Notebook_01.py

# %% [raw]
# ---
# title: "Figure 2: baseline - 1000 neurons with 4 types"
# author: Cédric Allier, Michael Innerberger, Stephan Saalfeld
# categories:
#   - Neural Activity
#   - Simulation
#   - GNN Training
# execute:
#   echo: false
# image: "graphs_data/signal/signal_fig_2/activity.png"
# ---

# %% [markdown]
# This script reproduces the panels of paper's **Figure 2** and other related supplementary panels (Supp. 1, 2, 5 and 6).
#
# **Simulation parameters:**
#
# - N_neurons: 1000
# - N_types: 4 parameterized by $\tau_i$={0.5,1}, $s_i$={1,2} and $g_i$=10
# - N_frames: 100,000
# - Connectivity: 100% (dense)
# - Connectivity weights: random, Cauchy distribution 
# - Noise: none
# - External inputs: none
#
# The simulation follows a simplified Equation 2 from the paper:
#
# $$\frac{dx_i}{dt} = -\frac{x_i}{\tau_i} + s_i \cdot \tanh(x_i) + g_i \cdot \sum_j W_{ij} \cdot \tanh(x_j)$$

# %%
#| output: false
import os
import warnings

from neural_gnn.config import NeuralGraphConfig
from neural_gnn.generators.graph_data_generator import data_generate
from neural_gnn.models.graph_trainer import data_train, data_test
from neural_gnn.utils import set_device, add_pre_folder, load_and_display
from GNN_PlotFigure import data_plot, create_training_montage

warnings.filterwarnings("ignore", message="pkg_resources is deprecated as an API")
warnings.filterwarnings("ignore", category=FutureWarning)

# %% [markdown]
# ## Configuration and Setup

# %%
#| echo: true
#| output: false
print()
print("=" * 80)
print("Figure 2: 1000 neurons, 4 types, dense connectivity")
print("=" * 80)

device = []
best_model = ''
config_file_ = 'signal_fig_2'

print()
config_root = "./config"
config_file, pre_folder = add_pre_folder(config_file_)

# load config
config = NeuralGraphConfig.from_yaml(f"{config_root}/{config_file}.yaml")
config.config_file = config_file
config.dataset = config_file

if device == []:
    device = set_device(config.training.device)

log_dir = f'./log/{config_file}'
graphs_dir = f'./graphs_data/{config_file}'


# %% [markdown]
# ## Step 1: Generate Data
# Generate synthetic neural activity data using the PDE_N2 model (src/neural-gnn/generators).
# This creates the training dataset with 1000 neurons of 4 different types over 100,000 time points.
#
# **Outputs:**
#
# - Figure 2b: Sample of 100 time series
# - Figure 2c: True connectivity matrix $W_{ij}$

# %%
#| echo: true
#| output: false
# STEP 1: GENERATE
print()
print("-" * 80)
print("STEP 1: GENERATE - Simulating neural activity (Fig 2a-c)")
print("-" * 80)

# Check if data already exists
data_file = f'{graphs_dir}/x_list_0.npy'
if os.path.exists(data_file):
    print(f"data already exists at {graphs_dir}/")
    print("skipping simulation, regenerating figures...")
    data_generate(
        config,
        device=device,
        visualize=False,
        run_vizualized=0,
        style="color",
        alpha=1,
        erase=False,
        bSave=True,
        step=2,
        regenerate_plots_only=True,
    )
else:
    print(f"simulating {config.simulation.n_neurons} neurons, {config.simulation.n_neuron_types} types")
    print(f"generating {config.simulation.n_frames} time frames")
    print(f"output: {graphs_dir}/")
    print()
    data_generate(
        config,
        device=device,
        visualize=False,
        run_vizualized=0,
        style="color",
        alpha=1,
        erase=False,
        bSave=True,
        step=2,
    )

# %%
#| fig-cap: "Fig 2b: Sample of 100 time series taken from the activity data."
load_and_display(f"./graphs_data/signal/signal_fig_2/activity.png")

