BOLD5000 Additional ROIs and RDMs for neural network research

Artificial neural networks (ANNs) are sensitive to perturbations and adversarial attacks. One hypothesized solution to adversarial robustness is to align manifolds in the embedded space of neural networks with biologically grounded manifolds. Recent state-of-the-art works that emphasize learning robust neural representations, rather than optimizing for a specific target task like classification, support the idea that researchers should investigate this hypothesis.  While works have shown that fine-tuning ANNs to coincide with biological vision does increase robustness to both perturbations and adversarial attacks, these works have relied on proprietary datasets- the lack of publicly available biological benchmarks make it difficult to evaluate the efficacy of these claims. Here, we deliver a curated dataset consisting of biological representations of images taken from two commonly used computer vision datasets, ImageNet and COCO, that can be easily integrated into model training and eva..., , , # BOLD5000 Additional ROIs and RDMs for Neural Network Research

This dataset is made available as part of the publication of the following journal article:

Pickard W, Sikes K, Jamil H, Chaffee N, Blanchard N, Kirby M and Peterson C (2023) Exploring fMRI RDMs: enhancing model robustness through neurobiological data. Front. Comput. Sci. 5:1275026. doi: [10.3389/fcomp.2023.127502](https://doi.org/10.3389/fcomp.2023.127502)

## Description of the data and file structure

This dataset is derivative of the BOLD5000 Release 2.0. Additional post-processing steps were performed to make the data more accessible in machine learning (ML) research using representational similarity analysis (RSA).

As a general overview, the following additional post-processing steps were performed with the results made available here:

### 1. New cortical regions of interest (ROIs) were defined for each subject using vcAtlast and visfAtlas.

Freesurfer was used to create the new ROIs. Freesurfer derivatives for...