " grads = brainstate.transform.grad(loss_fn, params)(net, X, y)\n", + " optimizer.update(grads)\n", + "\n", + "# Training with persistent state (e.g., RNN)\n", + "for X, y in data_loader:\n", + " # Don't reset - state carries over\n", + " output = net(X)\n", + " params = net.states(brainstate.ParamState)\n", + " if len(params) > 0:\n", + " grads = brainstate.transform.grad(loss_fn, params)(net, X, y)\n", + " optimizer.update(grads)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batching\n", + "\n", + "### Batch Dimensions\n", + "\n", + "States can have a batch dimension for parallel trials.\n", + "\n", + "**Single trial:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neuron = bp.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "brainstate.nn.init_all_states(neuron)\n", + "# neuron.V.value.shape = (100,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Batched trials:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LIF(\n", + " in_size=(100,),\n", + " out_size=(100,),\n", + " spk_reset=soft,\n", + " spk_fun=ReluGrad(alpha=0.3, width=1.0),\n", + " R=1. * ohm,\n", + " tau=10 * msecond,\n", + " V_th=-50 * mvolt,\n", + " V_rest=-65 * mvolt,\n", + " V_reset=0. * mvolt,\n", + " V_initializer=Constant(value=0.0 * mvolt),\n", + " V=HiddenState(\n", + " value=~float32[32,100] * mvolt\n", + " )\n", + ")" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "neuron = bp.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "brainstate.nn.init_all_states(neuron, batch_size=32)\n", + "# neuron.V.value.shape = (32, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Usage:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Input also needs batch dimension\n", + "inp = brainstate.random.rand(32, 100) * 2.0 * u.nA\n", + "\n", + "# Update operates on all batches in parallel\n", + "neuron(inp)\n", + "\n", + "# Output has batch dimension\n", + "spikes = neuron.get_spike() # shape: (32, 100)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Benefits of Batching\n", + "\n", + "**1. Parallelism:** GPU processes all batches simultaneously\n", + "\n", + "**2. Statistical averaging:** Reduce noise in gradients\n", + "\n", + "**3. Exploration:** Try different initial conditions\n", + "\n", + "**4. Efficiency:** Amortize compilation cost\n", + "\n", + "**Example: Parameter sweep with batching**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Test 10 different input currents in parallel\n", + "batch_size = 10\n", + "neuron_batched = bp.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "brainstate.nn.init_all_states(neuron_batched, batch_size=batch_size)\n", + "\n", + "# Different input for each batch\n", + "currents = jnp.linspace(0, 5, batch_size).reshape(-1, 1) * u.nA\n", + "inp_batched = jnp.broadcast_to(currents, (batch_size, 100))\n", + "\n", + "# Simulate (example - shortened for demonstration)\n", + "# for _ in range(1000):\n", + "# neuron_batched(inp_batched)\n", + "\n", + "# Analyze each trial separately\n", + "# spike_counts = jnp.sum(neuron_batched.spike.value, axis=1) # (10,)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checkpointing and Serialization\n", + "\n", + "### Saving Models\n", + "\n", + "Save model state to disk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "\n", + "# Example: Saving checkpoint (pseudocode)\n", + "current_epoch = 10 # Example epoch number\n", + "# \n", + "# # Get all states to save\n", + "state_dict = {\n", + " 'params': net.states(brainstate.ParamState),\n", + " 'long_term': net.states(brainstate.LongTermState),\n", + " 'epoch': current_epoch,\n", + " 'optimizer_state': optimizer.state_dict() # If applicable\n", + "}\n", + "# \n", + "# Save to file\n", + "with open('checkpoint.pkl', 'wb') as f:\n", + " pickle.dump(state_dict, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note:** Don't save ShortTermState (it resets each trial).\n", + "\n", + "### Loading Models\n", + "\n", + "Restore model state from disk." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Loading checkpoint\n", + "with open('checkpoint.pkl', 'rb') as f:\n", + " state_dict = pickle.load(f)\n", + "\n", + "# Create fresh model\n", + "net = MyNetwork()\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "# Restore parameters\n", + "params = state_dict['params']\n", + "for name, param_state in params.items():\n", + " # Find corresponding parameter in net and copy value\n", + " net_params = net.states(brainstate.ParamState)\n", + " if name in net_params:\n", + " net_params[name].value = param_state.value\n", + "\n", + "# Restore long-term states similarly\n", + "\n", + "# Restore optimizer if continuing training\n", + "optimizer.load_state_dict(state_dict['optimizer_state'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Best Practices for Checkpointing\n", + "\n", + "**1. Save regularly during training**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "epoch = 10\n", + "save_interval = 5\n", + "\n", + "if epoch % save_interval == 0:\n", + " save_checkpoint(net, optimizer, epoch, path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. Keep multiple checkpoints**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "epoch = 10\n", + "save_path = f'checkpoint_epoch_{epoch}.pkl'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3. Save best model separately**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "val_loss = 0.5\n", + "best_val_loss = 1.0\n", + "\n", + "if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " save_checkpoint(net, optimizer, epoch, 'best_model.pkl')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**4. Include metadata**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "state_dict = {\n", + " 'params': net.states(brainstate.ParamState),\n", + " 'epoch': 10,\n", + " 'best_val_loss': 0.5,\n", + " 'config': {'lr': 1e-3, 'batch_size': 32}, # Hyperparameters\n", + " 'timestamp': datetime.now()\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Common Patterns\n", + "\n", + "### Pattern 1: Resetting Between Trials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Simulate multiple trials (pseudocode)\n", + "num_trials = 10\n", + "trial_length = 100\n", + "\n", + "def get_input(trial, t):\n", + " \"\"\"Generate input for given trial and time.\"\"\"\n", + " return jnp.ones(100) * 5.0 * u.nA\n", + "\n", + "def record(output):\n", + " \"\"\"Record output for analysis.\"\"\"\n", + " pass\n", + "\n", + "# Simulate multiple trials\n", + "for trial in range(num_trials):\n", + " # Reset dynamics\n", + " brainstate.nn.init_all_states(net)\n", + "\n", + " # Run trial\n", + " for t in range(trial_length):\n", + " inp = get_input(trial, t)\n", + " output = net(inp)\n", + " record(output)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pattern 2: Accumulating Statistics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class NeuronWithStats(brainstate.nn.Module):\n", + " def __init__(self, size):\n", + " super().__init__()\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " # Accumulate across trials\n", + " self.total_spikes = brainstate.LongTermState(\n", + " jnp.zeros(size, dtype=jnp.int32)\n", + " )\n", + " self.n_steps = brainstate.LongTermState(0)\n", + "\n", + " def update(self, I):\n", + " # ... dynamics ...\n", + "\n", + " # Accumulate\n", + " self.total_spikes.value += self.spike.value.astype(jnp.int32)\n", + " self.n_steps.value += 1\n", + "\n", + " def get_firing_rate(self):\n", + " \"\"\"Average firing rate across all trials.\"\"\"\n", + " dt = brainstate.environ.get_dt()\n", + " total_time = self.n_steps.value * dt.to_decimal(u.second)\n", + " return self.total_spikes.value / total_time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pattern 3: Conditional Updates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class AdaptiveNeuron(brainstate.nn.Module):\n", + " def __init__(self, size):\n", + " super().__init__()\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.threshold = brainstate.ParamState(jnp.ones(size) * (-50.0))\n", + "\n", + " def update(self, I):\n", + " # Dynamics (simplified)\n", + " self.spike.value = (self.V.value > self.threshold.value).astype(float)\n", + "\n", + " # Homeostatic threshold adaptation\n", + " # Simplified spike rate computation\n", + " spike_rate = jnp.mean(self.spike.value) * 1000.0 # Assume dt=1ms\n", + "\n", + " # Adjust threshold based on activity\n", + " target_rate = 5.0 # Hz\n", + " adjustment = 0.01 * (spike_rate - target_rate)\n", + "\n", + " # Update learnable threshold\n", + " self.threshold.value -= adjustment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pattern 4: Hierarchical States" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class MyLayer(brainstate.nn.Module):\n", + " def __init__(self, in_size, out_size):\n", + " super().__init__()\n", + " self.W = brainstate.ParamState(jnp.ones((in_size, out_size)) * 0.01)\n", + " \n", + " def update(self, x):\n", + " return jnp.dot(x, self.W.value)\n", + "\n", + "class HierarchicalNetwork(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " # Submodules have their own states\n", + " self.layer1 = MyLayer(100, 50)\n", + " self.layer2 = MyLayer(50, 10)\n", + "\n", + " def update(self, x):\n", + " # Each layer manages its own states\n", + " h1 = self.layer1(x)\n", + " h2 = self.layer2(h1)\n", + " return h2\n", + "\n", + "net = HierarchicalNetwork()\n", + "\n", + "# Collect ALL states from hierarchy\n", + "all_params = net.states(brainstate.ParamState)\n", + "# Includes params from layer1 AND layer2\n", + "\n", + "# Initialize ALL states in hierarchy\n", + "brainstate.nn.init_all_states(net)\n", + "# Calls reset_state() on net, layer1, and layer2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Topics\n", + "\n", + "### Custom State Types\n", + "\n", + "Create custom state types for specialized needs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class RandomState(brainstate.State):\n", + " \"\"\"State that re-randomizes on reset.\"\"\"\n", + "\n", + " def __init__(self, shape, low=0.0, high=1.0):\n", + " super().__init__(jnp.zeros(shape))\n", + " self.shape = shape\n", + " self.low = low\n", + " self.high = high\n", + "\n", + " def reset(self):\n", + " \"\"\"Re-randomize on reset.\"\"\"\n", + " self.value = brainstate.random.uniform(\n", + " self.low, self.high, size=self.shape\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### State Sharing\n", + "\n", + "Share state between modules (use with caution)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class ModuleA(brainstate.nn.Module):\n", + " def __init__(self, shared_W):\n", + " super().__init__()\n", + " self.W = shared_W\n", + "\n", + "class ModuleB(brainstate.nn.Module):\n", + " def __init__(self, shared_W):\n", + " super().__init__()\n", + " self.W = shared_W\n", + "\n", + "class SharedState(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " # Shared weight matrix\n", + " shared_W = brainstate.ParamState(jnp.ones((100, 100)))\n", + " self.module1 = ModuleA(shared_W)\n", + " self.module2 = ModuleB(shared_W)\n", + " # module1 and module2 both modify the same weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**When to use:** Siamese networks, weight tying, parameter sharing\n", + "\n", + "**Caution:** Makes dependencies implicit, harder to debug\n", + "\n", + "### State Inspection\n", + "\n", + "Debug by inspecting state values." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Print all parameter shapes\n", + "params = net.states(brainstate.ParamState)\n", + "for name, state in params.items():\n", + " print(f\"{name}: {state.value.shape}\")\n", + "\n", + "# Check for NaN values\n", + "for name, state in params.items():\n", + " if jnp.any(jnp.isnan(state.value)):\n", + " print(f\"NaN detected in {name}!\")\n", + "\n", + "# Compute statistics\n", + "V_values = neuron.V.value\n", + "print(f\"V range: [{V_values.min():.2f}, {V_values.max():.2f}]\")\n", + "print(f\"V mean: {V_values.mean():.2f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Troubleshooting\n", + "\n", + "### Issue: States not updating\n", + "\n", + "**Symptoms:** Values stay constant\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Assign to `.value`, not the state itself\n", + "2. Check you're updating the right variable\n", + "3. Verify update function is called" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example of correct vs wrong state assignment\n", + "example_V = jnp.ones(100) * -60.0\n", + "\n", + "# WRONG - Creates new object, doesn't update state!\n", + "self.V = example_V\n", + "\n", + "# CORRECT - Updates state value\n", + "self.V.value = example_V" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue: Batch dimension errors\n", + "\n", + "**Symptoms:** Shape mismatch errors\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Initialize with `batch_size` parameter\n", + "2. Ensure inputs have batch dimension\n", + "3. Check `reset_state()` handles batching" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize with batching\n", + "brainstate.nn.init_all_states(net, batch_size=32)\n", + "\n", + "# Input needs batch dimension\n", + "inp = jnp.zeros((32, 100)) # (batch, neurons)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue: Gradients are None\n", + "\n", + "**Symptoms:** No gradients for parameters\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Ensure parameters are `ParamState`\n", + "2. Check parameters are used in loss computation\n", + "3. Verify gradient function call" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Ensure parameters are ParamState\n", + "init_W = jnp.ones((100, 50)) * 0.01\n", + "\n", + "# Parameters must be ParamState\n", + "self.W = brainstate.ParamState(init_W) # Correct\n", + "\n", + "# Compute gradients for parameters only\n", + "params = net.states(brainstate.ParamState)\n", + "grads = brainstate.transform.grad(loss_fn, params)(...)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue: Memory leak during training\n", + "\n", + "**Symptoms:** Memory grows over time\n", + "\n", + "**Solutions:**\n", + "\n", + "1. Don't accumulate history in Python lists\n", + "2. Clear unnecessary references\n", + "3. Use `jnp.array` operations (not Python append)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Avoid memory leaks\n", + "inp = jnp.ones(100) * 5.0 * u.nA\n", + "\n", + "# BAD - accumulates in Python memory\n", + "history = []\n", + "for t in range(10000):\n", + " output = net(inp)\n", + " history.append(output) # Memory leak!\n", + "\n", + "# GOOD - use fixed-size buffer or don't store\n", + "for t in range(10000):\n", + " output = net(inp)\n", + "# # Process immediately, don't store" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further Reading\n", + "\n", + "- architecture - Overall BrainPy architecture\n", + "- neurons - Neuron models and their states\n", + "- synapses - Synapse models and their states\n", + "- ../tutorials/advanced/05-snn-training - Training with states\n", + "- BrainState documentation: https://brainstate.readthedocs.io/\n", + "\n", + "## Summary\n", + "\n", + "**Key takeaways:**\n", + "\n", + "✅ **Three state types:**\n", + " - `ParamState`: Learnable parameters\n", + " - `ShortTermState`: Temporary dynamics\n", + " - `LongTermState`: Persistent statistics\n", + "\n", + "✅ **Initialization:**\n", + " - Use `brainstate.nn.init_all_states(module)`\n", + " - Implement `reset_state()` for custom logic\n", + " - Handle batch dimensions\n", + "\n", + "✅ **Access:**\n", + " - Read/write with `.value`\n", + " - Collect with `.states(StateType)`\n", + " - Never assign to state object directly\n", + "\n", + "✅ **Training:**\n", + " - Gradients computed for `ParamState`\n", + " - Register with optimizer\n", + " - Update with `optimizer.update(grads)`\n", + "\n", + "✅ **Checkpointing:**\n", + " - Save `ParamState` and `LongTermState`\n", + " - Don't save `ShortTermState`\n", + " - Include metadata and optimizer state\n", + "\n", + "**Quick reference:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quick reference example (pseudocode)\n", + "\n", + "# Define example initializers\n", + "init_W = jnp.ones((100, 50)) * 0.01\n", + "init_V = jnp.zeros(100)\n", + "init_c = jnp.zeros(100, dtype=jnp.int32)\n", + "\n", + "# Define states\n", + "class MyModule(brainstate.nn.Module):\n", + " def __init__(self, size=100):\n", + " super().__init__()\n", + " self.size = size\n", + " self.W = brainstate.ParamState(init_W) # Learnable\n", + " self.V = brainstate.ShortTermState(init_V) # Resets\n", + " self.count = brainstate.LongTermState(init_c) # Persists\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " \"\"\"Initialize ShortTermState.\"\"\"\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.V.value = jnp.zeros(shape)\n", + "\n", + "# Initialize\n", + "module = MyModule()\n", + "brainstate.nn.init_all_states(module, batch_size=32)\n", + "\n", + "# Access\n", + "params = module.states(brainstate.ParamState)\n", + "new_V = jnp.ones(100) * -60.0\n", + "module.V.value = new_V\n", + "\n", + "# Train\n", + "def loss(params, module, X, y):\n", + " return jnp.mean((module.update(X) - y) ** 2)\n", + "grads = brainstate.transform.grad(loss, params)(module, X, y)\n", + "# optimizer.update(grads)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/quickstart/core-concepts/synapses.ipynb b/docs_state/quickstart/core-concepts/synapses.ipynb new file mode 100644 index 00000000..4a77ef5e --- /dev/null +++ b/docs_state/quickstart/core-concepts/synapses.ipynb @@ -0,0 +1,938 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Synapses\n", + "\n", + "Synapses model the temporal dynamics of neural connections in `brainpy.state`. This document explains\n", + "how synapses work, what models are available, and how to use them effectively.\n", + "\n", + "## Overview\n", + "\n", + "Synapses provide temporal filtering of spike trains, transforming discrete spikes into continuous currents or conductances. They model:\n", + "\n", + "- **Postsynaptic potentials** (PSPs)\n", + "- **Temporal integration** of spike trains\n", + "- **Synaptic dynamics** (rise and decay)\n", + "\n", + "In BrainPy's architecture, synapses are part of the projection system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Spikes → [Connectivity] → [Synapse] → [Output] → Neurons\n", + " ↑\n", + " Temporal filtering" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Usage\n", + "\n", + "### Creating Synapses\n", + "\n", + "Synapses are typically created as part of projections:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import braintools\n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "# Create neurons for demonstration\n", + "neurons = brainpy.state.LIF(50, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Create synapse descriptor\n", + "syn = brainpy.state.Expon.desc(\n", + " in_size=100, # Number of synapses\n", + " tau=5. * u.ms # Time constant\n", + ")\n", + "\n", + "# Use in projection\n", + "projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 50, 0.1, 0.5),\n", + " syn=syn, # Synapse here\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=neurons\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Synapse Lifecycle\n", + "\n", + "1. **Creation**: Define synapse with `.desc()` method\n", + "2. **Integration**: Include in projection\n", + "3. **Update**: Called automatically by projection\n", + "4. **Access**: Read synaptic variables as needed" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import jax.numpy as jnp\n", + "\n", + "# Example presynaptic spikes\n", + "presynaptic_spikes = jnp.zeros(100) # 100 presynaptic neurons\n", + "\n", + "projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.AllToAll(100, 100, 0.1,0.5),\n", + " syn=brainpy.state.Expon.desc(100, tau=5.0),\n", + " out=brainpy.state.COBA.desc(E=0),\n", + " post=neurons,\n", + ")\n", + "# During simulation\n", + "projection(presynaptic_spikes) # Updates synapse internally\n", + "\n", + "\n", + "# Access synaptic variable\n", + "synaptic_current = projection.syn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Available Synapse Models\n", + "\n", + "### Expon (Single Exponential)\n", + "\n", + "The simplest and most commonly used synapse model.