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
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