README.md

---
license: apache-2.0
tags:
  - pytorch
  - computer-vision
  - self-supervised-learning
  - simclr
  - resnet18
  - egocentric-vision
  - eccentricity
  - visual-neuroscience
  - vedb
  - arxiv:2607.19316
---

# VEDB SimCLR ResNet-18 — NSD Voxelwise Encoding Models

This repository contains **subject-specific voxelwise encoding-model fits** from:

**Diaz, D. M., & Henderson, M. M. (2026). *Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field.* Proceedings of the 9th Conference on Cognitive Computational Neuroscience.**

**DOI:** `10.32470/0416gfsq`
**arXiv:** `2607.19316`

The encoding models were fit to fMRI responses from the **Natural Scenes Dataset (NSD)** using representations extracted from four VEDB-pretrained SimCLR ResNet-18 models:

* **Baseline**
* **Fovea-Gaze**
* **Periph**
* **Periph-NF**

## Repository Structure

```text
baseline/
fovea-gaze/
periph/
periph-nf/
```

Each folder contains voxelwise encoding-model fits for **NSD subjects S1–S8**.

Example:

```text
fovea-gaze/
├── NSD_S1_resnet18-Fovea-Gaze_concat.npy
├── NSD_S2_resnet18-Fovea-Gaze_concat.npy
├── ...
└── NSD_S8_resnet18-Fovea-Gaze_concat.npy
```

## Encoding Models

For each subject and visual-field condition, features were extracted from:

```text
conv1
layer1.1
layer2.1
layer3.1
layer4.1
avgpool
```

Layer features were dimensionally reduced with PCA, concatenated, and used to fit **voxelwise L2-regularized linear regression (ridge) encoding models**.

Each `.npy` file contains a saved Python dictionary including:

* fitted voxelwise `weights`
* held-out `r2`
* held-out `corr`
* candidate `lambdas`
* `best_lambda_inds`
* voxel mask and index information
* voxel noise ceilings
* subject and model metadata

## Loading a Fit

```python
import numpy as np

fit = np.load(
    "baseline/NSD_S1_resnet18-Baseline_concat.npy",
    allow_pickle=True
).item()

weights = fit["weights"]
r2 = fit["r2"]
corr = fit["corr"]
best_lambda_inds = fit["best_lambda_inds"]
```

## Related Models

The pretrained SimCLR checkpoints used to generate these representations are available in the [**Eccentricity-Constrained SimCLR Models (VEDB)**](https://huggingface.co/collections/DM-Diaz/eccentricity-constrained-simclr-models-vedb) Hugging Face collection.

## Release Status

Encoding-model fits are available now. Additional documentation and analysis code are forthcoming.

## Citation

```bibtex
@inproceedings{diaz2026eccentricity,
  author    = {Diaz, Dylan M. and Henderson, Margaret M.},
  title     = {Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field},
  booktitle = {Proceedings of the 9th Conference on Cognitive Computational Neuroscience},
  address   = {New York, NY, USA},
  year      = {2026},
  doi       = {10.32470/0416gfsq}
}