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "\n", + "$$\n", + "\\tau \\frac{dg}{dt} = -g\n", + "$$\n", + "When spike arrives: \n", + "$g \\leftarrow g + 1$\n", + "\n", + "**Impulse Response:**\n", + "\n", + "\n", + "$$\n", + "g(t) = \\exp(-t/\\tau)\n", + "$$\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "syn = brainpy.state.Expon.desc(\n", + " size=100,\n", + " tau=5. * u.ms,\n", + " g_initializer=braintools.init.Constant(0. * u.mS)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parameters:**\n", + "\n", + "- `size`: Number of synapses\n", + "- `tau`: Decay time constant\n", + "- `g_initializer`: Initial synaptic variable (optional)\n", + "\n", + "**Key Features:**\n", + "\n", + "- Single time constant\n", + "- Fast computation\n", + "- Instantaneous rise\n", + "\n", + "**Use cases:**\n", + "\n", + "- General-purpose modeling\n", + "- Fast simulations\n", + "- When precise kinetics are not critical\n", + "\n", + "**Behavior:**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Response to single spike at t=0\n", + "# g(t) = exp(-t/τ)\n", + "# Fast rise, exponential decay" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Alpha Synapse\n", + "\n", + "A more realistic model with non-instantaneous rise time.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\tau \\frac{dh}{dt} &= -h\n", + "\\tau \\frac{dg}{dt} &= -g + h\n", + "\\end{aligned}\n", + "$$\n", + "When spike arrives: $ h\\leftarrow h + 1 $\n", + "\n", + "**Impulse Response:**\n", + "\n", + "\n", + "$$\n", + "g(t) = \\frac{t}{\\tau}\\exp(-t/\\tau)\n", + "$$\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "syn = brainpy.state.Alpha.desc(\n", + " size=100,\n", + " tau=5. * u.ms,\n", + " g_initializer=braintools.init.Constant(0. * u.mS)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Parameters:**\n", + "\n", + "Same as Expon, but produces alpha-shaped response.\n", + "\n", + "**Key Features:**\n", + "\n", + "- Smooth rise and fall\n", + "- Biologically realistic\n", + "- Peak at t = τ\n", + "\n", + "**Use cases:**\n", + "\n", + "- Biological realism\n", + "- Detailed cortical modeling\n", + "- When kinetics matter\n", + "\n", + "**Behavior:**" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [], + "source": [ + "# Response to single spike at t=0\n", + "# g(t) = (t/τ) * exp(-t/τ)\n", + "# Gradual rise to peak at τ, then decay" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### AMPA (Excitatory)\n", + "\n", + "Models AMPA receptor dynamics for excitatory synapses.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "Similar to Alpha, but with parameters tuned for AMPA receptors.\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "syn = brainpy.state.AMPA.desc(\n", + " size=100,\n", + " tau=2. * u.ms, # Fast AMPA kinetics\n", + " g_initializer=braintools.init.Constant(0. * u.mS)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Key Features:**\n", + "\n", + "- Fast kinetics (τ ≈ 2 ms)\n", + "- Excitatory receptor\n", + "- Biologically parameterized\n", + "\n", + "**Use cases:**\n", + "\n", + "- Excitatory synapses\n", + "- Cortical pyramidal neurons\n", + "- Biological realism\n", + "\n", + "### GABAa (Inhibitory)\n", + "\n", + "Models GABAa receptor dynamics for inhibitory synapses.\n", + "\n", + "**Mathematical Model:**\n", + "\n", + "Similar to Alpha, but with parameters tuned for GABAa receptors.\n", + "\n", + "**Example:**" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "syn = brainpy.state.GABAa.desc(\n", + " size=100,\n", + " tau=10. * u.ms, # Slower GABAa kinetics\n", + " g_initializer=braintools.init.Constant(0. * u.mS)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Key Features:**\n", + "\n", + "- Slower kinetics (τ ≈ 10 ms)\n", + "- Inhibitory receptor\n", + "- Biologically parameterized\n", + "\n", + "**Use cases:**\n", + "\n", + "- Inhibitory synapses\n", + "- GABAergic interneurons\n", + "- Biological realism\n", + "\n", + "## Synaptic Variables\n", + "\n", + "### The Descriptor Pattern\n", + "\n", + "BrainPy synapses use a descriptor pattern:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Example: Descriptor pattern\n", + "\n", + "# Create descriptor (not yet instantiated)\n", + "syn_desc = brainpy.state.Expon.desc(in_size=100, tau=5*u.ms)\n", + "\n", + "# Define neurons for example\n", + "example_neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Instantiated within projection\n", + "example_projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 100, 0.1, 0.5),\n", + " syn=syn_desc,\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=example_neurons\n", + ")\n", + "\n", + "# Access instantiated synapse\n", + "actual_synapse = example_projection.syn\n", + "g_value = actual_synapse" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Why Descriptors?\n", + "\n", + "- **Deferred instantiation**: Created when needed\n", + "- **Reusability**: Same descriptor for multiple projections\n", + "- **Flexibility**: Configure before instantiation\n", + "\n", + "### Accessing Synaptic State" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Define neurons for this example\n", + "demo_neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Within projection\n", + "demo_projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 100, conn_num=10, conn_weight=0.5),\n", + " syn=brainpy.state.Expon.desc(in_size=100, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=demo_neurons\n", + ")\n", + "\n", + "# Initialize states\n", + "brainstate.nn.init_all_states(demo_projection)\n", + "\n", + "# Access the synaptic conductance state\n", + "synaptic_var = demo_projection.syn.g.value # Current value with units\n", + "\n", + "# Convert to array for plotting\n", + "g_array = u.get_magnitude(synaptic_var)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Synaptic Dynamics Visualization\n", + "\n", + "### Comparing Different Models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainstate\n", + "import brainunit as u\n", + "import matplotlib.pyplot as plt\n", + "import jax.numpy as jnp\n", + "\n", + "# Set simulation timestep\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Create different synapses (without unit initializers to avoid mismatch)\n", + "expon = brainpy.state.Expon(100, tau=5*u.ms)\n", + "alpha = brainpy.state.Alpha(100, tau=5*u.ms)\n", + "ampa = brainpy.state.AMPA(100, T=2*u.ms)\n", + "gaba = brainpy.state.GABAa(100, T=10*u.ms)\n", + "\n", + "# Initialize\n", + "for syn in [expon, alpha, ampa, gaba]:\n", + " brainstate.nn.init_all_states(syn)\n", + "\n", + "# Single spike at t=0 (dimensionless spike count)\n", + "spike_input = jnp.zeros(100)\n", + "spike_input = spike_input.at[0].set(1.0)\n", + "\n", + "# Simulate\n", + "times = u.math.arange(0*u.ms, 50*u.ms, 0.1*u.ms)\n", + "responses = {\n", + " 'Expon': [],\n", + " 'Alpha': [],\n", + " 'AMPA': [],\n", + " 'GABAa': []\n", + "}\n", + "\n", + "for syn, name in zip([expon, alpha, ampa, gaba],\n", + " ['Expon', 'Alpha', 'AMPA', 'GABAa']):\n", + " # Re-initialize for clean start\n", + " brainstate.nn.init_all_states(syn)\n", + " for i, t in enumerate(times):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " if i == 0:\n", + " syn(spike_input)\n", + " else:\n", + " syn(jnp.zeros(100))\n", + " # Get the value (may have units or not)\n", + " g_val = syn.g.value[0]\n", + " if hasattr(g_val, 'magnitude'):\n", + " responses[name].append(float(u.get_magnitude(g_val)))\n", + " else:\n", + " responses[name].append(float(g_val))\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(10, 6))\n", + "for name, response in responses.items():\n", + " response_array = jnp.array(response)\n", + " plt.plot(u.get_magnitude(times),\n", + " response_array,\n", + " label=name, linewidth=2)\n", + "\n", + "plt.xlabel('Time (ms)')\n", + "plt.ylabel('Synaptic Variable (normalized)')\n", + "plt.title('Comparison of Synapse Models (Single Spike)')\n", + "plt.legend()\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Integration with Projections\n", + "\n", + "### Complete Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy \n", + "import brainstate\n", + "import brainunit as u\n", + "\n", + "# Set simulation timestep\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Create neurons\n", + "pre_neurons = brainpy.state.LIF(80, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "post_neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Create projection with exponential synapse\n", + "projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(\n", + " 80, 100, 0.1, 0.5\n", + " ),\n", + " syn=brainpy.state.Expon.desc(100, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post_neurons\n", + ")\n", + "\n", + "# Initialize all states\n", + "brainstate.nn.init_all_states([pre_neurons, post_neurons, projection])\n", + "\n", + "# Simulation function\n", + "def update(t, i, input_current):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " # Update presynaptic neurons\n", + " pre_neurons(input_current)\n", + "\n", + " # Get spikes and propagate through projection\n", + " spikes = pre_neurons.get_spike()\n", + " projection(spikes)\n", + "\n", + " # Update postsynaptic neurons\n", + " post_neurons(0 * u.nA)\n", + "\n", + " return post_neurons.get_spike()\n", + "\n", + "# Run simulation\n", + "times = u.math.arange(0*u.ms, 100*u.ms, 0.1*u.ms)\n", + "indices = u.math.arange(times.size)\n", + "\n", + "# Create input current for all timesteps\n", + "input_currents = jnp.ones(80) * 2*u.nA\n", + "\n", + "results = brainstate.transform.for_loop(\n", + " lambda t, i: update(t, i, input_currents),\n", + " times,\n", + " indices\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Short-Term Plasticity\n", + "\n", + "Synapses can be combined with short-term plasticity (STP):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Short-term plasticity (STP) can be combined with synapses\n", + "Note: STP is typically implemented as part of custom synapse models\n", + "or as a separate plasticity mechanism in the projection\n", + "\n", + "Example conceptual usage (not directly supported in current API):\n", + "The idea is to modulate synaptic efficacy based on recent activity\n", + "- Facilitation: increases with repeated activation\n", + "- Depression: decreases with repeated activation\n", + "\n", + "For implementing STP, you would typically:\n", + "1. Create a custom synapse class that includes STP dynamics\n", + "2. Or use a separate plasticity module that modulates connection weights\n", + "\n", + "See the plasticity documentation for more details on implementing\n", + "short-term plasticity mechanisms" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See plasticity for more details on STP.\n", + "\n", + "## Custom Synapses\n", + "\n", + "### Creating Custom Synapse Models\n", + "\n", + "You can create custom synapse models by inheriting from `Synapse`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate\n", + "import braintools\n", + "from brainpy.state import AlignPost\n", + "\n", + "class MyCustomSynapse(AlignPost):\n", + " \"\"\"Custom synapse with double exponential dynamics.\"\"\"\n", + " \n", + " def __init__(self, size, tau1, tau2, g_initializer=None, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + "\n", + " self.tau1 = tau1\n", + " self.tau2 = tau2\n", + "\n", + " # Synaptic variable - initialize without units for compatibility\n", + " if g_initializer is None:\n", + " g_initializer = braintools.init.Constant(0.)\n", + " self.g = brainstate.ShortTermState(g_initializer(size))\n", + "\n", + " def update(self, x):\n", + " # Get time step\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Custom dynamics: simple decay\n", + " # dg/dt = -g/tau1\n", + " decay = -self.g.value / self.tau1\n", + " self.g.value = self.g.value + decay * dt\n", + " \n", + " # Add spike input if provided\n", + " if x is not None:\n", + " self.g.value = self.g.value + x / self.tau2\n", + "\n", + " return self.g.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usage:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define neurons for custom synapse example\n", + "custom_neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Create custom synapse descriptor using .desc() method\n", + "syn_desc = MyCustomSynapse.desc(\n", + " size=100,\n", + " tau1=5*u.ms,\n", + " tau2=10*u.ms\n", + ")\n", + "\n", + "# Use in projection\n", + "custom_projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 100, 0.1, 0.5),\n", + " syn=syn_desc,\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=custom_neurons\n", + ")\n", + "\n", + "print(\"Custom synapse projection created successfully!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Choosing the Right Synapse\n", + "\n", + "### Decision Guide\n", + "\n", + "\n", + " * - Model\n", + " - When to Use\n", + " - Pros\n", + " - Cons\n", + " * - Expon\n", + " - General purpose, speed\n", + " - Fast, simple\n", + " - Unrealistic rise\n", + " * - Alpha\n", + " - Biological realism\n", + " - Realistic kinetics\n", + " - Slower computation\n", + " * - AMPA\n", + " - Excitatory, fast\n", + " - Biologically accurate\n", + " - Specific use case\n", + " * - GABAa\n", + " - Inhibitory, slow\n", + " - Biologically accurate\n", + " - Specific use case\n", + "\n", + "### Recommendations\n", + "\n", + "**For machine learning / SNNs:**\n", + " Use `Expon` for speed and simplicity.\n", + "\n", + "**For biological modeling:**\n", + " Use `Alpha`, `AMPA`, or `GABAa` for realism.\n", + "\n", + "**For cortical networks:**\n", + " - Excitatory: `AMPA` (τ ≈ 2 ms)\n", + " - Inhibitory: `GABAa` (τ ≈ 10 ms)\n", + "\n", + "**For custom dynamics:**\n", + " Implement custom synapse class.\n", + "\n", + "## Performance Considerations\n", + "\n", + "### Computational Cost\n", + "\n", + "\n", + " * - Model\n", + " - Relative Cost\n", + " - Notes\n", + " * - Expon\n", + " - 1x (baseline)\n", + " - Single state variable\n", + " * - Alpha\n", + " - 2x\n", + " - Two state variables\n", + " * - AMPA/GABAa\n", + " - 2x\n", + " - Similar to Alpha\n", + "\n", + "### Optimization Tips\n", + "\n", + "1. **Use Expon when possible**: Fastest option\n", + "\n", + "2. **Batch operations**: Multiple synapses together" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define neurons for batch optimization example\n", + "batch_neurons = brainpy.state.LIF(1000, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Good: Single projection with 1000 synapses\n", + "good_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(1000, 1000, 0.1, 0.5),\n", + " syn=brainpy.state.Expon.desc(1000, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=batch_neurons\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **JIT compilation**: Always use for simulations" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "# Define neurons and projection for JIT example\n", + "jit_neurons = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "jit_projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 100, 0.1, 0.5),\n", + " syn=brainpy.state.Expon.desc(100, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=jit_neurons\n", + ")\n", + "\n", + "# Initialize\n", + "brainstate.nn.init_all_states([jit_neurons, jit_projection])\n", + "\n", + "# Example spikes\n", + "spikes = jnp.zeros(100)\n", + "\n", + "@brainstate.transform.jit\n", + "def step():\n", + " jit_projection(spikes)\n", + " jit_neurons(0*u.nA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Common Patterns\n", + "\n", + "### Excitatory-Inhibitory Balance" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "# Define neurons and sizes for E-I balance example\n", + "post_size = 100\n", + "ei_neurons = brainpy.state.LIF(post_size, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Excitatory projection (fast)\n", + "E_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(80, post_size, 0.1, 0.5),\n", + " syn=brainpy.state.Expon.desc(post_size, tau=2*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=ei_neurons\n", + ")\n", + "\n", + "# Inhibitory projection (slow)\n", + "I_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(20, post_size, 0.1, 0.5),\n", + " syn=brainpy.state.Expon.desc(post_size, tau=10*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=ei_neurons\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multiple Receptor Types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define neurons and size for multiple receptor example\n", + "receptor_size = 100\n", + "receptor_neurons = brainpy.state.LIF(receptor_size, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "# Fast excitatory (AMPA-like) - using Expon with fast time constant\n", + "ampa_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(80, receptor_size, 0.1, 0.3),\n", + " syn=brainpy.state.Expon.desc(receptor_size, tau=2*u.ms), # Fast AMPA kinetics\n", + " out=brainpy.state.COBA.desc(E=0*u.mV),\n", + " post=receptor_neurons\n", + ")\n", + "\n", + "# Slow excitatory (NMDA-like) - using Expon with slow time constant\n", + "nmda_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(80, receptor_size, 0.1, 0.3),\n", + " syn=brainpy.state.Expon.desc(receptor_size, tau=100*u.ms), # Slow NMDA kinetics\n", + " out=brainpy.state.COBA.desc(E=0*u.mV),\n", + " post=receptor_neurons\n", + ")\n", + "\n", + "# Fast inhibitory (GABAa-like) - using Expon with medium time constant\n", + "gaba_proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(20, receptor_size, 0.1, 0.5),\n", + " syn=brainpy.state.Expon.desc(receptor_size, tau=10*u.ms), # GABAa kinetics\n", + " out=brainpy.state.COBA.desc(E=-80*u.mV),\n", + " post=receptor_neurons\n", + ")\n", + "\n", + "print(\"Multiple receptor type projections created successfully!