mlsanityData to 7 Oct 2026

Net2Brain: A Toolbox to compare artificial vision models with human brain responses

arXiv 2208.09677 ↗First posted 20 Aug 2022cs.CV cs.AI q-bio.NCcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 65 references that are in the map. First posted on arXiv 20 Aug 2022.

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21 of 65 references.

References · 21

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9 more
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  1. 2025Cognitive Neural Architecture Search Reveals Hierarchical Entailmentcited by 0