Refactor neural network classes and update progress bar parameters for simulations

Author: chaoming0625

docs_classic/index.rst

@@ -123,6 +123,14 @@ Learn more
 
 ----
 
+See also the ecosystem
+^^^^^^^^^^^^^^^^^^^^^^
+
+``brainpy`` is one part of our `brain simulation ecosystem <https://brainmodeling.readthedocs.io/>`_.
+
+
+----
+
 
 
 .. toctree::

docs_state/core-concepts/index.rst

@@ -1,6 +1,40 @@
 Core Concepts
 =============
 
+This section provides an in-depth exploration of the fundamental concepts underlying ``brainpy.state``.
+Understanding these core principles will help you build more sophisticated and efficient neural network
+models.
+
+``brainpy.state`` introduces a State-based programming paradigm that simplifies the development of
+spiking neural networks while improving performance and scalability. The concepts covered here form
+the foundation for all neural modeling work in this framework.
+
+
+Overview
+--------
+
+The core concepts of ``brainpy.state`` include:
+
+- **Architecture**: The overall structure and design principles of State-based neural networks, including how components interact and compose together
+- **Neurons**: Building blocks for neural computation, including different neuron models and their state representations
+- **Synapses**: Connections between neurons that transmit signals and implement learning rules
+- **Projections**: Network-level structures that organize and manage connections between populations of neurons
+- **State Management**: The powerful state handling system that enables efficient simulation and flexible model composition
+
+
+Why these concepts matter
+--------------------------
+
+Mastering these core concepts will enable you to:
+
+- Design complex neural networks with clean, maintainable code
+- Leverage the State-based paradigm for improved performance
+- Understand how to compose neurons, synapses, and projections effectively
+- Manage model state efficiently during simulation
+- Seamlessly integrate with the BrainX ecosystem
+
+Each concept builds upon the previous ones, so we recommend reading them in order if you're new to ``brainpy.state``.
+
 
 .. toctree::
    :hidden:

docs_state/examples/gallery.rst

@@ -434,15 +434,13 @@ Additional Resources
 **Core Concepts**
    For architectural understanding, see :doc:`../core-concepts/architecture`
 
-**Migration Guide**
-   For updating from 2.x, see :doc:`../migration/migration-guide`
 
 Browse All Examples
 -------------------
 
 View all examples on GitHub:
 
-`BrainPy Examples (Version 3.0) <https://github.com/brainpy/BrainPy/tree/master/examples_state>`_
+`BrainPy-State Examples <https://github.com/brainpy/BrainPy/tree/master/examples_state>`_
 
 For more extensive examples and notebooks:
 

docs_state/index.rst

@@ -93,13 +93,6 @@ Learn more
          :class-card: sd-text-black sd-bg-light
          :link: apis.html
 
-   .. grid-item::
-      :columns: 6 6 6 4
-
-      .. card:: :material-regular:`swap_horiz;2em` Migration from 2.x
-         :class-card: sd-text-black sd-bg-light
-         :link: migration/migration-guide.html
-
    .. grid-item::
       :columns: 6 6 6 4
 

docs_state/quickstart/index.rst

@@ -1,6 +1,34 @@
 Quickstart
 ==========
 
+Welcome to the quickstart guide for ``brainpy.state``! This section will help you get up and running
+with the State-based programming paradigm for building and simulating spiking neural networks.
+
+Whether you're new to BrainPy or transitioning from ``brainpy.dyn``, this guide provides everything
+you need to start building neural models with the improved State-based approach.
+
+
+What you'll learn
+-----------------
+
+This quickstart guide covers:
+
+- **Installation**: How to install BrainPy with the appropriate backend (CPU, GPU, TPU) for your needs
+- **5-Minute Tutorial**: A hands-on introduction to building your first neural network with ``brainpy.state``
+- **Core Concepts**: Understanding the fundamental concepts of State-based programming and how it differs from traditional approaches
+
+
+Getting started
+---------------
+
+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
+
+For experienced users, you can jump directly to the concepts overview or explore the tutorials and examples sections.
+
 
 .. toctree::
    :hidden:

examples_state/107_gamma_oscillation_1996.py

@@ -139,7 +139,7 @@ def update(self, t):
 # simulation
 with brainstate.environ.context(dt=0.01 * u.ms):
     times = u.math.arange(0. * u.ms, 500. * u.ms, brainstate.environ.get_dt())
-    spikes, vs = brainstate.transform.for_loop(net.update, times, pbar=brainstate.transform.ProgressBar(10))
+    spikes, vs = brainstate.transform.for_loop(net.update, times, pbar=100)
 
 # visualization
 fig, gs = braintools.visualize.get_figure(1, 2, 4, 4)

examples_state/110_Susin_Destexhe_2021_gamma_oscillation_AI.py

@@ -73,7 +73,7 @@ def simulate_adex_neurons():
     plt.show()
 
 
-class AINet(brainstate.nn.DynamicsGroup):
+class AINet(brainstate.nn.Module):
     def __init__(self):
         super().__init__()
 
@@ -164,8 +164,7 @@ def simulate_ai_net():
         # simulation
         times = u.math.arange(0. * u.ms, duration, brainstate.environ.get_dt())
         indices = u.math.arange(0, len(times))
-        returns = brainstate.transform.for_loop(net.update, indices, times, varied_rates,
-                                                pbar=brainstate.transform.ProgressBar(100))
+        returns = brainstate.transform.for_loop(net.update, indices, times, varied_rates, pbar=4000)
 
         # # spike raster plot
         # spikes = returns['FS.spike']

examples_state/111_Susin_Destexhe_2021_gamma_oscillation_CHING.py

@@ -31,7 +31,7 @@
 )
 
 
-class CHINGNet(brainstate.nn.DynamicsGroup):
+class CHINGNet(brainstate.nn.Module):
     def __init__(self):
         super().__init__()
 

examples_state/112_Susin_Destexhe_2021_gamma_oscillation_ING.py

@@ -32,7 +32,7 @@
 )
 
 
-class INGNet(brainstate.nn.DynamicsGroup):
+class INGNet(brainstate.nn.Module):
     def __init__(self):
         super().__init__()
 
@@ -178,8 +178,7 @@ def simulate_ing_net():
         # simulation
         times = u.math.arange(0. * u.ms, duration, brainstate.environ.get_dt())
         indices = u.math.arange(0, len(times))
-        returns = brainstate.transform.for_loop(net.update, indices, times, varied_rates,
-                                                pbar=brainstate.transform.ProgressBar(100))
+        returns = brainstate.transform.for_loop(net.update, indices, times, varied_rates, pbar=1000)
 
         # visualization
         visualize_simulation_results(

examples_state/113_Susin_Destexhe_2021_gamma_oscillation_PING.py

@@ -32,7 +32,7 @@
 )
 
 
-class PINGNet(brainstate.nn.DynamicsGroup):
+class PINGNet(brainstate.nn.Module):
     def __init__(self):
         super().__init__()