  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.