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "Synapses in `brainpy.state`:\n", + "\n", + "✅ **Multiple models**: Expon, Alpha, AMPA, GABAa\n", + "\n", + "✅ **Temporal filtering**: Convert spikes to continuous signals\n", + "\n", + "✅ **Descriptor pattern**: Flexible, reusable configuration\n", + "\n", + "✅ **Integration ready**: Seamless use in projections\n", + "\n", + "✅ **Extensible**: Easy custom synapse models\n", + "\n", + "✅ **Physical units**: Proper unit handling throughout\n", + "\n", + "## Next Steps\n", + "\n", + "- Learn about [projections](projections) for complete connectivity\n", + "- Explore [plasticity](state-management) for learning rules\n", + "- Follow [tutorials](../tutorials/index) for practice\n", + "- See [examples](../examples/gallery) for network examples" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/quickstart/index.rst b/docs_state/quickstart/index.rst index e3715ada..7177bc3a 100644 --- a/docs_state/quickstart/index.rst +++ b/docs_state/quickstart/index.rst @@ -25,16 +25,15 @@ If you're new to ``brainpy.state``, we recommend following these steps: 1. Start with :doc:`installation` to set up your environment 2. Follow the :doc:`5min-tutorial` to build your first model -3. Review :doc:`concepts-overview` to understand the underlying principles +3. Review :doc:`core-concepts/index` to understand the underlying principles For experienced users, you can jump directly to the concepts overview or explore the tutorials and examples sections. .. toctree:: :hidden: - :maxdepth: 1 + :maxdepth: 2 installation.rst 5min-tutorial.ipynb - concepts-overview.rst - + core-concepts/index \ No newline at end of file diff --git a/docs_state/quickstart/installation.rst b/docs_state/quickstart/installation.rst index ee72f6d0..04fa6fe5 100644 --- a/docs_state/quickstart/installation.rst +++ b/docs_state/quickstart/installation.rst @@ -159,7 +159,7 @@ Next Steps Now that you have BrainPy installed, you can: - Follow the :doc:`5-minute tutorial <5min-tutorial>` for a quick introduction -- Read about :doc:`core concepts ` to understand BrainPy's architecture +- Read about :doc:`core concepts ` to understand BrainPy's architecture - Explore the :doc:`tutorials <../tutorials/index>` for detailed guides Using BrainPy with Binder diff --git a/docs_state/quickstart/overview.ipynb b/docs_state/quickstart/overview.ipynb new file mode 100644 index 00000000..33247564 --- /dev/null +++ b/docs_state/quickstart/overview.ipynb @@ -0,0 +1,402 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Overview\n", + "\n", + "`brainpy.state` introduces a modern, state-based architecture built on top of `brainstate`. This overview will help you understand the key concepts and design philosophy.\n", + "\n", + "## What's New\n", + "\n", + "`brainpy.state` has been completely rewritten to provide:\n", + "\n", + "- **State-based programming**: Built on `brainstate` for efficient state management\n", + "- **Modular architecture**: Clear separation of concerns (communication, dynamics, outputs)\n", + "- **Physical units**: Integration with `brainunit` for scientifically accurate simulations\n", + "- **Modern API**: Cleaner, more intuitive interfaces\n", + "- **Better performance**: Optimized JIT compilation and memory management\n", + "\n", + "## Key Architectural Components\n", + "\n", + "`brainpy.state` is organized around several core concepts:\n", + "\n", + "### 1. State Management\n", + "\n", + "Everything in `brainpy.state` revolves around **states**. States are variables that persist across time steps:\n", + "\n", + "- `brainstate.State`: Base state container\n", + "- `brainstate.ParamState`: Trainable parameters\n", + "- `brainstate.ShortTermState`: Temporary variables\n", + "\n", + "States enable:\n", + "\n", + "- Automatic differentiation for training\n", + "- Efficient memory management\n", + "- Batching and parallelization\n", + "\n", + "### 2. Neurons\n", + "\n", + "Neurons are the fundamental computational units:" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainunit as u\n", + "\n", + "# Create a population of 100 LIF neurons\n", + "neurons = brainpy.state.LIF(100, tau=10*u.ms, V_th=-50*u.mV)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Key neuron models:\n", + "\n", + "- `brainpy.state.IF`: Integrate-and-Fire\n", + "- `brainpy.state.LIF`: Leaky Integrate-and-Fire\n", + "- `brainpy.state.LIFRef`: LIF with refractory period\n", + "- `brainpy.state.ALIF`: Adaptive LIF\n", + "\n", + "### 3. Synapses\n", + "\n", + "Synapses model the dynamics of neural connections:" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "# Exponential synapse\n", + "synapse = brainpy.state.Expon(100, tau=5*u.ms)\n", + "\n", + "# Alpha synapse (more realistic)\n", + "synapse = brainpy.state.Alpha(100, tau=5*u.ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Synapse models:\n", + "\n", + "- `brainpy.state.Expon`: Single exponential decay\n", + "- `brainpy.state.Alpha`: Double exponential (alpha function)\n", + "- `brainpy.state.AMPA`: Excitatory receptor dynamics\n", + "- `brainpy.state.GABAa`: Inhibitory receptor dynamics\n", + "\n", + "### 4. Projections\n", + "\n", + "Projections connect neural populations:" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate\n", + "\n", + "N_pre=100\n", + "N_post=50\n", + "prob=0.1\n", + "weight=0.5\n", + "\n", + "projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(N_pre, N_post, prob, weight),\n", + " syn=brainpy.state.Expon.desc(N_post, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=neurons\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The projection architecture separates:\n", + "\n", + "- **Communication**: How spikes are transmitted (connectivity, weights)\n", + "- **Synaptic dynamics**: How synapses respond (temporal filtering)\n", + "- **Output mechanism**: How synaptic currents affect neurons (CUBA/COBA)\n", + "\n", + "### 5. Networks\n", + "\n", + "Networks combine neurons and projections:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "class EINet(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.E = brainpy.state.LIF(800)\n", + " self.I = brainpy.state.LIF(200)\n", + " self.E2E = brainpy.state.AlignPostProj(...)\n", + " self.E2I = brainpy.state.AlignPostProj(...)\n", + " # ... more projections\n", + "\n", + " def update(self, input):\n", + " # Define network dynamics\n", + " pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computational Model\n", + "\n", + "### Time-Stepped Simulation\n", + "\n", + "BrainPy uses discrete time steps for simulation:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Set simulation time step\n", + "brainstate.environ.set(dt=0.1 * u.ms)\n", + "\n", + "# Create a simple neuron for demonstration\n", + "neurons = brainpy.state.LIF(100, tau=10*u.ms, V_th=-50*u.mV)\n", + "\n", + "# Initialize all states\n", + "brainstate.nn.init_all_states(neurons)\n", + "\n", + "# Run simulation\n", + "def step(t, i):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " # Provide input current to the neurons\n", + " neurons.update(5 * u.nA)\n", + " return neurons.get_spike()\n", + "\n", + "times = u.math.arange(0*u.ms, 1000*u.ms, brainstate.environ.get_dt())\n", + "indices = u.math.arange(times.size)\n", + "results = brainstate.transform.for_loop(step, times, indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### JIT Compilation\n", + "\n", + "BrainPy leverages JAX for Just-In-Time compilation:" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a simple network for demonstration\n", + "network = brainpy.state.LIF(100, tau=10*u.ms, V_th=-50*u.mV)\n", + "brainstate.nn.init_all_states(network)\n", + "\n", + "# Define input current\n", + "input_current = 5 * u.nA\n", + "\n", + "# JIT-compiled simulation function\n", + "@brainstate.transform.jit\n", + "def simulate(t, i):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " network.update(input_current)\n", + " return network.get_spike()\n", + "\n", + "# First call compiles, subsequent calls are fast\n", + "times = u.math.arange(0*u.ms, 100*u.ms, brainstate.environ.get_dt())\n", + "indices = u.math.arange(times.size)\n", + "result = brainstate.transform.for_loop(simulate, times, indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Benefits:\n", + "\n", + "- Near-C performance\n", + "- Automatic GPU/TPU dispatch\n", + "- Optimized memory usage\n", + "\n", + "### Physical Units\n", + "\n", + "``brainpy.state`` integrates `brainunit` for scientific accuracy:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "import brainunit as u\n", + "\n", + "# Define parameters with units\n", + "tau = 10 * u.ms\n", + "V_threshold = -50 * u.mV\n", + "current = 5 * u.nA\n", + "\n", + "# Units are checked automatically\n", + "neurons = brainpy.state.LIF(100, tau=tau, V_th=V_threshold)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This prevents unit-related bugs and makes code self-documenting.\n", + "\n", + "## Training and Learning\n", + "\n", + "``brainpy.state`` supports gradient-based training:" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "import braintools\n", + "\n", + "# Create a simple network for training\n", + "net = brainpy.state.LIF(10, tau=10*u.ms, V_th=-50*u.mV)\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "# Define optimizer\n", + "optimizer = braintools.optim.Adam(lr=1e-3)\n", + "optimizer.register_trainable_weights(net.states(brainstate.ParamState))\n", + "\n", + "# Prepare dummy data for demonstration\n", + "num_steps = 100\n", + "inputs = u.math.ones((num_steps,)) * 5 * u.nA\n", + "targets = u.math.zeros((num_steps, 10)) # dummy target\n", + "\n", + "# Define loss function\n", + "def loss_fn():\n", + " def step(t, i, inp):\n", + " with brainstate.environ.context(t=t, i=i):\n", + " net.update(inp)\n", + " return net.spike.value\n", + " \n", + " times = u.math.arange(0*u.ms, num_steps*brainstate.environ.get_dt(), brainstate.environ.get_dt())\n", + " indices = u.math.arange(times.size)\n", + " predictions = brainstate.transform.for_loop(step, times, indices, inputs)\n", + " # Simple MSE loss\n", + " return u.math.mean((predictions.astype(float) - targets) ** 2)\n", + "\n", + "# Training step\n", + "@brainstate.transform.jit\n", + "def train_step():\n", + " grads, loss_value = brainstate.transform.grad(\n", + " loss_fn,\n", + " net.states(brainstate.ParamState),\n", + " return_value=True\n", + " )()\n", + " optimizer.update(grads)\n", + " return loss_value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Key features:\n", + "\n", + "- Surrogate gradients for spiking neurons\n", + "- Automatic differentiation\n", + "- Various optimizers (Adam, SGD, etc.)\n", + "\n", + "## Ecosystem Components\n", + "\n", + "`brainpy.state` is part of a larger ecosystem:\n", + "\n", + "### brainstate\n", + "\n", + "The foundation for state management and compilation:\n", + "\n", + "- State-based IR construction\n", + "- JIT compilation\n", + "- Program augmentation (batching, etc.)\n", + "\n", + "### brainunit\n", + "\n", + "Physical units system:\n", + "\n", + "- SI units support\n", + "- Automatic unit checking\n", + "- Unit conversions\n", + "\n", + "### braintools\n", + "\n", + "Utilities and tools:\n", + "\n", + "- Optimizers (`braintools.optim`)\n", + "- Initialization (`braintools.init`)\n", + "- Metrics and losses (`braintools.metric`)\n", + "- Surrogate gradients (`braintools.surrogate`)\n", + "- Visualization (`braintools.visualize`)\n", + "\n", + "## Design Philosophy\n", + "\n", + "`brainpy.state` follows these principles:\n", + "\n", + "1. **Explicit over implicit**: Clear, readable code\n", + "2. **Modular composition**: Build complex models from simple components\n", + "3. **Performance by default**: JIT compilation and optimization built-in\n", + "4. **Scientific accuracy**: Physical units and biologically realistic models\n", + "5. **Extensibility**: Easy to add custom components\n", + "\n", + "## Next Steps\n", + "\n", + "Now that you understand the core concepts:\n", + "\n", + "- Try the [5-minute tutorial](5min-tutorial.ipynb) to get hands-on experience\n", + "- Read the detailed [core concepts](../core-concepts/index.rst) documentation\n", + "- Explore [basic tutorials](../tutorials/index.rst) to learn each component\n", + "- Check out the [examples gallery](../examples/gallery.rst) for real-world models" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/tutorials/advanced/05-snn-training.ipynb b/docs_state/tutorials/advanced/05-snn-training.ipynb index c9612a8f..27627f58 100644 --- a/docs_state/tutorials/advanced/05-snn-training.ipynb +++ b/docs_state/tutorials/advanced/05-snn-training.ipynb @@ -153,9 +153,9 @@ "outputs": [], "source": [ "# Create surrogate gradient functions\n", - "sigmoid_surrogate = braintools.surrogate.sigmoid(alpha=4.0)\n", - "relu_surrogate = braintools.surrogate.relu_grad(alpha=1.0)\n", - "superspike_surrogate = braintools.surrogate.slayer_grad(alpha=4.0)\n", + "sigmoid_surrogate = braintools.surrogate.Sigmoid(alpha=4.0)\n", + "relu_surrogate = braintools.surrogate.ReluGrad(alpha=1.0)\n", + "superspike_surrogate = braintools.surrogate.SlayerGrad(alpha=4.0)\n", "\n", "# Voltage range\n", "V_range = np.linspace(-2, 2, 1000)\n", @@ -235,7 +235,7 @@ " super().__init__()\n", " \n", " # Input to hidden projection\n", - " self.fc1 = brainstate.nn.Linear(n_input, n_hidden, w_init=brainstate.init.KaimingNormal())\n", + " self.fc1 = brainstate.nn.Linear(n_input, n_hidden, w_init=braintools.init.KaimingNormal())\n", " \n", " # Hidden LIF neurons with surrogate gradient\n", " self.lif1 = brainpy.state.LIF(\n", @@ -248,7 +248,7 @@ " )\n", " \n", " # Hidden to output projection\n", - " self.fc2 = brainstate.nn.Linear(n_hidden, n_output, w_init=brainstate.init.KaimingNormal())\n", + " self.fc2 = brainstate.nn.Linear(n_hidden, n_output, w_init=braintools.init.KaimingNormal())\n", " \n", " # Output LIF neurons with surrogate gradient\n", " self.lif2 = brainpy.state.LIF(\n", @@ -903,7 +903,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -917,7 +917,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/advanced/06-synaptic-plasticity.ipynb b/docs_state/tutorials/advanced/06-synaptic-plasticity.ipynb index 5667f0e9..83a99db9 100644 --- a/docs_state/tutorials/advanced/06-synaptic-plasticity.ipynb +++ b/docs_state/tutorials/advanced/06-synaptic-plasticity.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -80,7 +80,29 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "📊 STD Observations:\n", + " • Each spike depletes available resources\n", + " • Synaptic conductance decreases with repeated spikes\n", + " • Resources recover exponentially between spikes\n", + " • Implements synaptic fatigue\n" + ] + } + ], "source": [ "# Create synapse with short-term depression\n", "class STDSynapse(brainpy.state.Synapse):\n", @@ -136,8 +158,8 @@ "for i in range(n_steps):\n", " spike = 1.0 if i in spike_indices else 0.0\n", " g = std_syn(spike)\n", - " g_history.append(float(g))\n", - " x_history.append(float(std_syn.x.value))\n", + " g_history.append(float(g.item()))\n", + " x_history.append(float(std_syn.x.value.item()))\n", "\n", "# Plot\n", "times = np.arange(n_steps) * 0.1\n", @@ -195,9 +217,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "📊 STF vs STD:\n", + " STD: Synaptic strength DECREASES with repeated use\n", + " STF: Synaptic strength INCREASES with repeated use\n", + " Both effects are temporary (100s of ms)\n" + ] + } + ], "source": [ "class STFSynapse(brainpy.state.Synapse):\n", " \"\"\"Synapse with short-term facilitation.\"\"\"\n", @@ -244,8 +287,8 @@ "for i in range(n_steps):\n", " spike = 1.0 if i in spike_indices else 0.0\n", " g = stf_syn(spike)\n", - " g_history_f.append(float(g))\n", - " u_history.append(float(stf_syn.u.value))\n", + " g_history_f.append(float(g.item()))\n", + " u_history.append(float(stf_syn.u.value.item()))\n", "\n", "# Plot comparison\n", "fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)\n", @@ -307,9 +350,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "📊 STP Parameter Regimes:\n", + " Blue (Depressing): High U, slow depression recovery\n", + " Green (Facilitating): Low U, fast depression recovery\n", + " Magenta (Mixed): Balanced parameters\n" + ] + } + ], "source": [ "# Use BrainPy's built-in STP model\n", "# For demonstration, we'll test different parameter regimes\n", @@ -359,7 +423,7 @@ " for i in range(n_steps):\n", " spike = 1.0 if i in spike_indices else 0.0\n", " g = syn(spike)\n", - " g_hist.append(float(g))\n", + " g_hist.append(float(g.item()))\n", " \n", " return g_hist\n", "\n", @@ -429,9 +493,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "📊 STDP Principle:\n", + " 'Neurons that fire together, wire together'\n", + " Positive Δt (pre→post): Potentiation (LTP)\n", + " Negative Δt (post→pre): Depression (LTD)\n", + " Exponential decay with distance from Δt=0\n" + ] + } + ], "source": [ "# STDP learning window\n", "def stdp_window(dt_values, A_plus=0.01, A_minus=0.01, tau_plus=20.0, tau_minus=20.0):\n", @@ -498,9 +584,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "📊 STDP Learning Result:\n", + " Initial weight: 0.500\n", + " Final weight: 0.481\n", + " Change: -0.019\n", + " ✅ Weight increased due to consistent pre→post timing!\n" + ] + } + ], "source": [ "class STDPSynapse(brainpy.state.Synapse):\n", " \"\"\"Synapse with STDP learning.\"\"\"\n", @@ -575,7 +683,7 @@ " pre_spike = 1.0 if i in pre_indices else 0.0\n", " post_spike = 1.0 if i in post_indices else 0.0\n", " stdp_syn(pre_spike, post_spike)\n", - " w_history.append(float(stdp_syn.w.value))\n", + " w_history.append(float(stdp_syn.w.value.item()))\n", "\n", "# Plot weight evolution\n", "times_plot = np.arange(n_steps) * 0.1\n", @@ -623,9 +731,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "📊 Network with Plasticity:\n", + " Initial weight norm: 4.396\n", + " Final weight norm: 4.413\n", + " Change: +0.017\n", + " Weights adapt based on network activity!\n" + ] + } + ], "source": [ "class PlasticNetwork(brainstate.nn.Module):\n", " \"\"\"Recurrent network with STDP.\"\"\"\n", @@ -660,7 +790,7 @@ " spikes = self.neurons.get_spike()\n", " \n", " # Compute recurrent input\n", - " recurrent_input = jnp.dot(spikes, self.weights.value) * u.nA\n", + " recurrent_input = jnp.dot(spikes, self.weights.value) * u.mA\n", " \n", " # Update neurons\n", " self.neurons(inp + recurrent_input)\n", @@ -690,7 +820,7 @@ "\n", "for i in range(n_steps):\n", " # Random external input\n", - " inp = brainstate.random.rand(net.n_neurons) * 2.0 * u.nA\n", + " inp = brainstate.random.rand(net.n_neurons) * 200.0 * u.mA\n", " \n", " # Get spikes before update\n", " pre_spikes = net.neurons.get_spike()\n", @@ -757,9 +887,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "💡 Hybrid Learning Strategy:\n", + "\n", + "1. Feedforward weights: Trained with gradient descent (supervised)\n", + " - Fast convergence\n", + " - Optimized for task objective\n", + "\n", + "2. Recurrent weights: Updated with STDP (unsupervised)\n", + " - Biologically plausible\n", + " - Adapts to input statistics\n", + " - Provides temporal dynamics\n", + "\n", + "3. Benefits:\n", + " - Best of both worlds\n", + " - Robust to distribution shift\n", + " - Continual adaptation\n", + "\n", + "Implementation:\n", + " - Train feedforward with brainstate.transform.grad()\n", + " - Update recurrent with STDP rule\n", + " - Alternate or interleave both updates\n", + "\n" + ] + } + ], "source": [ "# Template for hybrid learning\n", "class HybridNetwork(brainstate.nn.Module):\n", @@ -921,7 +1079,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -935,7 +1093,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/advanced/07-large-scale-simulations.ipynb b/docs_state/tutorials/advanced/07-large-scale-simulations.ipynb index e5f98c44..202c335b 100644 --- a/docs_state/tutorials/advanced/07-large-scale-simulations.ipynb +++ b/docs_state/tutorials/advanced/07-large-scale-simulations.ipynb @@ -288,28 +288,28 @@ " \n", " # Sparse projections with EventFixedProb\n", " self.E2E = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob=p_conn, weight=0.6*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, p_conn, 0.6*u.mS),\n", " syn=brainpy.state.Expon.desc(n_exc, tau=5.*u.ms),\n", " out=brainpy.state.COBA.desc(E=0.