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

Abstract

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.

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

References in corpus (8)

• A Simple Framework for Contrastive Learning of Visual Representations
• Learning Transferable Visual Models From Natural Language Supervision
• Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Brain's Ventral Visual Pathway
• Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition
• Recurrence is required to capture the representational dynamics of the human visual system
• Similarity of Neural Network Representations Revisited
• The Dynamic Sensorium competition for predicting large-scale mouse visual cortex activity from videos
• Improving neural network representations using human similarity judgments