Net2Brain: A Toolbox to Compare Artificial Vision Models with Human Brain Responses
Aug 25, 2022·,,,,·
1 min read
Domenic Bersch
Kshitij Dwivedi
Martina Vilas
Radoslaw M. Cichy
Gemma Roig
Image credit: UnsplashAbstract
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. Net2Brain supports activations from over 600 DNNs trained on diverse vision-related tasks (e.g., semantic segmentation, depth estimation, action recognition), for both image and video datasets. It computes representational dissimilarity matrices (RDMs) from activations and compares them to brain recordings using representational similarity analysis (RSA), weighted RSA, and searchlight search. The toolbox also allows integration of new stimuli and brain recording datasets. An example demonstrates its utility for testing cognitive computational neuroscience hypotheses.
Type
Publication
arXiv preprint
Net2Brain is a graphical and command-line toolbox for comparing the representational spaces of over 600 deep neural networks with human brain responses. It supports multiple vision-related tasks and datasets, enabling RSA, weighted RSA, and searchlight analysis for cognitive computational neuroscience.
This paper demonstrates Net2Brain’s utility and flexibility, making it a valuable tool for hypothesis testing in neuroscience. For more details, read the full paper on arXiv or visit the code repository.