*u.mV),\n", " post=self.E\n", " )\n", " \n", " self.E2I = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, prob=p_conn, weight=0.6*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, p_conn, 0.6*u.mS),\n", " syn=brainpy.state.Expon.desc(n_inh, tau=5.*u.ms),\n", " out=brainpy.state.COBA.desc(E=0.*u.mV),\n", " post=self.I\n", " )\n", " \n", " self.I2E = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, prob=p_conn, weight=6.7*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, p_conn, 6.7*u.mS),\n", " syn=brainpy.state.Expon.desc(n_exc, tau=10.*u.ms),\n", " out=brainpy.state.COBA.desc(E=-80.*u.mV),\n", " post=self.E\n", " )\n", " \n", " self.I2I = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, prob=p_conn, weight=6.7*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, p_conn, 6.7*u.mS),\n", " syn=brainpy.state.Expon.desc(n_inh, tau=10.*u.ms),\n", " out=brainpy.state.COBA.desc(E=-80.*u.mV),\n", " post=self.I\n", @@ -509,7 +509,7 @@ " super().__init__()\n", " self.lif = brainpy.state.LIF(n_neurons, V_rest=-65.*u.mV, V_th=-50.*u.mV, tau=10.*u.ms)\n", " self.proj = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_neurons, n_neurons, prob=0.01, weight=0.5*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_neurons, n_neurons, 0.01, 0.5*u.mS),\n", " syn=brainpy.state.Expon.desc(n_neurons, tau=5.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.lif\n", @@ -742,28 +742,28 @@ " \n", " # Sparse connectivity\n", " self.E2E = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob=p_conn, weight=0.05*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, p_conn, 0.05*u.mS),\n", " syn=brainpy.state.Expon.desc(n_exc, tau=5.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.E\n", " )\n", " \n", " self.E2I = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, prob=p_conn, weight=0.05*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, p_conn, 0.05*u.mS),\n", " syn=brainpy.state.Expon.desc(n_inh, tau=5.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.I\n", " )\n", " \n", " self.I2E = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, prob=p_conn, weight=0.4*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, p_conn, 0.4*u.mS),\n", " syn=brainpy.state.Expon.desc(n_exc, tau=10.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.E\n", " )\n", " \n", " self.I2I = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, prob=p_conn, weight=0.4*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, p_conn, 0.4*u.mS),\n", " syn=brainpy.state.Expon.desc(n_inh, tau=10.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.I\n", @@ -981,7 +981,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -995,7 +995,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/basic/01-lif-neuron.ipynb b/docs_state/tutorials/basic/01-lif-neuron.ipynb index 2b1c6dbf..677a9029 100644 --- a/docs_state/tutorials/basic/01-lif-neuron.ipynb +++ b/docs_state/tutorials/basic/01-lif-neuron.ipynb @@ -17,9 +17,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BrainPy version: 2.7.2\n" + ] + } + ], "source": [ "import brainpy\n", "import brainstate\n", @@ -27,6 +35,7 @@ "import braintools\n", "import matplotlib.pyplot as plt\n", "import jax.numpy as jnp\n", + "import numpy as np\n", "\n", "print(f\"BrainPy version: {brainpy.__version__}\")" ] @@ -60,16 +69,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neuron created successfully!\n", + "Initial membrane potential: ArrayImpl([0.], dtype=float32) * mvolt\n" + ] + } + ], "source": [ "# Set simulation time step\n", "brainstate.environ.set(dt=0.1 * u.ms)\n", "\n", "# Create a single LIF neuron\n", "neuron = brainpy.state.LIF(\n", - " size=1,\n", + " in_size=1,\n", " V_rest=-65. * u.mV, # Resting potential\n", " V_th=-50. * u.mV, # Spike threshold\n", " V_reset=-65. * u.mV, # Reset potential\n", @@ -96,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +127,7 @@ "times = u.math.arange(0. * u.ms, duration, dt)\n", "\n", "# Constant input current\n", - "I_input = 2.0 * u.nA\n", + "I_input = 20.0 * u.mA\n", "\n", "# Run simulation\n", "voltages = []\n", @@ -126,9 +144,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of spikes: 14\n", + "Firing rate: 70.00 Hz\n", + "Mean ISI: 13.90 ms\n", + "ISI CV: 0.000\n" + ] + } + ], "source": [ "# Plot results\n", "times_plot = times.to_decimal(u.ms)\n", @@ -182,18 +221,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Completed 1/10: I = 0.0 * mamp, rate = 0.0 Hz\n", + "Completed 2/10: I = 2.222222328186035 * mamp, rate = 0.0 Hz\n", + "Completed 3/10: I = 4.44444465637207 * mamp, rate = 0.0 Hz\n", + "Completed 4/10: I = 6.6666669845581055 * mamp, rate = 0.0 Hz\n", + "Completed 5/10: I = 8.88888931274414 * mamp, rate = 0.0 Hz\n", + "Completed 6/10: I = 11.111111640930176 * mamp, rate = 0.0 Hz\n", + "Completed 7/10: I = 13.333333969116211 * mamp, rate = 0.0 Hz\n", + "Completed 8/10: I = 15.55555534362793 * mamp, rate = 28.0 Hz\n", + "Completed 9/10: I = 17.77777862548828 * mamp, rate = 52.0 Hz\n", + "Completed 10/10: I = 20.0 * mamp, rate = 70.0 Hz\n" + ] + } + ], "source": [ "# Range of input currents\n", - "currents = u.math.linspace(0 * u.nA, 5 * u.nA, 20)\n", + "currents = u.math.linspace(0 * u.mA, 20 * u.mA, 10)\n", "firing_rates = []\n", "\n", "duration = 500. * u.ms\n", "times = u.math.arange(0. * u.ms, duration, dt)\n", "\n", - "for I in currents:\n", + "# for I in currents:\n", + "for i, I in enumerate(currents):\n", " # Reset neuron\n", " brainstate.nn.init_all_states(neuron)\n", " \n", @@ -207,15 +264,34 @@ " # Calculate firing rate\n", " rate = spike_count / (duration.to_decimal(u.second))\n", " firing_rates.append(rate)\n", + " print(f\"Completed {i+1}/{len(currents)}: I = {I}, rate = {rate} Hz\")\n", "\n", "firing_rates = jnp.array(firing_rates)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rheobase (minimum spiking current): 15.55555534362793 * mamp\n" + ] + } + ], "source": [ "# Plot F-I curve\n", "plt.figure(figsize=(8, 5))\n", @@ -250,13 +326,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Create two neurons with different reset modes\n", "neuron_hard = brainpy.state.LIF(\n", - " size=1,\n", + " 1,\n", " V_rest=-65. * u.mV,\n", " V_th=-50. * u.mV,\n", " V_reset=-65. * u.mV,\n", @@ -265,7 +341,7 @@ ")\n", "\n", "neuron_soft = brainpy.state.LIF(\n", - " size=1,\n", + " 1,\n", " V_rest=-65. * u.mV,\n", " V_th=-50. * u.mV,\n", " V_reset=-65. * u.mV, # Not used in soft reset\n", @@ -280,7 +356,7 @@ "# Simulate both with same input\n", "duration = 200. * u.ms\n", "times = u.math.arange(0. * u.ms, duration, dt)\n", - "I_input = 3.0 * u.nA # Strong input\n", + "I_input = 20.0 * u.mA # Strong input\n", "\n", "voltages_hard = []\n", "voltages_soft = []\n", @@ -304,9 +380,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hard reset: 14 spikes, 70.00 Hz\n", + "Soft reset: 17 spikes, 85.00 Hz\n", + "\n", + "Soft reset fires 21.4% more frequently\n" + ] + } + ], "source": [ "# Plot comparison\n", "times_plot = times.to_decimal(u.ms)\n", @@ -355,14 +452,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\soft\\Anaconda3\\envs\\Ecosystem-py\\Lib\\site-packages\\braintools\\surrogate.py:72: UserWarning: Explicitly requested dtype float64 requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/jax-ml/jax#current-gotchas for more.\n", + " z = jnp.asarray(x >= 0, dtype=x.dtype)\n" + ] + } + ], "source": [ "# Create population with varied initial conditions\n", "pop_size = 50\n", "neuron_pop = brainpy.state.LIF(\n", - " size=pop_size,\n", + " pop_size,\n", " V_rest=-65. * u.mV,\n", " V_th=-50. * u.mV,\n", " V_reset=-65. * u.mV,\n", @@ -381,7 +487,7 @@ "spike_history = []\n", "for t in times:\n", " # Step current: 0 → 2.5 nA at t=50ms\n", - " I = 2.5 * u.nA if t > 50*u.ms else 0 * u.nA\n", + " I = 20. * u.mA if t > 50. *u.ms else 0 * u.mA\n", " neuron_pop(I)\n", " spike_history.append(neuron_pop.get_spike())\n", "\n", @@ -390,14 +496,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Raster plot\n", "t_indices, n_indices = u.math.where(spike_history > 0)\n", "spike_times = times[t_indices].to_decimal(u.ms)\n", "\n", + "time_max = float(duration.to_decimal(u.ms))\n", + "xticks = np.arange(0, time_max + 1, 50)\n", + "\n", "plt.figure(figsize=(12, 6))\n", "plt.scatter(spike_times, n_indices, s=2, c='black', alpha=0.6)\n", "plt.axvline(x=50, color='r', linestyle='--', alpha=0.5, label='Input onset')\n", @@ -406,6 +536,8 @@ "plt.title('Population Activity Raster Plot (50 LIF Neurons)', fontsize=14)\n", "plt.legend()\n", "plt.grid(True, alpha=0.3)\n", + "plt.xlim(0, time_max)\n", + "plt.xticks(xticks)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", @@ -424,6 +556,8 @@ "plt.title('Population Firing Rate', fontsize=14)\n", "plt.legend()\n", "plt.grid(True, alpha=0.3)\n", + "plt.xlim(0, time_max)\n", + "plt.xticks(xticks)\n", "plt.tight_layout()\n", "plt.show()" ] @@ -439,9 +573,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observation: Larger τ → slower integration, lower firing rate\n" + ] + } + ], "source": [ "# Different time constants\n", "taus = [5*u.ms, 10*u.ms, 20*u.ms]\n", @@ -456,7 +608,7 @@ "# Simulate\n", "duration = 150. * u.ms\n", "times = u.math.arange(0. * u.ms, duration, dt)\n", - "I_input = 2.0 * u.nA\n", + "I_input = 20.0 * u.mA\n", "\n", "results = {}\n", "for tau, neuron in zip(taus, neurons):\n", @@ -523,7 +675,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -537,7 +689,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/basic/02-synapse-models.ipynb b/docs_state/tutorials/basic/02-synapse-models.ipynb index f65fd281..cd93d420 100644 --- a/docs_state/tutorials/basic/02-synapse-models.ipynb +++ b/docs_state/tutorials/basic/02-synapse-models.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -63,16 +63,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created Expon synapse with tau=5.0 * msecond\n" + ] + } + ], "source": [ "# Set time step\n", "brainstate.environ.set(dt=0.1 * u.ms)\n", "\n", "# Create exponential synapse\n", "expon_syn = brainpy.state.Expon(\n", - " size=1,\n", + " in_size=1,\n", " tau=5. * u.ms,\n", " g_initializer=braintools.init.Constant(0. * u.mS)\n", ")\n", @@ -85,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -99,8 +107,10 @@ "responses = []\n", "for i, t in enumerate(times):\n", " # Spike at t=0\n", - " spike = 1.0 if i == 0 else 0.0\n", - " expon_syn(jnp.array([spike]))\n", + " # spike = 1.0 if i == 0 else 0.0\n", + " # expon_syn(jnp.array([spike]))\n", + " spike = 1.0 * u.mS if i == 0 else 0.0 * u.mS\n", + " expon_syn(u.math.asarray([spike]))\n", " responses.append(expon_syn.g.value[0])\n", "\n", "responses = u.math.asarray(responses)" @@ -108,9 +118,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observation: Instantaneous rise, exponential decay\n" + ] + } + ], "source": [ "# Plot response\n", "plt.figure(figsize=(10, 4))\n", @@ -143,13 +171,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Create alpha synapse\n", "alpha_syn = brainpy.state.Alpha(\n", - " size=1,\n", + " in_size=1,\n", " tau=5. * u.ms,\n", " g_initializer=braintools.init.Constant(0. * u.mS)\n", ")\n", @@ -159,8 +187,8 @@ "# Simulate\n", "alpha_responses = []\n", "for i, t in enumerate(times):\n", - " spike = 1.0 if i == 0 else 0.0\n", - " alpha_syn(jnp.array([spike]))\n", + " spike = 1.0 * u.mS if i == 0 else 0.0 * u.mS\n", + " alpha_syn(u.math.asarray([spike]))\n", " alpha_responses.append(alpha_syn.g.value[0])\n", "\n", "alpha_responses = u.math.asarray(alpha_responses)" @@ -168,9 +196,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Key difference: Alpha has realistic rise time, peak at t=τ\n" + ] + } + ], "source": [ "# Compare Expon vs Alpha\n", "plt.figure(figsize=(10, 5))\n", @@ -204,29 +250,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AMPA tau: 2.0 * msecond (fast)\n", + "GABAa tau: 1.0 * mmolar (slow)\n" + ] + } + ], "source": [ "# Create AMPA synapse (fast excitatory)\n", "ampa_syn = brainpy.state.AMPA(\n", - " size=1,\n", - " tau=2. * u.ms,\n", + " in_size=1,\n", + " T=2. * u.ms,\n", " g_initializer=braintools.init.Constant(0. * u.mS)\n", ")\n", "\n", "# Create GABAa synapse (slower inhibitory)\n", "gaba_syn = brainpy.state.GABAa(\n", - " size=1,\n", - " tau=10. * u.ms,\n", + " in_size=1,\n", + " T_dur=10. * u.ms,\n", " g_initializer=braintools.init.Constant(0. * u.mS)\n", ")\n", "\n", "brainstate.nn.init_all_states(ampa_syn)\n", "brainstate.nn.init_all_states(gaba_syn)\n", "\n", - "print(f\"AMPA tau: {ampa_syn.tau} (fast)\")\n", - "print(f\"GABAa tau: {gaba_syn.tau} (slow)\")" + "print(f\"AMPA tau: {ampa_syn.T} (fast)\")\n", + "print(f\"GABAa tau: {gaba_syn.T} (slow)\")" ] }, { @@ -295,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -312,10 +367,10 @@ "\n", "for i, t in enumerate(times):\n", " t_ms = t.to_decimal(u.ms)\n", - " spike = 1.0 if any(abs(t_ms - st) < 0.1 for st in spike_times) else 0.0\n", + " spike = 1.0 *u.mS if any(abs(t_ms - st) < 0.1 for st in spike_times) else 0.0 *u.mS\n", " \n", - " expon_syn(jnp.array([spike]))\n", - " alpha_syn(jnp.array([spike]))\n", + " expon_syn(u.math.asarray([spike]))\n", + " alpha_syn(u.math.asarray([spike]))\n", " \n", " expon_train_resp.append(expon_syn.g.value[0])\n", " alpha_train_resp.append(alpha_syn.g.value[0])\n", @@ -328,7 +383,25 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Temporal summation: synaptic responses accumulate over time\n" + ] + } + ], "source": [ "# Plot spike train responses\n", "fig, axes = plt.subplots(2, 1, figsize=(12, 8), sharex=True)\n", @@ -389,8 +462,8 @@ "for tau, syn in zip(taus, synapses):\n", " resp = []\n", " for i, t in enumerate(times):\n", - " spike = 1.0 if i == 0 else 0.0\n", - " syn(jnp.array([spike]))\n", + " spike = 1.0 * u.mS if i == 0 else 0.0 * u.mS\n", + " syn(u.math.asarray([spike]))\n", " resp.append(syn.g.value[0])\n", " responses_by_tau[tau] = u.math.asarray(resp)" ] @@ -399,7 +472,29 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Effect of τ:\n", + "- Smaller τ → faster decay, less temporal summation\n", + "- Larger τ → slower decay, more temporal summation\n", + "- Choose τ based on biological constraints and computational needs\n" + ] + } + ], "source": [ "# Plot effect of tau\n", "plt.figure(figsize=(10, 6))\n", @@ -437,7 +532,19 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Synapse integrated into projection!\n", + "Synapse type: Expon\n", + "Synapse tau: 5.0 * msecond\n", + "\n", + "See Tutorial 3 for complete network building!\n" + ] + } + ], "source": [ "# Create neurons\n", "pre_neurons = brainpy.state.LIF(10, V_rest=-65.*u.mV, V_th=-50.*u.mV, tau=10.*u.ms)\n", @@ -446,7 +553,7 @@ "# Create projection with exponential synapse\n", "projection = brainpy.state.AlignPostProj(\n", " comm=brainstate.nn.EventFixedProb(\n", - " 10, 5, prob=0.5, weight=0.5*u.mS\n", + " 10, 5, 0.5, 0.5*u.mS\n", " ),\n", " syn=brainpy.state.Expon.desc(5, tau=5.*u.ms), # Synapse descriptor\n", " out=brainpy.state.CUBA.desc(),\n", @@ -503,7 +610,17 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom synapse created!\n", + "Rise time: 1.0 * msecond\n", + "Decay time: 10.0 * msecond\n" + ] + } + ], "source": [ "from brainpy.state import Synapse\n", "\n", @@ -560,7 +677,25 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Custom synapse shows fast rise (1ms) and slow decay (10ms)\n" + ] + } + ], "source": [ "# Test custom synapse\n", "custom_resp = []\n", @@ -629,7 +764,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -643,7 +778,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/basic/03-network-connections.ipynb b/docs_state/tutorials/basic/03-network-connections.ipynb index de27debd..b55b4b3f 100644 --- a/docs_state/tutorials/basic/03-network-connections.ipynb +++ b/docs_state/tutorials/basic/03-network-connections.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -60,9 +60,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created projection: 20 → 10 neurons\n", + "Connectivity: 30% probability\n", + "Expected connections: ~60 synapses\n", + "Synapse type: Exponential (τ=5ms)\n", + "Output mechanism: CUBA (current-based)\n" + ] + } + ], "source": [ "# Set time step\n", "brainstate.environ.set(dt=0.1 * u.ms)\n", @@ -75,10 +87,10 @@ "projection = brainpy.state.AlignPostProj(\n", " # Stage 1: Communication (connectivity + weights)\n", " comm=brainstate.nn.EventFixedProb(\n", - " pre_num=20, \n", - " post_num=10, \n", - " prob=0.3, # 30% connection probability\n", - " weight=0.5*u.mS # Synaptic weight\n", + " 20, \n", + " 10, \n", + " 0.3, # 30% connection probability\n", + " 0.5*u.mS # Synaptic weight\n", " ),\n", " \n", " # Stage 2: Synapse (temporal dynamics)\n", @@ -107,9 +119,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pre spikes: 20 neurons fired\n", + "Synaptic conductance (g): ArrayImpl([0., 0., 0., 0., 0.], dtype=float32) * msiemens\n", + "Post spikes: 10 neurons fired\n" + ] + } + ], "source": [ "# Initialize all states\n", "brainstate.nn.init_all_states(pre_neurons)\n", @@ -160,9 +182,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CUBA: I_syn = g\n", + "COBA: I_syn = g * (V - E_rev)\n", + "\n", + "With V=-65mV and E=0mV:\n", + "COBA current will be ~65mV larger (more driving force)\n" + ] + } + ], "source": [ "# Create neurons\n", "neurons_cuba = brainpy.state.LIF(5, V_rest=-65.*u.mV, V_th=-50.*u.mV, tau=10.*u.ms)\n", @@ -171,7 +205,7 @@ "\n", "# CUBA projection\n", "proj_cuba = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(10, 5, prob=0.5, weight=1.0*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(10, 5, 0.5, 1.0*u.mS),\n", " syn=brainpy.state.Expon.desc(5, tau=5.*u.ms),\n", " out=brainpy.state.CUBA.desc(), # Current-based\n", " post=neurons_cuba\n", @@ -179,7 +213,7 @@ "\n", "# COBA projection (excitatory reversal potential at 0 mV)\n", "proj_coba = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(10, 5, prob=0.5, weight=1.0*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(10, 5, 0.5, 1.0*u.mS),\n", " syn=brainpy.state.Expon.desc(5, tau=5.*u.ms),\n", " out=brainpy.state.COBA.desc(E=0.*u.mV), # Conductance-based\n", " post=neurons_coba\n", @@ -193,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -232,9 +266,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observation: COBA produces stronger depolarization due to larger driving force\n" + ] + } + ], "source": [ "# Plot comparison\n", "plt.figure(figsize=(12, 5))\n", @@ -268,9 +320,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created E-I network:\n", + " - 80 excitatory neurons\n", + " - 20 inhibitory neurons\n", + " - 100 total neurons\n", + " - 10% connectivity\n", + " - 4 projection types (E2E, E2I, I2E, I2I)\n" + ] + } + ], "source": [ "class EINetwork(brainstate.nn.Module):\n", " def __init__(self, n_exc=80, n_inh=20, prob=0.1):\n", @@ -299,14 +364,14 @@ " \n", " # Excitatory projections (fast, AMPA-like)\n", " self.E2E = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob=prob, weight=0.3*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_exc, prob, 0.3*u.mS),\n", " syn=brainpy.state.Expon.desc(n_exc, tau=2.*u.ms), # Fast excitation\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.E\n", " )\n", " \n", " self.E2I = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, prob=prob, weight=0.3*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_exc, n_inh, prob, 0.3*u.mS),\n", " syn=brainpy.state.Expon.desc(n_inh, tau=2.