# %%
#| fig-cap: "Fig 2c: True connectivity $W_{ij}$. The inset shows 20×20 weights."
load_and_display("./graphs_data/signal/signal_fig_2/connectivity_matrix.png")

# %% [markdown]
# ## Step 2: Train GNN
# Train the GNN to learn connectivity $W$, latent embeddings $\mathbf{a}_i$, and functions $\phi^*, \psi^*$ with the SignalPropagation model  ('src/neural-gnn/models').
# The GNN learns to predict $dx_i/dt$ from the observed activity $x_i$.
#
# The GNN optimizes the update rule (Equation 3 from the paper):
#
# $$\hat{\dot{x}}_i = \phi^*(\mathbf{a}_i, x_i) + \sum_j W_{ij} \psi^*(x_j)$$
#
# where $\phi^*$ and $\psi^*$ are MLPs (ReLU, hidden dim=64, 3 layers).
# $\mathbf{a}_i$ is a learnable 2D latent vector per neuron, and $W$ is the learnable connectivity matrix.
#

# %%
#| echo: true
#| output: false
# STEP 2: TRAIN
print()
print("-" * 80)
print("STEP 2: TRAIN - Training GNN to learn W, embeddings, phi, psi")
print("-" * 80)

# Check if trained model already exists (any .pt file in models folder)
import glob
model_files = glob.glob(f'{log_dir}/models/*.pt')
if model_files:
    print(f"trained model already exists at {log_dir}/models/")
    print("skipping training (delete models folder to retrain)")
else:
    print(f"training for {config.training.n_epochs} epochs, {config.training.n_runs} run(s)")
    print(f"learning: connectivity W, latent vectors a_i, functions phi* and psi*")
    print(f"models: {log_dir}/models/")
    print(f"training plots: {log_dir}/tmp_training")
    print(f"tensorboard: tensorboard --logdir {log_dir}/")
    print()
    data_train(
        config=config,
        erase=False,
        best_model=best_model,
        style='color',
        device=device
    )

# %% [markdown]
# ## Step 3: GNN Evaluation
# Figures matching Figure 2, and supplementary Fig 1, 2, 5, and 6 from the paper.
#
# **Figure panels:**
#
# - Fig 2d: Learned connectivity matrix
# - Fig 2e: Comparison of learned vs true connectivity
# - Fig 2f: Learned latent vectors $\mathbf{a}_i$
# - Fig 2g: Learned update functions $\phi^*(\mathbf{a}_i, x)$
# - Fig 2h: Learned transfer function $\psi^*(x)$ 

# %%
#| echo: true
#| output: false
# STEP 3: GNN EVALUATION
print()
print("-" * 80)
print("STEP 3: GNN EVALUATION - Generating Figure 2 panels (d-h)")
print("-" * 80)
print(f"Fig 2d: Learned connectivity matrix")
print(f"Fig 2e: W learned vs true (R^2, slope)")
print(f"Fig 2f: Latent vectors a_i (4 clusters)")
print(f"Fig 2g: Update functions phi*(a_i, x)")
print(f"Fig 2h: Transfer function psi*(x)")
print(f"output: {log_dir}/results/")
print()
folder_name = './log/' + pre_folder + '/tmp_results/'
os.makedirs(folder_name, exist_ok=True)
data_plot(config=config, config_file=config_file, epoch_list=['best'], style='color', extended='plots', device=device, apply_weight_correction=True, plot_eigen_analysis=False)

# %% [markdown]
# ### Figures 2d-2h: GNN Evaluation Results

# %%
#| fig-cap: "Fig 2d: Learned connectivity."
load_and_display("./log/signal/signal_fig_2/results/connectivity_learned.png")

# %%
#| fig-cap: "Fig 2e: Comparison of learned and true connectivity (given $g_i$=10)."
load_and_display("./log/signal/signal_fig_2/results/weights_comparison_corrected.png")

# %%
#| fig-cap: "Fig 2f: Learned latent vectors $a_i$ of all neurons."
load_and_display("./log/signal/signal_fig_2/results/embedding.png")

