Updated repo requirements and structure in README, cleaned src/analysis keeping only essential scripts

README.md

@@ -90,13 +90,30 @@ and **Bonferroni** correction for planned comparisons.
 NeuroAI-Project13/
 ├── README.md
 ├── environment.yml          # pinned conda environment
+├── requirements.txt         # pip dependencies
+├── pyproject.toml
 ├── src/
-│   ├── models/              # CTRNN definition + Euler integration
+│   ├── models/              # CTRNN definition and architecture
 │   ├── training/            # training loop, loss functions, BPTT
-│   └── analysis/            # PID / ΦID / MI / Fisher pipelines
-├── notebooks/               # exploratory analysis + throwaway/example files
-├── results/                 # saved model weights/activations, metric outputs
+│   ├── tasks/               # NeuroGym task wrappers and stimulus preprocessing
+│   └── analysis/            # PID computation, stats, plotting
+├── notebooks/               # exploratory scratch analysis + throwaway/example files
+├── results/
+│   ├── model_weights/       # saved .pt checkpoint files per CTRNN seed
+│   │   ├── perceptual/
+│   │   └── context/
+│   ├── model_activations/   # saved hidden state tensors per CTRNN seed and trial
+│   │   ├── perceptual/
+│   │   └── context/    
+│   ├── stimulus_coherences/ # saved coherence arrays per CTRNN seed and trial
+│   │   ├── perceptual/
+│   │   └── context/
+│   └── pid_outputs/         # saved PID atom arrays per CTRNN seed
+│       ├── perceptual/
+│       └── context/
 ├── figures/                 # final figures
+│   ├── learning_curves/     # CTRNN train/test loss and accuracy curves
+│   └── pid/                 # PID atom plots
 └── docs/                    # technical note, references
 ```
 

requirements.txt

@@ -3,11 +3,11 @@ torch==2.3.1; python_version >= '3.11'
 --extra-index-url https://download.pytorch.org/whl/cu121
 
 # Numerics & stats
-numpy==1.26.4; python_version >= '3.11'
+numpy==2.2.6; python_version >= '3.11'
 scipy==1.13.1; python_version >= '3.11'
 
 # Visualization
-matplotlib==3.9.2; python_version >= '3.11'
+matplotlib==3.10.9; python_version >= '3.11'
 seaborn==0.13.2; python_version >= '3.11'
 
 # NeuroGym tasks

src/analysis/PID_figures.py

@@ -38,7 +38,43 @@
 plt.gca().spines['top'].set_visible(False)
 plt.gca().spines['right'].set_visible(False)
 plt.tight_layout()
-plt.savefig(f'{dir}pid_bars_{SYSTEM}.png', dpi=300)
+# plt.savefig(f'{dir}pid_bars_{SYSTEM}.png', dpi=300)
 plt.show()
 
 
+
+
+# Measure Gaussianity of hidden unit activations across all timesteps, for each unit.
+import torch
+from scipy import stats
+
+h = torch.load(f'{dir}hidden_Sinusoid.pt', weights_only=True).numpy()  # (300, 10)
+n_units = h.shape[1]
+
+fig, axes = plt.subplots(2, 5, figsize=(14, 5))
+axes = axes.ravel()
+
+for i in range(n_units):
+    ax = axes[i]
+    unit_acts = h[:, i]                          # 300 values across timesteps
+
+    # histogram of activations
+    ax.hist(unit_acts, bins=20, color='#4C78A8', alpha=0.75, density=True, zorder=2)
+
+    # overlay best-fit Gaussian for visual comparison
+    mu, sigma = unit_acts.mean(), unit_acts.std()
+    x = np.linspace(unit_acts.min(), unit_acts.max(), 200)
+    ax.plot(x, stats.norm.pdf(x, mu, sigma), color='#E45756', lw=1.8, zorder=3)
+
+    # Shapiro-Wilk p-value (tests departure from normality; p<0.05 = non-Gaussian)
+    _, p = stats.shapiro(unit_acts)
+    ax.set_title(f'Unit {i+1}   p={p:.2f}', fontsize=9)
+    ax.tick_params(labelsize=7)
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+
+fig.suptitle('Hidden unit activation distributions (blue=data, red=fitted Gaussian)\n'
+             'Shapiro-Wilk p-value: p>0.05 consistent with Gaussian', fontsize=11)
+fig.tight_layout()
+# fig.savefig('unit_activations_gaussianity.png', dpi=150, bbox_inches='tight')
+plt.show()