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.I\n", @@ -314,14 +379,14 @@ " \n", " # Inhibitory projections (slower, GABAa-like)\n", " self.I2E = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, prob=prob, weight=-2.0*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_exc, prob, -2.0*u.mS),\n", " syn=brainpy.state.Expon.desc(n_exc, tau=10.*u.ms), # Slower inhibition\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.E\n", " )\n", " \n", " self.I2I = brainpy.state.AlignPostProj(\n", - " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, prob=prob, weight=-2.0*u.mS),\n", + " comm=brainstate.nn.EventFixedProb(n_inh, n_inh, prob, -2.0*u.mS),\n", " syn=brainpy.state.Expon.desc(n_inh, tau=10.*u.ms),\n", " out=brainpy.state.CUBA.desc(),\n", " post=self.I\n", @@ -367,9 +432,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running simulation...\n", + "Duration: 500.0 * msecond\n", + "Time step: 0.1 * msecond\n", + "Steps: 5000\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bfd46438f02a4a389559c7f6749219bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/5000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Combine E and I spikes\n", "all_spikes = u.math.concatenate([spk_e, spk_i], axis=1)\n", @@ -517,15 +643,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connectivity patterns:\n", + "1. FixedProb: Random connections with fixed probability\n", + "2. All2All: Every neuron connects to every other\n", + "3. One2One: Neuron i connects only to neuron i\n", + "\n", + "You can also create custom connectivity!\n" + ] + } + ], "source": [ "# Example: Different connectivity patterns\n", "\n", "# 1. Fixed probability (what we used)\n", "conn_fixed_prob = brainstate.nn.EventFixedProb(\n", - " 20, 10, prob=0.3, weight=0.5*u.mS\n", + " 20, 10, 0.3, 0.5*u.mS\n", ")\n", "\n", "# 2. All-to-all (full connectivity)\n", @@ -556,9 +695,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experiments to try:\n", + "\n", + "1. Change E/I balance:\n", + " - Increase inhibitory weight → more synchrony\n", + " - Decrease inhibitory weight → more irregular\n", + "\n", + "2. Change connectivity:\n", + " - Higher prob → more correlated activity\n", + " - Lower prob → more independent neurons\n", + "\n", + "3. Change time constants:\n", + " - Faster inhibition → sharper oscillations\n", + " - Slower inhibition → smoother dynamics\n", + "\n", + "4. Change external input:\n", + " - Stronger input → higher firing rates\n", + " - Unbalanced input → population bias\n" + ] + } + ], "source": [ "# Try these experiments:\n", "\n", @@ -627,7 +790,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -641,7 +804,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/basic/04-input-output.ipynb b/docs_state/tutorials/basic/04-input-output.ipynb index 8277bf6c..32007128 100644 --- a/docs_state/tutorials/basic/04-input-output.ipynb +++ b/docs_state/tutorials/basic/04-input-output.ipynb @@ -145,6 +145,15 @@ "output_spikes_hist = u.math.asarray(output_spikes_hist)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(dt)" + ] + }, { "cell_type": "code", "execution_count": null, @@ -537,6 +546,7 @@ " n_hidden, n_output,\n", " weight_initializer=braintools.init.KaimingNormal()\n", " )\n", + "\n", " \n", " def update(self, spike_input):\n", " # Convert input spikes to current\n", @@ -736,7 +746,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Ecosystem-py", "language": "python", "name": "python3" }, @@ -750,7 +760,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.11.13" } }, "nbformat": 4, diff --git a/docs_state/tutorials/how-to-guides/custom-components.ipynb b/docs_state/tutorials/how-to-guides/custom-components.ipynb new file mode 100644 index 00000000..68872d05 --- /dev/null +++ b/docs_state/tutorials/how-to-guides/custom-components.ipynb @@ -0,0 +1,633 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Create Custom Components\n", + "\n", + "This guide shows you how to create custom neurons, synapses, and other components in BrainPy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick Start\n", + "\n", + "**Custom neuron template:**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "import jax.numpy as jnp\n", + "\n", + "class CustomNeuron(brainpy.state.Neuron):\n", + " def __init__(self, size, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + "\n", + " # Parameters\n", + " self.tau = 10.0 * u.ms\n", + " self.V_th = -50.0 * u.mV\n", + "\n", + " # States\n", + " self.V = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.V.value = jnp.zeros(shape)\n", + " self.spike.value = jnp.zeros(shape)\n", + "\n", + " def update(self, x):\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Dynamics\n", + " dV = -self.V.value / self.tau.to_decimal(u.ms) + x.to_decimal(u.nA)\n", + " self.V.value += dV * dt.to_decimal(u.ms)\n", + "\n", + " # Spike generation\n", + " self.spike.value = (self.V.value >= self.V_th.to_decimal(u.mV)).astype(float)\n", + "\n", + " # Reset\n", + " self.V.value = jnp.where(\n", + " self.spike.value > 0,\n", + " 0.0, # Reset voltage\n", + " self.V.value\n", + " )\n", + "\n", + " return self.V.value\n", + "\n", + " def get_spike(self):\n", + " return self.spike.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Neurons\n", + "\n", + "### Example 1: Adaptive LIF\n", + "\n", + "**LIF with spike-frequency adaptation:**" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class AdaptiveLIF(brainpy.state.Neuron):\n", + " \"\"\"LIF neuron with adaptation current.\"\"\"\n", + "\n", + " def __init__(self, size, tau=10*u.ms, tau_w=100*u.ms,\n", + " V_th=-50*u.mV, V_reset=-65*u.mV, a=0.1*u.nA,\n", + " b=0.5*u.nA, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + "\n", + " self.tau = tau\n", + " self.tau_w = tau_w\n", + " self.V_th = V_th\n", + " self.V_reset = V_reset\n", + " self.a = a # Adaptation coupling\n", + " self.b = b # Spike-triggered adaptation\n", + "\n", + " # States\n", + " self.V = brainstate.ShortTermState(jnp.ones(size) * V_reset.to_decimal(u.mV))\n", + " self.w = brainstate.ShortTermState(jnp.zeros(size)) # Adaptation current\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.V.value = jnp.ones(shape) * self.V_reset.to_decimal(u.mV)\n", + " self.w.value = jnp.zeros(shape)\n", + " self.spike.value = jnp.zeros(shape)\n", + "\n", + " def update(self, I_ext):\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Membrane potential dynamics\n", + " dV = (-self.V.value + self.V_reset.to_decimal(u.mV) + I_ext.to_decimal(u.nA) - self.w.value) / self.tau.to_decimal(u.ms)\n", + " self.V.value += dV * dt.to_decimal(u.ms)\n", + "\n", + " # Adaptation dynamics\n", + " dw = (self.a.to_decimal(u.nA) * (self.V.value - self.V_reset.to_decimal(u.mV)) - self.w.value) / self.tau_w.to_decimal(u.ms)\n", + " self.w.value += dw * dt.to_decimal(u.ms)\n", + "\n", + " # Spike generation\n", + " self.spike.value = (self.V.value >= self.V_th.to_decimal(u.mV)).astype(float)\n", + "\n", + " # Reset and adaptation jump\n", + " self.V.value = jnp.where(\n", + " self.spike.value > 0,\n", + " self.V_reset.to_decimal(u.mV),\n", + " self.V.value\n", + " )\n", + " self.w.value += self.spike.value * self.b.to_decimal(u.nA)\n", + "\n", + " return self.V.value\n", + "\n", + " def get_spike(self):\n", + " return self.spike.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2: Izhikevich Neuron" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "class Izhikevich(brainpy.state.Neuron):\n", + " \"\"\"Izhikevich neuron model.\"\"\"\n", + "\n", + " def __init__(self, size, a=0.02, b=0.2, c=-65*u.mV, d=8*u.mV, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + "\n", + " self.a = a\n", + " self.b = b\n", + " self.c = c\n", + " self.d = d\n", + "\n", + " # States\n", + " self.V = brainstate.ShortTermState(jnp.ones(size) * c.to_decimal(u.mV))\n", + " self.u = brainstate.ShortTermState(jnp.zeros(size))\n", + " self.spike = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.V.value = jnp.ones(shape) * self.c.to_decimal(u.mV)\n", + " self.u.value = jnp.zeros(shape)\n", + " self.spike.value = jnp.zeros(shape)\n", + "\n", + " def update(self, I):\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Izhikevich dynamics\n", + " dV = (0.04 * self.V.value**2 + 5 * self.V.value + 140 - self.u.value + I.to_decimal(u.nA))\n", + " du = self.a * (self.b * self.V.value - self.u.value)\n", + "\n", + " self.V.value += dV * dt.to_decimal(u.ms)\n", + " self.u.value += du * dt.to_decimal(u.ms)\n", + "\n", + " # Spike and reset\n", + " self.spike.value = (self.V.value >= 30).astype(float)\n", + " self.V.value = jnp.where(self.spike.value > 0, self.c.to_decimal(u.mV), self.V.value)\n", + " self.u.value = jnp.where(self.spike.value > 0, self.u.value + self.d.to_decimal(u.mV), self.u.value)\n", + "\n", + " return self.V.value\n", + "\n", + " def get_spike(self):\n", + " return self.spike.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Synapses\n", + "\n", + "### Example: Biexponential Synapse" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "class BiexponentialSynapse(brainpy.state.Synapse):\n", + " \"\"\"Synapse with separate rise and decay.\"\"\"\n", + "\n", + " def __init__(self, size, tau_rise=1*u.ms, tau_decay=5*u.ms, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + "\n", + " self.tau_rise = tau_rise\n", + " self.tau_decay = tau_decay\n", + "\n", + " # States\n", + " self.h = brainstate.ShortTermState(jnp.zeros(size)) # Rising phase\n", + " self.g = brainstate.ShortTermState(jnp.zeros(size)) # Decaying phase\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.h.value = jnp.zeros(shape)\n", + " self.g.value = jnp.zeros(shape)\n", + "\n", + " def update(self, x):\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Two-stage dynamics\n", + " dh = -self.h.value / self.tau_rise.to_decimal(u.ms) + x\n", + " dg = -self.g.value / self.tau_decay.to_decimal(u.ms) + self.h.value\n", + "\n", + " self.h.value += dh * dt.to_decimal(u.ms)\n", + " self.g.value += dg * dt.to_decimal(u.ms)\n", + "\n", + " return self.g.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example: NMDA Synapse" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class NMDASynapse(brainpy.state.Synapse):\n", + " \"\"\"NMDA receptor with voltage dependence.\"\"\"\n", + "\n", + " def __init__(self, size, tau=100*u.ms, a=0.5/u.mM, Mg=1.0*u.mM, **kwargs):\n", + " super().__init__(size, **kwargs)\n", + "\n", + " self.tau = tau\n", + " self.a = a\n", + " self.Mg = Mg\n", + "\n", + " self.g = brainstate.ShortTermState(jnp.zeros(size))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape = self.size if batch_size is None else (batch_size, self.size)\n", + " self.g.value = jnp.zeros(shape)\n", + "\n", + " def update(self, x, V_post=None):\n", + " \"\"\"Update with optional postsynaptic voltage.\"\"\"\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Conductance dynamics\n", + " dg = -self.g.value / self.tau.to_decimal(u.ms) + x\n", + " self.g.value += dg * dt.to_decimal(u.ms)\n", + "\n", + " # Voltage-dependent magnesium block\n", + " if V_post is not None:\n", + " mg_block = 1 / (1 + self.Mg.to_decimal(u.mM) * self.a.to_decimal(1/u.mM) * jnp.exp(-0.062 * V_post.to_decimal(u.mV)))\n", + " return self.g.value * mg_block\n", + " else:\n", + " return self.g.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Learning Rules\n", + "\n", + "### Example: Simplified STDP" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "class SimpleSTDP(brainstate.nn.Module):\n", + " \"\"\"Simplified STDP learning rule.\"\"\"\n", + "\n", + " def __init__(self, n_pre, n_post, A_plus=0.01, A_minus=0.01,\n", + " tau_plus=20*u.ms, tau_minus=20*u.ms):\n", + " super().__init__()\n", + "\n", + " self.A_plus = A_plus\n", + " self.A_minus = A_minus\n", + " self.tau_plus = tau_plus\n", + " self.tau_minus = tau_minus\n", + "\n", + " # Learnable weights\n", + " self.W = brainstate.ParamState(jnp.ones((n_pre, n_post)) * 0.5)\n", + "\n", + " # Eligibility traces\n", + " self.pre_trace = brainstate.ShortTermState(jnp.zeros(n_pre))\n", + " self.post_trace = brainstate.ShortTermState(jnp.zeros(n_post))\n", + "\n", + " def reset_state(self, batch_size=None):\n", + " shape_pre = self.W.value.shape[0] if batch_size is None else (batch_size, self.W.value.shape[0])\n", + " shape_post = self.W.value.shape[1] if batch_size is None else (batch_size, self.W.value.shape[1])\n", + " self.pre_trace.value = jnp.zeros(shape_pre)\n", + " self.post_trace.value = jnp.zeros(shape_post)\n", + "\n", + " def update(self, pre_spike, post_spike):\n", + " dt = brainstate.environ.get_dt()\n", + "\n", + " # Update traces\n", + " self.pre_trace.value += -self.pre_trace.value / self.tau_plus.to_decimal(u.ms) * dt.to_decimal(u.ms) + pre_spike\n", + " self.post_trace.value += -self.post_trace.value / self.tau_minus.to_decimal(u.ms) * dt.to_decimal(u.ms) + post_spike\n", + "\n", + " # Weight updates\n", + " # LTP: pre spike finds existing post trace\n", + " dw_ltp = self.A_plus * jnp.outer(pre_spike, self.post_trace.value)\n", + "\n", + " # LTD: post spike finds existing pre trace\n", + " dw_ltd = -self.A_minus * jnp.outer(self.pre_trace.value, post_spike)\n", + "\n", + " # Update weights\n", + " self.W.value = jnp.clip(self.W.value + dw_ltp + dw_ltd, 0, 1)\n", + "\n", + " return jnp.dot(pre_spike, self.W.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Network Architectures\n", + "\n", + "### Example: Liquid State Machine" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class LiquidStateMachine(brainstate.nn.Module):\n", + " \"\"\"Reservoir computing with spiking neurons.\"\"\"\n", + "\n", + " def __init__(self, n_input=100, n_reservoir=1000, n_output=10):\n", + " super().__init__()\n", + "\n", + " # Input projection (trainable)\n", + " self.input_weights = brainstate.ParamState(\n", + " brainstate.random.randn(n_input, n_reservoir) * 0.1\n", + " )\n", + "\n", + " # Reservoir (fixed random recurrent network)\n", + " self.reservoir = brainpy.state.LIF(n_reservoir, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + " # Fixed random recurrent weights\n", + " w_reservoir = brainstate.random.randn(n_reservoir, n_reservoir) * 0.01\n", + " mask = (brainstate.random.rand(n_reservoir, n_reservoir) < 0.1).astype(float)\n", + " self.reservoir_weights = w_reservoir * mask # Not a ParamState (fixed)\n", + "\n", + " # Readout (trainable)\n", + " self.readout = brainpy.state.Readout(n_reservoir, n_output)\n", + "\n", + " def update(self, x):\n", + " # Input to reservoir\n", + " reservoir_input = jnp.dot(x, self.input_weights.value) * u.nA\n", + "\n", + " # Reservoir recurrence\n", + " spk = self.reservoir.get_spike()\n", + " recurrent_input = jnp.dot(spk, self.reservoir_weights) * u.nA\n", + "\n", + " # Update reservoir\n", + " self.reservoir(reservoir_input + recurrent_input)\n", + "\n", + " # Readout from reservoir state\n", + " output = self.readout(self.reservoir.get_spike())\n", + "\n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Custom Input Encoders\n", + "\n", + "### Example: Temporal Contrast Encoder" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "class TemporalContrastEncoder(brainstate.nn.Module):\n", + " \"\"\"Encode images as spike timing based on contrast.\"\"\"\n", + "\n", + " def __init__(self, n_pixels, max_time=100, threshold=0.1):\n", + " super().__init__()\n", + " self.n_pixels = n_pixels\n", + " self.max_time = max_time\n", + " self.threshold = threshold\n", + "\n", + " def encode(self, image):\n", + " \"\"\"Convert image to spike timing.\n", + "\n", + " Args:\n", + " image: Array of pixel values [0, 1]\n", + "\n", + " Returns:\n", + " spike_times: When each pixel spikes (or max_time if no spike)\n", + " \"\"\"\n", + " # Higher intensity → earlier spike\n", + " spike_times = jnp.where(\n", + " image > self.threshold,\n", + " self.max_time * (1 - image), # Invert: bright pixels spike early\n", + " self.max_time # Below threshold: no spike\n", + " )\n", + "\n", + " return spike_times\n", + "\n", + " def decode_to_spikes(self, spike_times, current_time):\n", + " \"\"\"Get spikes at current simulation time.\"\"\"\n", + " spikes = (spike_times == current_time).astype(float)\n", + " return spikes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "✅ **Inherit from base classes**\n", + " - `brainpy.state.Neuron` for neurons\n", + " - `brainpy.state.Synapse` for synapses\n", + " - `brainstate.nn.Module` for general components\n", + "\n", + "✅ **Use ShortTermState for dynamics**\n", + " - Reset each trial\n", + " - Temporary variables\n", + "\n", + "✅ **Use ParamState for learnable parameters**\n", + " - Trained by optimizers\n", + " - Saved in checkpoints\n", + "\n", + "✅ **Implement reset_state()**\n", + " - Handle batch_size parameter\n", + " - Initialize all ShortTermStates\n", + "\n", + "✅ **Use physical units**\n", + " - All parameters with `brainunit`\n", + " - Convert for computation with `.to_decimal()`\n", + "\n", + "✅ **Follow naming conventions**\n", + " - `V` for voltage\n", + " - `spike` for spike indicator\n", + " - `g` for conductance\n", + " - `w` for weights" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing Custom Components" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def test_custom_neuron():\n", + " \"\"\"Test custom neuron implementation.\"\"\"\n", + "\n", + " neuron = CustomNeuron(size=10)\n", + " brainstate.nn.init_all_states(neuron)\n", + "\n", + " # Test 1: Initialization\n", + " assert neuron.V.value.shape == (10,)\n", + " assert jnp.all(neuron.V.value == 0)\n", + "\n", + " # Test 2: Response to input\n", + " strong_input = jnp.ones(10) * 10.0 * u.nA\n", + " for _ in range(100):\n", + " neuron(strong_input)\n", + "\n", + " spike_count = jnp.sum(neuron.spike.value)\n", + " assert spike_count > 0, \"Neuron should spike with strong input\"\n", + "\n", + " # Test 3: Batch dimension\n", + " brainstate.nn.init_all_states(neuron, batch_size=5)\n", + " assert neuron.V.value.shape == (5, 10)\n", + "\n", + " print(\"✅ Custom neuron tests passed\")\n", + "\n", + "test_custom_neuron()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Complete Example\n", + "\n", + "**Putting it all together:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Custom components\n", + "class MyNeuron(brainpy.state.Neuron):\n", + " # ... (see examples above)\n", + " pass\n", + "\n", + "class MySynapse(brainpy.state.Synapse):\n", + " # ... (see examples above)\n", + " pass\n", + "\n", + "# Use in network\n", + "class CustomNetwork(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + "\n", + " self.pre = MyNeuron(size=100)\n", + " self.post = MyNeuron(size=50)\n", + "\n", + " self.projection = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 50, prob=0.1, weight=0.5*u.mS),\n", + " syn=MySynapse.desc(50), # Use custom synapse\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=self.post\n", + " )\n", + "\n", + " def update(self, inp):\n", + " spk_pre = self.pre.get_spike()\n", + " self.projection(spk_pre)\n", + " self.pre(inp)\n", + " self.post(0*u.nA)\n", + " return self.post.get_spike()\n", + "\n", + "# Use network\n", + "net = CustomNetwork()\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "for _ in range(100):\n", + " output = net(input_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "**Component creation checklist:**\n", + "\n", + "```python\n", + "✅ Inherit from brainpy.state.Neuron, brainpy.state.Synapse, or brainstate.nn.Module\n", + "✅ Define __init__ with parameters\n", + "✅ Create states (ShortTermState or ParamState)\n", + "✅ Implement reset_state(batch_size=None)\n", + "✅ Implement update() method\n", + "✅ Use physical units throughout\n", + "✅ Test with different batch sizes\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## See Also\n", + "\n", + "- Core Concepts: State Management\n", + "- Core Concepts: Neurons\n", + "- Core Concepts: Synapses\n", + "- Tutorials: Synaptic Plasticity" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/tutorials/how-to-guides/debugging-networks.ipynb b/docs_state/tutorials/how-to-guides/debugging-networks.ipynb new file mode 100644 index 00000000..f53f025e --- /dev/null +++ b/docs_state/tutorials/how-to-guides/debugging-networks.ipynb @@ -0,0 +1,1144 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Debug Networks\n", + "\n", + "This guide shows you how to identify and fix common issues when developing neural networks with BrainPy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick Diagnostic Checklist\n", + "\n", + "When your network isn't working, check these first:\n", + "\n", + "**☐ Is the network receiving input?