DM-Diaz/VEDB-NSD-ResNet18-Encoding-Models

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

baseline/
fovea-gaze/
periph/
periph-nf/

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

Example:

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:

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

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) Hugging Face collection.

Release Status

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

Citation

@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}
}