# %%
#| fig-cap: "Fig 2g: Learned update functions $\\phi^*(a_i, x)$. The plot shows 1000 overlaid curves, one for each vector $a_i$. Colors indicate true neuron types. True functions are overlaid in light gray."
load_and_display("./log/signal/signal_fig_2/results/MLP0.png")

# %%
#| fig-cap: "Fig 2h: Learned transfer function $\\psi^*(x)$, normalized to a maximum value of 1. True function is overlaid in light gray."
load_and_display("./log/signal/signal_fig_2/results/MLP1_corrected.png")

# %% [markdown]
# ## Step 4: GNN Training Visualization
# Generate training progression figures showing how the GNN learns across epochs.
#
# **Visualizations:**
#
# - Row a: Latent embeddings $\mathbf{a}_i$ evolution
# - Row b: Update functions $\phi^*(\mathbf{a}_i, x)$
# - Row c: Transfer function $\psi^*(x)$
# - Row d: Connectivity matrix $W$
# - Row e: $W$ learned vs true scatter plot

# %%
#| echo: true
#| output: false
# STEP 4: GNN TRAINING VISUALIZATION
print()
print("-" * 80)
print("STEP 4: GNN TRAINING - Generating training progression figures")
print("-" * 80)
print(f"generating plots for all training epochs")
print(f"output: {log_dir}/results/all/")
print()
data_plot(config=config, config_file=config_file, epoch_list=['all'], style='color', extended='plots', device=device, apply_weight_correction=True, plot_eigen_analysis=False)

# Create montage from individual epoch plots
print()
print("creating training montage (8 columns x 5 rows)...")
create_training_montage(config=config, n_cols=8)

# %%
#| fig-cap: "Supplementary Figure 1: Results plotted over 20 epochs. (a) Learned latent vectors $a_i$. (b) Learned update functions $\\phi^*(a_i, x)$. (c) Learned transfer function $\\psi^*(x)$, normalized to max=1. (d) Learned connectivity $W_{ij}$. (e) Comparison of learned and true connectivity. Colors indicate true neuron types."

load_and_display("./log/signal/signal_fig_2/results/training_montage.png")

# %% [markdown]
# ## Step 5: Test Model
# Test the trained GNN model. Evaluates prediction accuracy and performs rollout inference.

# %%
#| echo: true
#| output: false
# STEP 5: TEST
print()
print("-" * 80)
print("STEP 5: TEST - Evaluating trained model")
print("-" * 80)
print(f"testing prediction accuracy and rollout inference")
print(f"output: {log_dir}/results/")
print()
config.simulation.noise_model_level = 0.0

data_test(
    config=config,
    visualize=False,
    style="color name continuous_slice",
    verbose=False,
    best_model='best',
    run=0,
    test_mode="",
    sample_embedding=False,
    step=10,
    n_rollout_frames=1000,
    device=device,
    particle_of_interest=0,
    new_params=None,
)

# %% [markdown]
# ### Rollout Results
# - Left panel: activity traces (ground truth gray, learned colored)
# - Right panel: scatter plot of true vs learned $x_i$ with $R^2$ and slope

# %%
#| fig-cap: "Rollout comparison up to time-point 400."
load_and_display(f"{log_dir}/results/Fig_0_000039.png")

# %%
#| fig-cap: "Rollout comparison up to time-point 800."
load_and_display(f"{log_dir}/results/Fig_0_000079.png")

# %% [markdown]
# ## Step 6: Supplementary Figure 5 - Generalization Test
# Test the trained GNN with modified network structure. Modified neuron type proportions (10%, 20%, 30%, 40% instead of 25% each) and modified sparse connectivity (~25% sparsity, 243,831 weights instead of 10^6).
#
# **Outputs:**
#
# - Panel b: Modified neuron type proportions histogram
# - Panel d: Modified sparse connectivity matrix
# - Panels e,f: Rollout at 400 time-points
# - Panels g,h: Rollout at 800 time-points