** \n", + " Print input values, check shapes\n", + "\n", + "**☐ Are neurons firing?** \n", + " Count spikes, check spike rates\n", + "\n", + "**☐ Are projections working?** \n", + " Verify connectivity, check weights\n", + "\n", + "**☐ Is update order correct?** \n", + " Get spikes BEFORE updating neurons\n", + "\n", + "**☐ Are states initialized?** \n", + " Call `brainstate.nn.init_all_states()`\n", + "\n", + "**☐ Are units correct?** \n", + " All values need physical units (mV, nA, ms)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Common Issues and Solutions\n", + "\n", + "### Issue 1: No Spikes / Silent Network\n", + "\n", + "**Symptoms:**\n", + "\n", + "- Network produces no spikes\n", + "- All neurons stay at rest potential\n", + "\n", + "**Diagnosis:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import brainunit as u\n", + "import jax.numpy as jnp\n", + "\n", + "neuron = brainpy.state.LIF(100, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "brainstate.nn.init_all_states(neuron)\n", + "\n", + "# Check 1: Is input being provided?\n", + "inp = brainstate.random.rand(100) * 5.0 * u.nA\n", + "print(\"Input range:\", inp.min(), \"to\", inp.max())\n", + "\n", + "# Check 2: Are neurons updating?\n", + "V_before = neuron.V.value.copy()\n", + "neuron(inp)\n", + "V_after = neuron.V.value\n", + "print(\"Voltage changed:\", not jnp.allclose(V_before, V_after))\n", + "\n", + "# Check 3: Are any neurons near threshold?\n", + "print(\"Max voltage:\", V_after.max())\n", + "print(\"Threshold:\", neuron.V_th.to_decimal(u.mV))\n", + "print(\"Neurons above -55mV:\", jnp.sum(V_after > -55))\n", + "\n", + "# Check 4: Count spikes\n", + "for i in range(100):\n", + " neuron(inp)\n", + "spike_count = jnp.sum(neuron.spike.value)\n", + "print(f\"Spikes in 100 steps: {spike_count}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Common Causes:**\n", + "\n", + "1. **Input too weak:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Too weak\n", + "inp = brainstate.random.rand(100) * 0.1 * u.nA # Not enough!\n", + "\n", + "# Better\n", + "inp = brainstate.random.rand(100) * 5.0 * u.nA # Stronger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Threshold too high:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check threshold\n", + "neuron = brainpy.state.LIF(100, V_th=-40*u.mV, ...) # Harder to spike\n", + "neuron = brainpy.state.LIF(100, V_th=-50*u.mV, ...) # Easier to spike" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **Time constant too large:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Slow integration\n", + "neuron = brainpy.state.LIF(100, tau=100*u.ms, ...) # Very slow\n", + "\n", + "# Faster\n", + "neuron = brainpy.state.LIF(100, tau=10*u.ms, ...) # Normal speed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. **Missing initialization:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "neuron = brainpy.state.LIF(100, ...)\n", + "# MUST initialize!\n", + "brainstate.nn.init_all_states(neuron)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue 2: Runaway Activity / Explosion\n", + "\n", + "**Symptoms:**\n", + "\n", + "- All neurons fire constantly\n", + "- Membrane potentials go to infinity\n", + "- NaN values appear\n", + "\n", + "**Diagnosis:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Check for NaN\n", + "if jnp.any(jnp.isnan(neuron.V.value)):\n", + " print(\"❌ NaN detected in membrane potential!\")\n", + "\n", + "# Check for explosion\n", + "if jnp.any(jnp.abs(neuron.V.value) > 1000):\n", + " print(\"❌ Membrane potential exploded!\")\n", + "\n", + "# Check spike rate\n", + "spike_rate = jnp.mean(neuron.spike.value)\n", + "print(f\"Spike rate: {spike_rate*100:.1f}%\")\n", + "if spike_rate > 0.5:\n", + " print(\"⚠️ More than 50% of neurons firing every step!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Common Causes:**\n", + "\n", + "1. **Excitation-Inhibition imbalance:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Imbalanced (explosion!)\n", + "w_exc = 5.0 * u.mS # Too strong\n", + "w_inh = 1.0 * u.mS # Too weak\n", + "\n", + "# Balanced\n", + "w_exc = 0.5 * u.mS\n", + "w_inh = 5.0 * u.mS # Inhibition ~10× stronger" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Positive feedback loop:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Check recurrent excitation\n", + "# E → E with no inhibition can explode\n", + "\n", + "# Add inhibition\n", + "class BalancedNetwork(brainstate.nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.E = brainpy.state.LIF(800, ...)\n", + " self.I = brainpy.state.LIF(200, ...)\n", + "\n", + " self.E2E = ... # Excitatory recurrence\n", + " self.I2E = ... # MUST have inhibition!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **Time step too large:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Unstable\n", + "brainstate.environ.set(dt=1.0 * u.ms) # Too large\n", + "\n", + "# Stable\n", + "brainstate.environ.set(dt=0.1 * u.ms) # Standard" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. **Wrong reversal potentials:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# WRONG: Inhibition with excitatory reversal\n", + "out_inh = brainpy.state.COBA.desc(E=0*u.mV) # Should be negative!\n", + "\n", + "# CORRECT\n", + "out_exc = brainpy.state.COBA.desc(E=0*u.mV) # Excitation\n", + "out_inh = brainpy.state.COBA.desc(E=-80*u.mV) # Inhibition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue 3: Spikes Not Propagating\n", + "\n", + "**Symptoms:**\n", + "\n", + "- Presynaptic neurons spike\n", + "- Postsynaptic neurons don't respond\n", + "- Projection seems inactive\n", + "\n", + "**Diagnosis:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create simple network\n", + "pre = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "post = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + "proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(10, 10, prob=0.5, weight=2.0*u.mS),\n", + " syn=brainpy.state.Expon.desc(10, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post\n", + ")\n", + "\n", + "brainstate.nn.init_all_states([pre, post, proj])\n", + "\n", + "# Diagnosis\n", + "for i in range(10):\n", + " # CRITICAL: Get spikes BEFORE update\n", + " pre_spikes = pre.get_spike()\n", + "\n", + " # Strong input to pre\n", + " pre(brainstate.random.rand(10) * 10.0 * u.nA)\n", + "\n", + " # Check: Did pre spike?\n", + " if jnp.sum(pre_spikes) > 0:\n", + " print(f\"Step {i}: {jnp.sum(pre_spikes)} presynaptic spikes\")\n", + "\n", + " # Update projection\n", + " proj(pre_spikes)\n", + "\n", + " # Check: Did projection produce current?\n", + " print(f\" Synaptic conductance: {proj.syn.g.value.max():.4f}\")\n", + "\n", + " # Update post\n", + " post(0*u.nA) # Only synaptic input\n", + "\n", + " # Check: Did post spike?\n", + " post_spikes = post.get_spike()\n", + " print(f\" {jnp.sum(post_spikes)} postsynaptic spikes\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Common Causes:**\n", + "\n", + "1. **Wrong spike timing:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# WRONG: Spikes from current step\n", + "pre(inp) # Update first\n", + "spikes = pre.get_spike() # These are NEW spikes\n", + "proj(spikes) # But projection needs OLD spikes!\n", + "\n", + "# CORRECT: Spikes from previous step\n", + "spikes = pre.get_spike() # Get OLD spikes first\n", + "proj(spikes) # Update projection\n", + "pre(inp) # Then update neurons" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Weak connectivity:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Too sparse\n", + "comm = brainstate.nn.EventFixedProb(..., prob=0.01, weight=0.1*u.mS)\n", + "\n", + "# Stronger\n", + "comm = brainstate.nn.EventFixedProb(..., prob=0.1, weight=1.0*u.mS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. **Missing projection update:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Forgot to call projection!\n", + "spk = pre.get_spike()\n", + "# proj(spk) <- MISSING!\n", + "post(0*u.nA)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. **Wrong postsynaptic target:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Wrong target\n", + "proj = brainpy.state.AlignPostProj(..., post=wrong_population)\n", + "\n", + "# Correct target\n", + "proj = brainpy.state.AlignPostProj(..., post=correct_population)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Issue 4: Shape Mismatch Errors\n", + "\n", + "**Symptoms:**\n", + "\n", + "```\n", + "ValueError: operands could not be broadcast together\n", + "with shapes (100,) (64, 100)\n", + "```\n", + "\n", + "**Common Causes:**\n", + "\n", + "1. **Batch dimension mismatch:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Network initialized with batch\n", + "brainstate.nn.init_all_states(net, batch_size=64)\n", + "# States shape: (64, 100)\n", + "\n", + "# But input has no batch\n", + "inp = jnp.zeros(100) # Shape: (100,) - WRONG!\n", + "\n", + "# Fix: Add batch dimension\n", + "inp = jnp.zeros((64, 100)) # Shape: (64, 100) - CORRECT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. **Forgot batch in initialization:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialized without batch\n", + "brainstate.nn.init_all_states(net) # Shape: (100,)\n", + "\n", + "# But providing batched input\n", + "inp = jnp.zeros((64, 100)) # Shape: (64, 100)\n", + "\n", + "# Fix: Initialize with batch\n", + "brainstate.nn.init_all_states(net, batch_size=64)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Debug shape mismatches:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Input shape: {inp.shape}\")\n", + "print(f\"Network state shape: {net.neurons.V.value.shape}\")\n", + "print(f\"Expected: Both should have same batch dimension\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inspection Tools\n", + "\n", + "### Print State Values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Inspect neuron states\n", + "neuron = brainpy.state.LIF(10, ...)\n", + "brainstate.nn.init_all_states(neuron)\n", + "\n", + "print(\"Membrane potentials:\", neuron.V.value)\n", + "print(\"Spikes:\", neuron.spike.value)\n", + "print(\"Shape:\", neuron.V.value.shape)\n", + "\n", + "# Statistics\n", + "print(f\"V range: [{neuron.V.value.min():.2f}, {neuron.V.value.max():.2f}]\")\n", + "print(f\"V mean: {neuron.V.value.mean():.2f}\")\n", + "print(f\"Spike count: {jnp.sum(neuron.spike.value)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize Activity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Record activity\n", + "n_steps = 1000\n", + "V_history = []\n", + "spike_history = []\n", + "\n", + "for i in range(n_steps):\n", + " neuron(inp)\n", + " V_history.append(neuron.V.value.copy())\n", + " spike_history.append(neuron.spike.value.copy())\n", + "\n", + "V_history = jnp.array(V_history)\n", + "spike_history = jnp.array(spike_history)\n", + "\n", + "# Plot membrane potential\n", + "plt.figure(figsize=(12, 4))\n", + "plt.plot(V_history[:, 0]) # First neuron\n", + "plt.xlabel('Time step')\n", + "plt.ylabel('Membrane Potential (mV)')\n", + "plt.title('Neuron 0 Membrane Potential')\n", + "plt.show()\n", + "\n", + "# Plot raster\n", + "plt.figure(figsize=(12, 6))\n", + "times, neurons = jnp.where(spike_history > 0)\n", + "plt.scatter(times, neurons, s=1, c='black')\n", + "plt.xlabel('Time step')\n", + "plt.ylabel('Neuron index')\n", + "plt.title('Spike Raster')\n", + "plt.show()\n", + "\n", + "# Firing rate over time\n", + "plt.figure(figsize=(12, 4))\n", + "firing_rate = jnp.mean(spike_history, axis=1) * 1000 / 0.1 # Hz\n", + "plt.plot(firing_rate)\n", + "plt.xlabel('Time step')\n", + "plt.ylabel('Population Rate (Hz)')\n", + "plt.title('Population Firing Rate')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Check Connectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For sparse projections\n", + "proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(100, 50, prob=0.1, weight=0.5*u.mS),\n", + " syn=brainpy.state.Expon.desc(50, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post_neurons\n", + ")\n", + "\n", + "# Check connection count\n", + "print(f\"Expected connections: {100 * 50 * 0.1:.0f}\")\n", + "# Note: Actual connectivity may vary due to randomness\n", + "\n", + "# Check weights\n", + "# (Accessing internal connectivity structure depends on implementation)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Monitor Training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Track loss and metrics\n", + "train_losses = []\n", + "val_accuracies = []\n", + "\n", + "for epoch in range(num_epochs):\n", + " epoch_losses = []\n", + "\n", + " for batch in train_loader:\n", + " loss = train_step(net, batch)\n", + " epoch_losses.append(float(loss))\n", + "\n", + " avg_loss = np.mean(epoch_losses)\n", + " train_losses.append(avg_loss)\n", + "\n", + " # Validation\n", + " val_acc = evaluate(net, val_loader)\n", + " val_accuracies.append(val_acc)\n", + "\n", + " print(f\"Epoch {epoch}: Loss={avg_loss:.4f}, Val Acc={val_acc:.2%}\")\n", + "\n", + " # Check for issues\n", + " if np.isnan(avg_loss):\n", + " print(\"❌ NaN loss! Stopping training.\")\n", + " break\n", + "\n", + " if avg_loss > 10 * train_losses[0]:\n", + " print(\"⚠️ Loss exploding!\")\n", + "\n", + "# Plot training curves\n", + "plt.figure(figsize=(12, 4))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(train_losses)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Loss')\n", + "plt.title('Training Loss')\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(val_accuracies)\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "plt.title('Validation Accuracy')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Debugging\n", + "\n", + "### Gradient Checking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import braintools\n", + "\n", + "# Check if gradients are being computed\n", + "params = net.states(brainstate.ParamState)\n", + "\n", + "grads = brainstate.transform.grad(\n", + " loss_fn,\n", + " params,\n", + " return_value=True\n", + ")(net, X, y)\n", + "\n", + "# Inspect gradients\n", + "for name, grad in grads.items():\n", + " grad_norm = jnp.linalg.norm(grad.value.flatten())\n", + " print(f\"{name}: gradient norm = {grad_norm:.6f}\")\n", + "\n", + " if jnp.any(jnp.isnan(grad.value)):\n", + " print(f\" ❌ NaN in gradient!\")\n", + "\n", + " if grad_norm == 0:\n", + " print(f\" ⚠️ Zero gradient - parameter not learning\")\n", + "\n", + " if grad_norm > 1000:\n", + " print(f\" ⚠️ Exploding gradient!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Trace Execution" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def debug_step(net, inp):\n", + " \"\"\"Instrumented simulation step.\"\"\"\n", + " print(f\"\\n--- Step Start ---\")\n", + "\n", + " # Before\n", + " print(f\"Input range: [{inp.min():.2f}, {inp.max():.2f}]\")\n", + " print(f\"V before: [{net.neurons.V.value.min():.2f}, {net.neurons.V.value.max():.2f}]\")\n", + "\n", + " # Execute\n", + " output = net(inp)\n", + "\n", + " # After\n", + " print(f\"V after: [{net.neurons.V.value.min():.2f}, {net.neurons.V.value.max():.2f}]\")\n", + " print(f\"Spikes: {jnp.sum(net.neurons.spike.value)}\")\n", + " print(f\"Output range: [{output.min():.2f}, {output.max():.2f}]\")\n", + "\n", + " # Checks\n", + " if jnp.any(jnp.isnan(net.neurons.V.value)):\n", + " print(\"❌ NaN detected!\")\n", + " import pdb; pdb.set_trace() # Drop into debugger\n", + "\n", + " print(f\"--- Step End ---\\n\")\n", + " return output\n", + "\n", + "# Use for debugging\n", + "for i in range(10):\n", + " output = debug_step(net, input_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Assertion Checks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class SafeNetwork(brainstate.nn.Module):\n", + " \"\"\"Network with built-in checks.\"\"\"\n", + "\n", + " def __init__(self, n_neurons=100):\n", + " super().__init__()\n", + " self.neurons = brainpy.state.LIF(n_neurons, ...)\n", + "\n", + " def update(self, inp):\n", + " # Pre-checks\n", + " assert inp.shape[-1] == 100, f\"Wrong input size: {inp.shape}\"\n", + " assert not jnp.any(jnp.isnan(inp)), \"NaN in input!\"\n", + " assert not jnp.any(jnp.isinf(inp)), \"Inf in input!\"\n", + "\n", + " # Execute\n", + " self.neurons(inp)\n", + " output = self.neurons.get_spike()\n", + "\n", + " # Post-checks\n", + " assert not jnp.any(jnp.isnan(self.neurons.V.value)), \"NaN in membrane potential!\"\n", + " assert jnp.all(jnp.abs(self.neurons.V.value) < 1000), \"Voltage explosion!\"\n", + "\n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Unit Testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def test_neuron_spikes():\n", + " \"\"\"Test that neuron spikes with strong input.\"\"\"\n", + " neuron = brainpy.state.LIF(1, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " brainstate.nn.init_all_states(neuron)\n", + "\n", + " # Strong constant input should cause spiking\n", + " strong_input = jnp.array([20.0]) * u.nA\n", + "\n", + " spike_count = 0\n", + " for _ in range(100):\n", + " neuron(strong_input)\n", + " spike_count += int(neuron.spike.value[0])\n", + "\n", + " assert spike_count > 0, \"Neuron didn't spike with strong input!\"\n", + " assert spike_count < 100, \"Neuron spiked every step (check reset!)\"\n", + "\n", + " print(f\"✅ Neuron test passed ({spike_count} spikes)\")\n", + "\n", + "def test_projection():\n", + " \"\"\"Test that projection propagates spikes.\"\"\"\n", + " pre = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " post = brainpy.state.LIF(10, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + " proj = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(10, 10, prob=1.0, weight=5.0*u.mS), # 100% connectivity\n", + " syn=brainpy.state.Expon.desc(10, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(),\n", + " post=post\n", + " )\n", + "\n", + " brainstate.nn.init_all_states([pre, post, proj])\n", + "\n", + " # Make pre spike\n", + " pre(jnp.ones(10) * 20.0 * u.nA)\n", + "\n", + " # Projection should activate\n", + " spk = pre.get_spike()\n", + " assert jnp.sum(spk) > 0, \"Pre didn't spike!\"\n", + "\n", + " proj(spk)\n", + "\n", + " # Check synaptic conductance increased\n", + " assert proj.syn.g.value.max() > 0, \"Synapse didn't activate!\"\n", + "\n", + " print(\"✅ Projection test passed\")\n", + "\n", + "# Run tests\n", + "test_neuron_spikes()\n", + "test_projection()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Debugging Checklist\n", + "\n", + "When your network doesn't work:\n", + "\n", + "**1. Check Initialization**\n", + "\n", + "```python\n", + "☐ Called brainstate.nn.init_all_states()?