Encoder and diffusion checkpoints for the NeurIPS 2026 paper

README.md

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+---
+license: mit
+tags:
+  - neuroscience
+  - diffusion-models
+  - fisher-information
+---
+
+# Multi-scale information geometry: trained checkpoints
+
+Encoder and conditional diffusion checkpoints for the NeurIPS 2026 paper "A multi-scale information geometry reveals the structure of mutual information in neural populations" (Azeglio, Laquitaine, Ferrari and Chalk). Code and project page: https://multigeom.github.io/
+
+## Contents
+
+```
+encoders/<encoder>/<area>/best_model.pt      encoder fitted to the recorded responses
+encoders/SHA256SUMS                          checksums of the encoders
+diffusion/<encoder>/<area>/final_model.pt    conditional diffusion model, 200 epochs
+diffusion/<encoder>/<area>/train_log.json    loss per epoch
+diffusion/diffusion.yaml                     diffusion training configuration
+diffusion/SHA256SUMS                         checksums of the diffusion models
+diffusion/README.md                          diffusion training recipe and file format
+```
+
+`<encoder>` is `inception`, `resnet50` or `onoff`, and `<area>` is `V1` or `V4`: 12 checkpoints in total.
+
+## Encoders
+
+Each encoder predicts the responses of the 50 most reliable channels of one area, from the dataset of Papale et al. (2025). The Inception and ResNet-50 encoders are frozen backbones with a per-neuron Gaussian readout. The ON-OFF encoders are a frozen front end with a dense linear readout (409,650 parameters).
+
+Each `best_model.pt` is a dictionary with `model_state_dict`, the configuration, and the test-set results used for the encoder table in the paper:
+
+| Encoder | V1 correlation | V1 noise-corrected | V4 correlation | V4 noise-corrected |
+|---|---|---|---|---|
+| Inception | 0.75 | 0.81 | 0.71 | 0.79 |
+| ResNet-50 | 0.78 | 0.85 | 0.70 | 0.78 |
+| ON-OFF | 0.20 | 0.22 | 0.13 | 0.14 |
+
+## Download
+
+```
+hf download sazio/multigeom --local-dir runs
+```
+
+This places the files at the paths the code reads, for example `runs/diffusion/inception/V1/final_model.pt`.
+
+## Loading
+
+```python
+import torch
+
+ckpt = torch.load("runs/encoders/onoff/V1/best_model.pt", map_location="cpu", weights_only=False)
+```
+
+The files are pickled dictionaries, so load them only from a source you trust. They were saved with NumPy 2.
+
+Paths recorded in the `provenance` and `config` fields were made relative for this release. The weights and results are unchanged.
+
+## License
+
+MIT

diffusion/README.md

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+# Conditional diffusion models, 200 epochs with classifier-free guidance
+
+The six conditional diffusion models used for the figures of the NeurIPS 2026 paper "A multi-scale information geometry reveals the structure of mutual information in neural populations". There is one model per encoder (Inception, ResNet-50, ON-OFF) and area (V1, V4).
+
+## Contents
+
+    inception/V1/final_model.pt     inception/V4/final_model.pt
+    resnet50/V1/final_model.pt      resnet50/V4/final_model.pt
+    onoff/V1/final_model.pt         onoff/V4/final_model.pt
+    */*/train_log.json              loss per epoch
+    diffusion.yaml                  training configuration, shared by all six models
+    SHA256SUMS                      checksums of the checkpoints
+
+## Training recipe
+
+The recipe is the same for all six models:
+
+- Network: a single-channel 64x64 DDPM U-Net with block widths (64, 128, 256, 512) and two layers per block, conditioned on the 50-dimensional response vector through an MLP token (dimension 256) and cross-attention.
+- Training data: 100,000 TinyImageNet training images, converted to grayscale and resized to 64x64, each paired with the responses predicted by the encoder for that area, with observation noise of sigma = 0.22 added at every step.
+- Classifier-free guidance: the responses are dropped (null token) with probability 0.1, so the same network also gives the unconditional prediction.
+- Noise schedule: 1000 steps, linear schedule with beta from 1e-4 to 0.02, epsilon prediction.
+- Optimiser: AdamW with learning rate 1e-4 and weight decay 1e-4, cosine schedule with 500 warm-up steps, batch size 128, bfloat16 precision, gradient clipping at 1.0, seed 42.
+- Length: 200 epochs.
+
+## Format
+
+Each `final_model.pt` is a dictionary with the keys:
+
+- `model_state_dict`: the network weights;
+- `epoch`: 200;
+- `loss`: the final training loss;
+- `config`: the configuration file and the response file used for training;
+- `encoder_name` and `area`: which model this is;
+- `provenance`: the git commit of the training code and the command line, with paths made relative for this release.

diffusion/SHA256SUMS

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+8460f49a17eba4b407d94d61f5b581d73382ae21e3963ecf67e1a312fcdde565  inception/V1/final_model.pt
+397ed421bb2e2dae6d03805f0cdddc78df88d82463898348b241417fd625bfaa  inception/V4/final_model.pt
+fcb365f077c732e851bcaeaa8694e11cbef20eeaf3bcc1c037b41bac190c0c84  onoff/V1/final_model.pt
+7a454750a1b9bee1ac950cf502c4d1afcf8313b392d9a28868927895697cd4bc  onoff/V4/final_model.pt
+016a4c345f754040f58b51b690b515df8e442cdf6b3ef7e95f02daae178f352a  resnet50/V1/final_model.pt
+9357c46ca00a51d1183c47af3646970562976e889665f735bd0b1055934a89ae  resnet50/V4/final_model.pt

diffusion/diffusion.yaml

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+# Conditional diffusion model configuration (shared across encoders/areas).
+# Single-channel 64x64 DDPM conditioned on a 50-d response vector.
+
+unet:
+  image_size: 64
+  in_channels: 1
+  out_channels: 1
+  block_channels: [64, 128, 256, 512]
+  layers_per_block: 2
+  cross_attention_dim: 256
+
+conditioning:
+  embedding_dim: 256
+  mlp_layers: [50, 256, 256, 256]   # 50 neurons → 256-d token
+  mlp_activation: silu
+  cfg_dropout_rate: 0.1             # classifier-free guidance dropout
+  observation_noise_sigma: 0.22     # σ for ξ added to r at training time
+
+scheduler:
+  num_train_timesteps: 1000
+  beta_schedule: linear
+  beta_start: 1.0e-4
+  beta_end: 0.02
+  prediction_type: epsilon
+
+train:
+  data: tiny_imagenet
+  data_split: train
+  n_train_images: 100000
+  optimizer: adamw
+  lr: 1.0e-4
+  weight_decay: 1.0e-4
+  scheduler: cosine
+  warmup_steps: 500
+  batch_size: 128
+  epochs: 200                       # 200-ep checkpoint used for figures
+  precision: bfloat16
+  gradient_clip: 1.0
+  seed: 42

diffusion/inception/V1/final_model.pt

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