| --- |
| 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 |
|
|