\n", + "☐ Correct batch_size parameter?\n", + "☐ All submodules initialized?\n", + "```\n", + "\n", + "**2. Check Input**\n", + "\n", + "```python\n", + "☐ Input shape matches network?\n", + "☐ Input has units (nA, mV, etc.)?\n", + "☐ Input magnitude reasonable?\n", + "☐ Input not all zeros?\n", + "```\n", + "\n", + "**3. Check Neurons**\n", + "\n", + "```python\n", + "☐ Threshold reasonable (e.g., -50 mV)?\n", + "☐ Reset potential below threshold?\n", + "☐ Time constant reasonable (5-20 ms)?\n", + "☐ Neurons actually spiking?\n", + "```\n", + "\n", + "**4. Check Projections**\n", + "\n", + "```python\n", + "☐ Connectivity probability > 0?\n", + "☐ Weights reasonable magnitude?\n", + "☐ Correct update order (spikes before update)?\n", + "☐ Projection actually called?\n", + "```\n", + "\n", + "**5. Check Balance**\n", + "\n", + "```python\n", + "☐ Inhibition stronger than excitation (~10×)?\n", + "☐ Reversal potentials correct (E=0, I=-80)?\n", + "☐ E/I ratio appropriate (4:1)?\n", + "```\n", + "\n", + "**6. Check Training**\n", + "\n", + "```python\n", + "☐ Loss decreasing?\n", + "☐ Gradients non-zero?\n", + "☐ No NaN in gradients?\n", + "☐ Learning rate appropriate?\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Common Error Messages\n", + "\n", + "### \"operands could not be broadcast\"\n", + "\n", + "**Meaning:** Shape mismatch\n", + "\n", + "**Fix:** Check batch dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Shapes: {x.shape} vs {y.shape}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"RESOURCE_EXHAUSTED: Out of memory\"\n", + "\n", + "**Meaning:** GPU/CPU memory full\n", + "\n", + "**Fix:** Reduce batch size or network size" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reduce batch\n", + "brainstate.nn.init_all_states(net, batch_size=16) # Instead of 64" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"Concrete value required\"\n", + "\n", + "**Meaning:** JIT can't handle dynamic values\n", + "\n", + "**Fix:** Use static shapes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Dynamic (bad for JIT)\n", + "n = len(data) # Changes each call\n", + "\n", + "# Static (good for JIT)\n", + "n = 100 # Fixed value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### \"Invalid device\"\n", + "\n", + "**Meaning:** Trying to use unavailable device\n", + "\n", + "**Fix:** Check available devices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "print(jax.devices())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "✅ **Test small first** - Debug with 10 neurons before scaling to 10,000\n", + "\n", + "✅ **Visualize early** - Plot activity to see problems immediately\n", + "\n", + "✅ **Check incrementally** - Test each component before combining\n", + "\n", + "✅ **Use assertions** - Catch problems early with runtime checks\n", + "\n", + "✅ **Print liberally** - Add diagnostic prints during development\n", + "\n", + "✅ **Keep backups** - Save working versions before major changes\n", + "\n", + "✅ **Start simple** - Begin with minimal network, add complexity gradually\n", + "\n", + "✅ **Write tests** - Unit test individual components\n", + "\n", + "❌ **Don't debug by guessing** - Use systematic diagnosis\n", + "\n", + "❌ **Don't skip initialization** - Always call init_all_states\n", + "\n", + "❌ **Don't ignore warnings** - They often indicate real problems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "**Debugging workflow:**\n", + "\n", + "1. **Identify symptom** (no spikes, explosion, etc.)\n", + "2. **Isolate component** (neurons, projections, input)\n", + "3. **Inspect state** (print values, plot activity)\n", + "4. **Form hypothesis** (what might be wrong?)\n", + "5. **Test fix** (make one change at a time)\n", + "6. **Verify** (ensure problem solved)\n", + "\n", + "**Quick diagnostic code:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Comprehensive diagnostic\n", + "def diagnose_network(net, inp):\n", + " print(\"=== Network Diagnostic ===\")\n", + "\n", + " # Input\n", + " print(f\"Input shape: {inp.shape}\")\n", + " print(f\"Input range: [{inp.min():.2f}, {inp.max():.2f}]\")\n", + "\n", + " # States\n", + " if hasattr(net, 'neurons'):\n", + " V = net.neurons.V.value\n", + " print(f\"Voltage shape: {V.shape}\")\n", + " print(f\"Voltage range: [{V.min():.2f}, {V.max():.2f}]\")\n", + "\n", + " # Simulation\n", + " output = net(inp)\n", + "\n", + " # Results\n", + " if hasattr(net, 'neurons'):\n", + " spk_count = jnp.sum(net.neurons.spike.value)\n", + " print(f\"Spikes: {spk_count}\")\n", + "\n", + " print(f\"Output shape: {output.shape}\")\n", + " print(f\"Output range: [{output.min():.2f}, {output.max():.2f}]\")\n", + "\n", + " # Checks\n", + " if jnp.any(jnp.isnan(output)):\n", + " print(\"❌ NaN in output!\")\n", + " if jnp.all(output == 0):\n", + " print(\"⚠️ Output all zeros!\")\n", + "\n", + " print(\"=========================\")\n", + " return output" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## See Also\n", + "\n", + "- Core Concepts: State Management\n", + "- Core Concepts: Projections\n", + "- Performance Optimization" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Ecosystem-py", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/how-to-guides/index.rst b/docs_state/tutorials/how-to-guides/index.rst similarity index 87% rename from docs_state/how-to-guides/index.rst rename to docs_state/tutorials/how-to-guides/index.rst index 7526c311..cf314b92 100644 --- a/docs_state/how-to-guides/index.rst +++ b/docs_state/tutorials/how-to-guides/index.rst @@ -34,8 +34,8 @@ Practical guides for common tasks in BrainPy. :hidden: :maxdepth: 1 - save-load-models.rst - gpu-tpu-usage.rst - debugging-networks.rst - performance-optimization.rst - custom-components.rst + save-load-models.ipynb + gpu-tpu-usage.ipynb + debugging-networks.ipynb + performance-optimization.ipynb + custom-components.ipynb diff --git a/docs_state/tutorials/how-to-guides/performance-optimization.ipynb b/docs_state/tutorials/how-to-guides/performance-optimization.ipynb new file mode 100644 index 00000000..eda7b7ed --- /dev/null +++ b/docs_state/tutorials/how-to-guides/performance-optimization.ipynb @@ -0,0 +1,507 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Optimize Performance\n", + "\n", + "This guide shows you how to make your BrainPy simulations run faster." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick Wins\n", + "\n", + "**Top 5 optimizations (80% of speedup):**\n", + "\n", + "1. ✅ **Use JIT compilation** - 10-100× speedup\n", + "2. ✅ **Use sparse connectivity** - 10-100× memory reduction\n", + "3. ✅ **Batch operations** - 2-10× speedup on GPU\n", + "4. ✅ **Use GPU/TPU** - 10-100× speedup for large networks\n", + "5. ✅ **Minimize Python loops** - Use JAX operations instead" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## JIT Compilation\n", + "\n", + "**Essential for performance!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainstate\n", + "\n", + "# Slow (no JIT)\n", + "def slow_step(net, inp):\n", + " return net(inp)\n", + "\n", + "# Fast (with JIT)\n", + "@brainstate.transform.jit\n", + "def fast_step(net, inp):\n", + " return net(inp)\n", + "\n", + "# Warmup (compilation)\n", + "_ = fast_step(net, inp)\n", + "\n", + "# 10-100× faster than slow_step\n", + "output = fast_step(net, inp)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Rules for JIT:**\n", + "- Static shapes (no dynamic array sizes)\n", + "- Pure functions (no side effects)\n", + "- Avoid Python loops over data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sparse Connectivity\n", + "\n", + "**Biological networks are sparse (~1-10% connectivity)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Dense: 10,000 × 10,000 = 100M connections (400MB)\n", + "comm_dense = brainstate.nn.Linear(10000, 10000)\n", + "\n", + "# Sparse: 10,000 × 10,000 × 0.01 = 1M connections (4MB)\n", + "comm_sparse = brainstate.nn.EventFixedProb(\n", + " 10000, 10000,\n", + " prob=0.01, # 1% connectivity\n", + " weight=0.5*u.mS\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Memory savings:** 100× for 1% connectivity" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batching\n", + "\n", + "**Process multiple trials in parallel:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Sequential: 10 trials one by one\n", + "for trial in range(10):\n", + " brainstate.nn.init_all_states(net)\n", + " run_trial(net)\n", + "\n", + "# Parallel: 10 trials simultaneously\n", + "brainstate.nn.init_all_states(net, batch_size=10)\n", + "run_batched(net) # 5-10× faster on GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Optimal batch sizes:**\n", + "- CPU: 1-16\n", + "- GPU: 32-256\n", + "- TPU: 128-512" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## GPU Usage\n", + "\n", + "**Automatic when available:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "print(jax.devices()) # Check for GPU\n", + "\n", + "# BrainPy automatically uses GPU\n", + "net = brainpy.state.LIF(10000, ...)\n", + "# Runs on GPU if available" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**See:** GPU/TPU Usage guide for details" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Avoid Python Loops\n", + "\n", + "**Replace Python loops with JAX operations:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# SLOW: Python loop\n", + "result = []\n", + "for i in range(1000):\n", + " result.append(net(inp))\n", + "\n", + "# FAST: JAX loop\n", + "def body_fun(i):\n", + " return net(inp)\n", + "\n", + "results = brainstate.transform.for_loop(body_fun, jnp.arange(1000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use Appropriate Precision\n", + "\n", + "**Float32 is usually sufficient:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Default (float32) - fast\n", + "weights = jnp.ones((1000, 1000)) # 4 bytes/element\n", + "\n", + "# Float64 - 2× slower, 2× memory\n", + "weights = jnp.ones((1000, 1000), dtype=jnp.float64) # 8 bytes/element" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Minimize State Storage\n", + "\n", + "**Don't accumulate history:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# BAD: Stores all history in Python list\n", + "history = []\n", + "for t in range(10000):\n", + " output = net(inp)\n", + " history.append(output) # Memory leak!\n", + "\n", + "# GOOD: Process on the fly\n", + "for t in range(10000):\n", + " output = net(inp)\n", + " metrics = compute_metrics(output) # Don't store raw data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimize Network Architecture\n", + "\n", + "**1. Use simpler neuron models when possible:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Complex (slow but realistic)\n", + "neuron = brainpy.state.HH(1000, ...) # Hodgkin-Huxley\n", + "\n", + "# Simple (fast)\n", + "neuron = brainpy.state.LIF(1000, ...) # Leaky Integrate-and-Fire" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**2. Use CUBA instead of COBA when possible:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Slower (conductance-based)\n", + "out = brainpy.state.COBA.desc(E=0*u.mV)\n", + "\n", + "# Faster (current-based)\n", + "out = brainpy.state.CUBA.desc()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**3. Reduce connectivity:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Dense\n", + "prob = 0.1 # 10% connectivity\n", + "\n", + "# Sparse\n", + "prob = 0.02 # 2% connectivity (5× fewer connections)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Profile Before Optimizing\n", + "\n", + "**Identify actual bottlenecks:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "\n", + "# Time different components\n", + "start = time.time()\n", + "for _ in range(100):\n", + " net(inp)\n", + "print(f\"Network update: {time.time() - start:.2f}s\")\n", + "\n", + "start = time.time()\n", + "for _ in range(100):\n", + " output = process_output(net.get_spike())\n", + "print(f\"Output processing: {time.time() - start:.2f}s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Don't optimize blindly - measure first!**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Performance Checklist\n", + "\n", + "**For maximum performance:**\n", + "\n", + "```python\n", + "✅ JIT compiled (@brainstate.transform.jit)\n", + "✅ Sparse connectivity (EventFixedProb with prob < 0.1)\n", + "✅ Batched (batch_size ≥ 32 on GPU)\n", + "✅ GPU enabled (check jax.devices())\n", + "✅ Static shapes (no dynamic array sizes)\n", + "✅ Minimal history storage\n", + "✅ Appropriate neuron models (LIF vs HH)\n", + "✅ Float32 precision\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Common Bottlenecks\n", + "\n", + "**Issue 1: First run very slow** \n", + " → JIT compilation happens on first call (warmup)\n", + "\n", + "**Issue 2: CPU-GPU transfers** \n", + " → Keep data on GPU between operations\n", + "\n", + "**Issue 3: Small batch sizes** \n", + " → Increase batch_size for better GPU utilization\n", + "\n", + "**Issue 4: Python loops** \n", + " → Replace with JAX operations (for_loop, vmap)\n", + "\n", + "**Issue 5: Dense connectivity** \n", + " → Use sparse (EventFixedProb) for large networks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Complete Optimization Example" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainstate\n", + "import brainunit as u\n", + "import jax\n", + "\n", + "# Optimized network\n", + "class OptimizedNetwork(brainstate.nn.Module):\n", + " def __init__(self, n_neurons=10000):\n", + " super().__init__()\n", + "\n", + " # Simple neuron model\n", + " self.neurons = brainpy.state.LIF(n_neurons, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + "\n", + " # Sparse connectivity\n", + " self.recurrent = brainpy.state.AlignPostProj(\n", + " comm=brainstate.nn.EventFixedProb(\n", + " n_neurons, n_neurons,\n", + " prob=0.01, # Sparse!\n", + " weight=0.5*u.mS\n", + " ),\n", + " syn=brainpy.state.Expon.desc(n_neurons, tau=5*u.ms),\n", + " out=brainpy.state.CUBA.desc(), # Simple output\n", + " post=self.neurons\n", + " )\n", + "\n", + " def update(self, inp):\n", + " spk = self.neurons.get_spike()\n", + " self.recurrent(spk)\n", + " self.neurons(inp)\n", + " return spk\n", + "\n", + "# Initialize\n", + "net = OptimizedNetwork()\n", + "brainstate.nn.init_all_states(net, batch_size=64) # Batched\n", + "\n", + "# JIT compile\n", + "@brainstate.transform.jit\n", + "def simulate_step(net, inp):\n", + " return net(inp)\n", + "\n", + "# Warmup\n", + "inp = brainstate.random.rand(64, 10000) * 2.0 * u.nA\n", + "_ = simulate_step(net, inp)\n", + "\n", + "# Fast simulation\n", + "import time\n", + "start = time.time()\n", + "for _ in range(1000):\n", + " output = simulate_step(net, inp)\n", + "elapsed = time.time() - start\n", + "\n", + "print(f\"Optimized: {1000/elapsed:.1f} steps/s\")\n", + "print(f\"Throughput: {64*1000/elapsed:.1f} trials/s\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Benchmark Results\n", + "\n", + "**Typical speedups from optimization:**\n", + "\n", + "| Optimization | Speedup | Cumulative |\n", + "|--------------|---------|------------|\n", + "| Baseline (Python loops, dense) | 1× | 1× |\n", + "| + JIT compilation | 10-50× | 10-50× |\n", + "| + Sparse connectivity | 2-10× | 20-500× |\n", + "| + GPU | 5-20× | 100-10,000× |\n", + "| + Batching | 2-5× | 200-50,000× |\n", + "\n", + "**Real example:** 10,000 neuron network\n", + "- Baseline (CPU, no JIT): 0.5 steps/s\n", + "- Optimized (GPU, JIT, sparse, batched): 5,000 steps/s\n", + "- **Total speedup: 10,000×**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## See Also\n", + "\n", + "- Tutorials: Large-Scale Simulations\n", + "- GPU/TPU Usage\n", + "- Debugging Networks" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/tutorials/how-to-guides/save-load-models.ipynb b/docs_state/tutorials/how-to-guides/save-load-models.ipynb new file mode 100644 index 00000000..ffd16f51 --- /dev/null +++ b/docs_state/tutorials/how-to-guides/save-load-models.ipynb @@ -0,0 +1,621 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# How to Save and Load Models\n", + "\n", + "This guide shows you how to save and load BrainPy models for checkpointing, resuming training, and deployment." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quick Start\n", + "\n", + "**Save a trained model:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import brainpy\n", + "import brainstate\n", + "import pickle\n", + "\n", + "# After training...\n", + "state_dict = {\n", + " 'params': net.states(brainstate.ParamState),\n", + " 'epoch': current_epoch,\n", + "}\n", + "\n", + "with open('model.pkl', 'wb') as f:\n", + " pickle.dump(state_dict, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Load a model:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create model with same architecture\n", + "net = MyNetwork()\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "# Load saved state\n", + "with open('model.pkl', 'rb') as f:\n", + " state_dict = pickle.load(f)\n", + "\n", + "# Restore parameters\n", + "for name, state in state_dict['params'].items():\n", + " net.states(brainstate.ParamState)[name].value = state.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Understanding What to Save\n", + "\n", + "### State Types\n", + "\n", + "BrainPy has three state types with different persistence requirements:\n", + "\n", + "**ParamState (Always save)**\n", + " - Learnable weights and biases\n", + " - Required to restore trained model\n", + " - Examples: synaptic weights, neural biases\n", + "\n", + "**LongTermState (Usually save)**\n", + " - Persistent statistics and counters\n", + " - Not updated by gradients\n", + " - Examples: running averages, spike counts\n", + "\n", + "**ShortTermState (Never save)**\n", + " - Temporary dynamics that reset each trial\n", + " - Will be re-initialized anyway\n", + " - Examples: membrane potentials, synaptic conductances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Recommended Approach" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def save_checkpoint(net, optimizer, epoch, filepath):\n", + " \"\"\"Save model checkpoint.\"\"\"\n", + " state_dict = {\n", + " # Required: model parameters\n", + " 'params': net.states(brainstate.ParamState),\n", + "\n", + " # Optional but recommended: long-term states\n", + " 'long_term': net.states(brainstate.LongTermState),\n", + "\n", + " # Training metadata\n", + " 'epoch': epoch,\n", + " 'optimizer_state': optimizer.state_dict(), # If continuing training\n", + "\n", + " # Model configuration (helpful for loading)\n", + " 'config': {\n", + " 'n_input': net.n_input,\n", + " 'n_hidden': net.n_hidden,\n", + " 'n_output': net.n_output,\n", + " # ... other hyperparameters\n", + " }\n", + " }\n", + "\n", + " with open(filepath, 'wb') as f:\n", + " pickle.dump(state_dict, f)\n", + "\n", + " print(f\"✅ Saved checkpoint to {filepath}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Save/Load\n", + "\n", + "### Using Pickle (Simple)\n", + "\n", + "**Advantages:**\n", + "- Simple and straightforward\n", + "- Works with any Python object\n", + "- Good for quick prototyping\n", + "\n", + "**Disadvantages:**\n", + "- Python-specific format\n", + "- Version compatibility issues\n", + "- Not human-readable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "import brainpy\n", + "import brainstate\n", + "\n", + "# Define your model\n", + "class SimpleNet(brainstate.nn.Module):\n", + " def __init__(self, n_neurons=100):\n", + " super().