A Python Toolbox for Representational Similarity Analysis

Jasper JF van den Bosch, Tal Golan, Benjamin Peters, JohnMark Taylor, Mahdiyar Shahbazi, Baihan Lin, Ian Charest, Jörn Diedrichsen, Nikolaus Kriegeskorte, Marieke Mur, Heiko H Schütt

Abstract: Representational similarity analysis (RSA) is a method to characterize neural representations and evaluate computational models based on neural representational geometries. Here we present a wave of recent methodological advances, including improved measures of representational distances, evaluators for representational models, and statistical inference methods, which are available to the community in a new open-source toolbox in Python. The rsatoolbox enables neuroscientists to explore neural representational geometries and to evaluate neural network models, connecting theory to experiment in the new era of big models and big data.

External IDs:doi:10.7554/elife.107828