A Python Toolbox for Representational Similarity Analysis

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

Abstract:

Representational similarity analysis (RSA) is a method to characterize neural rep-
resentations 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 mod-
els, and statistical inference methods, which are available to the community in a new
open-source toolbox in Python. The rsatoolbox enables neuroscientists to explore neu-
ral representational geometries and to evaluate neural network models, connecting
theory to experiment in the new era of big models and big data.