__init__()\n", + " self.lif = brainpy.state.LIF(n_neurons, V_rest=-65*u.mV, V_th=-50*u.mV, tau=10*u.ms)\n", + " self.fc = brainstate.nn.Linear(n_neurons, 10)\n", + "\n", + " def update(self, x):\n", + " self.lif(x)\n", + " return self.fc(self.lif.get_spike())\n", + "\n", + "# Train model\n", + "net = SimpleNet()\n", + "brainstate.nn.init_all_states(net)\n", + "# ... training code ...\n", + "\n", + "# Save\n", + "params = net.states(brainstate.ParamState)\n", + "with open('simple_net.pkl', 'wb') as f:\n", + " pickle.dump(params, f)\n", + "\n", + "# Load\n", + "net_new = SimpleNet()\n", + "brainstate.nn.init_all_states(net_new)\n", + "\n", + "with open('simple_net.pkl', 'rb') as f:\n", + " loaded_params = pickle.load(f)\n", + "\n", + "# Restore parameters\n", + "for name, state in loaded_params.items():\n", + " net_new.states(brainstate.ParamState)[name].value = state.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Using NumPy (Arrays Only)\n", + "\n", + "**Advantages:**\n", + "- Language-agnostic\n", + "- Efficient storage\n", + "- Widely supported\n", + "\n", + "**Disadvantages:**\n", + "- Only saves arrays (not structure)\n", + "- Need to manually track parameter names" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Save parameters as .npz\n", + "params = net.states(brainstate.ParamState)\n", + "param_dict = {name: np.array(state.value) for name, state in params.items()}\n", + "np.savez('model_params.npz', **param_dict)\n", + "\n", + "# Load parameters\n", + "loaded = np.load('model_params.npz')\n", + "for name, array in loaded.items():\n", + " net.states(brainstate.ParamState)[name].value = jnp.array(array)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Checkpointing During Training\n", + "\n", + "### Periodic Checkpoints\n", + "\n", + "Save at regular intervals during training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import braintools\n", + "\n", + "# Training setup\n", + "net = MyNetwork()\n", + "optimizer = braintools.optim.Adam(lr=1e-3)\n", + "optimizer.register_trainable_weights(net.states(brainstate.ParamState))\n", + "\n", + "save_interval = 5 # Save every 5 epochs\n", + "checkpoint_dir = './checkpoints'\n", + "import os\n", + "os.makedirs(checkpoint_dir, exist_ok=True)\n", + "\n", + "# Training loop\n", + "for epoch in range(num_epochs):\n", + " # Training step\n", + " for batch in train_loader:\n", + " loss = train_step(net, optimizer, batch)\n", + "\n", + " # Periodic save\n", + " if (epoch + 1) % save_interval == 0:\n", + " checkpoint_path = f'{checkpoint_dir}/epoch_{epoch+1}.pkl'\n", + " save_checkpoint(net, optimizer, epoch, checkpoint_path)\n", + "\n", + " print(f\"Epoch {epoch+1}: Loss={loss:.4f}, Checkpoint saved\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Best Model Checkpoint\n", + "\n", + "Save only when validation performance improves." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "best_val_loss = float('inf')\n", + "best_model_path = 'best_model.pkl'\n", + "\n", + "for epoch in range(num_epochs):\n", + " # Training\n", + " train_loss = train_epoch(net, optimizer, train_loader)\n", + "\n", + " # Validation\n", + " val_loss = validate(net, val_loader)\n", + "\n", + " # Save if best\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " save_checkpoint(net, optimizer, epoch, best_model_path)\n", + " print(f\"✅ New best model! Val loss: {val_loss:.4f}\")\n", + "\n", + " print(f\"Epoch {epoch+1}: Train={train_loss:.4f}, Val={val_loss:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Resuming Training\n", + "\n", + "Continue training from a checkpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_checkpoint(filepath, net, optimizer=None):\n", + " \"\"\"Load checkpoint and restore state.\"\"\"\n", + " with open(filepath, 'rb') as f:\n", + " state_dict = pickle.load(f)\n", + "\n", + " # Restore model parameters\n", + " params = net.states(brainstate.ParamState)\n", + " for name, state in state_dict['params'].items():\n", + " if name in params:\n", + " params[name].value = state.value\n", + "\n", + " # Restore long-term states\n", + " if 'long_term' in state_dict:\n", + " long_term = net.states(brainstate.LongTermState)\n", + " for name, state in state_dict['long_term'].items():\n", + " if name in long_term:\n", + " long_term[name].value = state.value\n", + "\n", + " # Restore optimizer state\n", + " if optimizer is not None and 'optimizer_state' in state_dict:\n", + " optimizer.load_state_dict(state_dict['optimizer_state'])\n", + "\n", + " start_epoch = state_dict.get('epoch', 0) + 1\n", + " return start_epoch\n", + "\n", + "# Resume training\n", + "net = MyNetwork()\n", + "brainstate.nn.init_all_states(net)\n", + "optimizer = braintools.optim.Adam(lr=1e-3)\n", + "optimizer.register_trainable_weights(net.states(brainstate.ParamState))\n", + "\n", + "# Load checkpoint\n", + "start_epoch = load_checkpoint('checkpoint_epoch_50.pkl', net, optimizer)\n", + "\n", + "# Continue training from where we left off\n", + "for epoch in range(start_epoch, num_epochs):\n", + " train_step(net, optimizer, train_loader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Advanced Saving Strategies\n", + "\n", + "### Versioned Checkpoints\n", + "\n", + "Keep multiple checkpoints without overwriting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "def save_versioned_checkpoint(net, epoch, base_dir='checkpoints'):\n", + " \"\"\"Save checkpoint with timestamp.\"\"\"\n", + " timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')\n", + " filename = f'model_epoch{epoch}_{timestamp}.pkl'\n", + " filepath = os.path.join(base_dir, filename)\n", + "\n", + " state_dict = {\n", + " 'params': net.states(brainstate.ParamState),\n", + " 'epoch': epoch,\n", + " 'timestamp': timestamp,\n", + " }\n", + "\n", + " with open(filepath, 'wb') as f:\n", + " pickle.dump(state_dict, f)\n", + "\n", + " return filepath" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Keep Last N Checkpoints\n", + "\n", + "Automatically delete old checkpoints to save disk space." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "\n", + "def save_with_cleanup(net, epoch, checkpoint_dir='checkpoints', keep_last=5):\n", + " \"\"\"Save checkpoint and keep only last N.\"\"\"\n", + "\n", + " # Save new checkpoint\n", + " filepath = f'{checkpoint_dir}/epoch_{epoch:04d}.pkl'\n", + " save_checkpoint(net, None, epoch, filepath)\n", + "\n", + " # Get all checkpoints\n", + " checkpoints = sorted(glob.glob(f'{checkpoint_dir}/epoch_*.pkl'))\n", + "\n", + " # Delete old ones\n", + " if len(checkpoints) > keep_last:\n", + " for old_checkpoint in checkpoints[:-keep_last]:\n", + " os.remove(old_checkpoint)\n", + " print(f\"Removed old checkpoint: {old_checkpoint}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Model Export for Deployment\n", + "\n", + "### Minimal Model File\n", + "\n", + "Save only what's needed for inference." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def export_for_inference(net, filepath, metadata=None):\n", + " \"\"\"Export minimal model for inference.\"\"\"\n", + "\n", + " export_dict = {\n", + " 'params': net.states(brainstate.ParamState),\n", + " 'config': {\n", + " # Only architecture info, no training state\n", + " 'model_type': net.__class__.__name__,\n", + " # ... architecture hyperparameters\n", + " }\n", + " }\n", + "\n", + " if metadata:\n", + " export_dict['metadata'] = metadata\n", + "\n", + " with open(filepath, 'wb') as f:\n", + " pickle.dump(export_dict, f)\n", + "\n", + " # Report size\n", + " size_mb = os.path.getsize(filepath) / (1024 * 1024)\n", + " print(f\"📦 Exported model: {size_mb:.2f} MB\")\n", + "\n", + "# Export trained model\n", + "export_for_inference(\n", + " net,\n", + " 'deployed_model.pkl',\n", + " metadata={\n", + " 'description': 'LIF network for digit classification',\n", + " 'accuracy': 0.95,\n", + " 'date': datetime.now().isoformat()\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading for Inference" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def load_for_inference(filepath, model_class):\n", + " \"\"\"Load model for inference only.\"\"\"\n", + "\n", + " with open(filepath, 'rb') as f:\n", + " export_dict = pickle.load(f)\n", + "\n", + " # Create model from config\n", + " config = export_dict['config']\n", + " net = model_class(**config) # Must match saved config\n", + " brainstate.nn.init_all_states(net)\n", + "\n", + " # Load parameters\n", + " params = net.states(brainstate.ParamState)\n", + " for name, state in export_dict['params'].items():\n", + " params[name].value = state.value\n", + "\n", + " return net, export_dict.get('metadata')\n", + "\n", + "# Load and use\n", + "net, metadata = load_for_inference('deployed_model.pkl', MyNetwork)\n", + "print(f\"Loaded model: {metadata['description']}\")\n", + "\n", + "# Run inference\n", + "brainstate.nn.init_all_states(net)\n", + "output = net(input_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best Practices\n", + "\n", + "✅ **Always save configuration** - Include hyperparameters for reproducibility\n", + "\n", + "✅ **Version your checkpoints** - Track model version for compatibility\n", + "\n", + "✅ **Save metadata** - Include training metrics, date, description\n", + "\n", + "✅ **Regular backups** - Save periodically during long training\n", + "\n", + "✅ **Keep best model** - Separate best and latest checkpoints\n", + "\n", + "✅ **Test loading** - Verify checkpoint can be loaded before continuing\n", + "\n", + "✅ **Use relative paths** - Make checkpoints portable\n", + "\n", + "✅ **Document format** - Comment what's in your checkpoint files\n", + "\n", + "❌ **Don't save ShortTermState** - It resets anyway\n", + "\n", + "❌ **Don't save everything** - Minimize checkpoint size\n", + "\n", + "❌ **Don't overwrite** - Keep multiple checkpoints for safety" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "**Quick reference:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Save\n", + "checkpoint = {\n", + " 'params': net.states(brainstate.ParamState),\n", + " 'epoch': epoch,\n", + " 'config': net.get_config()\n", + "}\n", + "with open('checkpoint.pkl', 'wb') as f:\n", + " pickle.dump(checkpoint, f)\n", + "\n", + "# Load\n", + "with open('checkpoint.pkl', 'rb') as f:\n", + " checkpoint = pickle.load(f)\n", + "\n", + "net = MyNetwork.from_config(checkpoint['config'])\n", + "brainstate.nn.init_all_states(net)\n", + "\n", + "for name, state in checkpoint['params'].items():\n", + " net.states(brainstate.ParamState)[name].value = state.value" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## See Also\n", + "\n", + "- Core Concepts: State Management\n", + "- Tutorials: SNN Training\n", + "- GPU/TPU Usage" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs_state/tutorials/index.rst b/docs_state/tutorials/index.rst index 09e806b7..1c69360f 100644 --- a/docs_state/tutorials/index.rst +++ b/docs_state/tutorials/index.rst @@ -1,345 +1,19 @@ Tutorials ========= -Welcome to the ``brainpy.state`` tutorials! These step-by-step guides will help you master computational neuroscience modeling with BrainPy. +Welcome to the BrainPy tutorials! This collection of tutorials will guide you from basic concepts +to advanced applications in brain dynamics programming. -Learning Path -------------- +**Basic Tutorials** introduce fundamental concepts including neuron models, synapses, network +connections, and input/output handling. These are essential for getting started with BrainPy. -We recommend following the tutorials in order: +**Advanced Tutorials** cover more sophisticated topics such as spiking neural network training, +synaptic plasticity mechanisms, and large-scale brain simulations. -1. **Basic Tutorials**: Learn core components (neurons, synapses, networks) -2. **Advanced Tutorials**: Master complex topics (training, plasticity, large-scale simulations) -3. **Specialized Topics**: Explore specific applications and techniques - -Basic Tutorials ---------------- - -Start here to learn the fundamentals of ``brainpy.state``. - -Tutorial 1: LIF Neuron Basics -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Learn the most important spiking neuron model. - -**Topics covered:** - -- Creating and configuring LIF neurons -- Simulating neuron dynamics -- Computing F-I curves -- Hard vs soft reset modes -- Working with neuron populations -- Parameter effects on behavior - -:doc:`Go to Tutorial 1 ` - -**Prerequisites:** None (start here!) - -**Duration:** ~30 minutes - ---- - -Tutorial 2: Synapse Models -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Understand temporal filtering and synaptic dynamics. - -**Topics covered:** - -- Exponential synapses -- Alpha synapses -- AMPA and GABA receptors -- Comparing synapse models -- Custom synapse creation - -:doc:`Go to Tutorial 2 ` - -**Prerequisites:** Tutorial 1 - -**Duration:** ~25 minutes - ---- - -Tutorial 3: Network Connections -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Build connected neural networks. - -**Topics covered:** - -- Projection architecture (Comm-Syn-Out) -- Fixed probability connectivity -- CUBA vs COBA synapses -- E-I balanced networks -- Network visualization - -:doc:`Go to Tutorial 3 ` - -**Prerequisites:** Tutorials 1-2 - -**Duration:** ~35 minutes - ---- - -Tutorial 4: Input and Output -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Generate inputs and process network outputs. - -**Topics covered:** - -- Poisson spike trains -- Periodic inputs -- Custom input patterns -- Readout layers -- Population coding - -:doc:`Go to Tutorial 4 ` - -**Prerequisites:** Tutorials 1-3 - -**Duration:** ~20 minutes - -Advanced Tutorials ------------------- - -Dive deeper into sophisticated modeling techniques. - -Tutorial 5: Training Spiking Neural Networks -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Learn gradient-based training for SNNs. - -**Topics covered:** - -- Surrogate gradient methods -- BPTT for SNNs -- Loss functions for spikes -- Optimizers and learning rates -- Classification tasks -- Training loops - -:doc:`Go to Tutorial 5 ` - -**Prerequisites:** Basic Tutorials 1-4 - -**Duration:** ~45 minutes - ---- - -Tutorial 6: Synaptic Plasticity -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Implement learning rules and adaptation. - -**Topics covered:** - -- Short-term plasticity (STP) -- Depression and facilitation -- STDP principles -- Homeostatic mechanisms -- Network learning - -:doc:`Go to Tutorial 6 ` - -**Prerequisites:** Basic Tutorials, Tutorial 5 - -**Duration:** ~40 minutes - ---- - -Tutorial 7: Large-Scale Simulations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Scale up your models efficiently. - -**Topics covered:** - -- Memory optimization -- JIT compilation best practices -- Batching strategies -- GPU/TPU acceleration -- Performance profiling -- Sparse connectivity - -:doc:`Go to Tutorial 7 ` - -**Prerequisites:** All Basic Tutorials - -**Duration:** ~35 minutes - -Specialized Topics ------------------- - -Application-specific tutorials for advanced users. - -Brain Oscillations (Coming Soon) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Model and analyze network rhythms. - -**Topics:** Gamma oscillations, synchrony, oscillation mechanisms - -**Prerequisites:** Advanced - ---- - -Decision-Making Networks (Coming Soon) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Build cognitive computation models. - -**Topics:** Attractor dynamics, competition, working memory - -**Prerequisites:** Advanced - ---- - -Reservoir Computing (Coming Soon) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Use untrained recurrent networks for computation. - -**Topics:** Echo state networks, liquid state machines, readout training - -**Prerequisites:** Advanced - -Tutorial Format ---------------- - -Each tutorial includes: - -✅ **Clear learning objectives**: Know what you'll learn - -✅ **Runnable code**: All examples work out of the box - -✅ **Visualizations**: See your models in action - -✅ **Explanations**: Understand the "why" behind the code - -✅ **Exercises**: Practice what you've learned - -✅ **References**: Links to papers and further reading - -How to Use These Tutorials ---------------------------- - -Interactive (Recommended) -~~~~~~~~~~~~~~~~~~~~~~~~~ - -Download and run the Jupyter notebooks: - -.. code-block:: bash - - git clone https://github.com/brainpy/BrainPy.git - cd BrainPy/docs_version3/tutorials/basic - jupyter notebook 01-lif-neuron.ipynb - -Read-Only -~~~~~~~~~ - -Browse the tutorials online in the documentation. - -Binder -~~~~~~ - -Run tutorials in your browser without installation: - -.. image:: https://mybinder.org/badge_logo.svg - :target: https://mybinder.org/v2/gh/brainpy/BrainPy-binder/main - :alt: Binder - -Prerequisites -------------- - -Before starting the tutorials, ensure you have: - -✅ Python 3.10 or later - -✅ ``brainpy.state`` installed (see :doc:`../quickstart/installation`) - -✅ Basic Python knowledge (functions, classes, NumPy) - -✅ Basic neuroscience concepts (optional but helpful) - -Recommended setup: - -.. code-block:: bash - - pip install brainpy[cpu] matplotlib jupyter -U - -Additional Resources --------------------- - -**For Quick Start** - See the :doc:`../quickstart/5min-tutorial` for a rapid introduction - -**For Concepts** - Read :doc:`../core-concepts/architecture` for architectural understanding - -**For Examples** - Browse :doc:`../examples/gallery` for complete, real-world models - -**For Reference** - Consult the :doc:`../apis` for detailed API documentation - -Getting Help ------------- - -If you get stuck: - -- Check the :doc:`FAQ <../migration/migration-guide>` (Migration Guide has troubleshooting) -- Search `GitHub Issues `_ -- Ask on GitHub Discussions -- Review the `brainstate documentation `_ - -Tutorial Roadmap ----------------- - -**Currently Available:** - -**Basic Tutorials:** -- ✅ Tutorial 1: LIF Neuron Basics -- ✅ Tutorial 2: Synapse Models -- ✅ Tutorial 3: Network Connections -- ✅ Tutorial 4: Input and Output - -**Advanced Tutorials:** -- ✅ Tutorial 5: Training SNNs -- ✅ Tutorial 6: Synaptic Plasticity -- ✅ Tutorial 7: Large-Scale Simulations - -**Future Plans:** - -- Brain Oscillations -- Decision-Making Networks -- Reservoir Computing -- Custom Components -- Advanced Training Techniques - -We're actively developing new tutorials. Star the repository to stay updated! - -Contributing ------------- - -Want to contribute a tutorial? We'd love your help! - -1. Check the `contribution guidelines `_ -2. Open an issue to discuss your tutorial idea -3. Submit a pull request - -Good tutorial topics: - -- Specific neuron models (Izhikevich, AdEx, etc.) -- Network architectures (attractor networks, etc.) -- Analysis techniques (spike train analysis, etc.) -- Applications (sensory processing, motor control, etc.) - -Let's Start! ------------- - -Ready to begin? Start with :doc:`Tutorial 1: LIF Neuron Basics `! +**Guides** provide practical solutions for common tasks like saving/loading models, +debugging networks, optimizing performance, and creating custom components. +Choose a tutorial based on your experience level and learning goals! .. toctree:: :hidden: @@ -360,3 +34,14 @@ Ready to begin? Start with :doc:`Tutorial 1: LIF Neuron Basics