# %%
#| echo: true
#| output: false
# STEP 6: SUPPLEMENTARY FIGURE 5 - GENERALIZATION TEST
print()
print("-" * 80)
print("STEP 6: SUPPLEMENTARY FIGURE 5 - Generalization test with modified network")
print("-" * 80)
print("modified neuron type proportions: 10%, 20%, 30%, 40%")
print("modified connectivity: ~25% sparsity (243,831 weights)")
print()

# new_params: [connectivity_filling_factor, type0_pct, type1_pct, type2_pct, type3_pct]
new_params_supp5 = [0.25, 10, 20, 30, 40]

data_test(
    config=config,
    visualize=True,
    style="color",
    verbose=False,
    best_model='best',
    run=0,
    test_mode="",
    sample_embedding=False,
    step=10,
    n_rollout_frames=1000,
    device=device,
    particle_of_interest=0,
    new_params=new_params_supp5,
)

# %% [markdown]
# ### Supplementary Figure 5 Panels

# %%
#| fig-cap: "Panel b: Modified neuron type proportions (10%, 20%, 30%, 40%)."
load_and_display(f"{log_dir}/results/new_neuron_type_histogram.png")

# %%
#| fig-cap: "Panel d: Modified sparse connectivity matrix (~25% sparsity, 243,831 weights)."
load_and_display(f"{log_dir}/results/new connectivity.png")

# %%
#| fig-cap: "Panels e,f: Rollout up to 400 time-points."
load_and_display(f"{log_dir}/results/Fig_0_000039.png")

# %%
#| fig-cap: "Panels g,h: Rollout up to 800 time-points."
load_and_display(f"{log_dir}/results/Fig_0_000079.png")

# %% [markdown]
# ## Supplementary Figure 6 - Generalization Test
# Test the trained GNN with network modifications. Modified neuron type proportions: 60%, 40%, 0%, 0% (types 2 and 3 eliminated) and modified sparse connectivity: ~50% sparsity (487,401 weights instead of 10^6).
#
# **Outputs:**
#
# - Panel b: Modified neuron type proportions histogram
# - Panel d: Modified sparse connectivity matrix
# - Panels e,f: Rollout at 400 time-points
# - Panels g,h: Rollout at 800 time-points

# %%
#| echo: true
#| output: false
# SUPPLEMENTARY FIGURE 6 - GENERALIZATION TEST
print()
print("-" * 80)
print("SUPPLEMENTARY FIGURE 6 - Generalization test with extreme network modification")
print("-" * 80)
print("modified neuron type proportions: 60%, 40%, 0%, 0% (types 2,3 eliminated)")
print("modified connectivity: ~50% sparsity (487,401 weights)")
print()

# new_params: [connectivity_filling_factor, type0_pct, type1_pct, type2_pct, type3_pct]
# 50% sparsity = 0.5 filling factor -> ~500,000 weights
new_params_supp6 = [0.5, 60, 40, 0, 0]

data_test(
    config=config,
    visualize=True,
    style="color",
    verbose=False,
    best_model='best',
    run=0,
    test_mode="",
    sample_embedding=False,
    step=10,
    n_rollout_frames=1000,
    device=device,
    particle_of_interest=0,
    new_params=new_params_supp6,
)

# %% [markdown]
# ### Supplementary Figure 6 Panels

# %%
#| fig-cap: "Panel b: Modified neuron type proportions (60%, 40%, types 2,3 eliminated)."
load_and_display(f"{log_dir}/results/new_neuron_type_histogram.png")

# %%
#| fig-cap: "Panel d: Modified sparse connectivity matrix (~50% sparsity, 487,401 weights)."
load_and_display(f"{log_dir}/results/new connectivity.png")

# %%
#| fig-cap: "Panels e,f: Rollout up to 400 time-points."
load_and_display(f"{log_dir}/results/Fig_0_000039.png")

# %%
#| fig-cap: "Panels g,h: Rollout up to 800 time-points."
load_and_display(f"{log_dir}/results/Fig_0_000079.png")