We introduce Net2Brain, a graphical and command-line user interface toolbox for comparing the representational spaces of artificial deep neural networks (DNNs) and human brain recordings. While different toolboxes facilitate only single functionalities or only focus on a small subset of supervised image classification models, Net2Brain allows the extraction of activations of more than 600 DNNs trained to perform a diverse range of vision-related tasks (e.g semantic segmentation, depth estimation, action recognition, etc.), over both image and video datasets. The toolbox computes the representational dissimilarity matrices (RDMs) over those activations and compares them to brain recordings using representational similarity analysis (RSA), weighted RSA, both in specific ROIs and with searchlight search. In addition, it is possible to add a new data set of stimuli and brain recordings to the toolbox for evaluation. We demonstrate the functionality and advantages of Net2Brain with an example showcasing how it can be used to test hypotheses of cognitive computational neuroscience.
@article{arxiv.2208.09677,
title = {Net2Brain: A Toolbox to compare artificial vision models with human brain responses},
author = {Domenic Bersch and Kshitij Dwivedi and Martina Vilas and Radoslaw M. Cichy and Gemma Roig},
journal= {arXiv preprint arXiv:2208.09677},
year = {2025}
}
Comments
Published in Frontiers in Neuroinformatics (2025), Article 1515873. Version of record: https://doi.org/10.3389/fninf.2025.1515873 4 Pages, 3 figures, submitted and accepted to CCNeuro 2022. For associated repository, see https://github.com/ToastyDom/Net2Brain Update 1: Changed Citation