METHODS article
Front. Neurosci.
Sec. Brain Imaging Methods
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1597899
The Topology of Representational Geometry
Provisionally accepted
- McGill Vision Research, McGill University, Montréal, Canada
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Representational similarity analysis (RSA) is a powerful tool for abstracting and then comparing neural representations across brains, regions, models and modalities. However, typical RSA analyses compares pairs of representational dissimilarities to judge similarity of two neural systems, and we argue that such methods cannot capture the shape of representational spaces.By leveraging tools from computational topology which can probe the shape of high-dimensional data, we augment RSA to be able to detect more subtle yet real differences and similarities of representational structures. This new method could be used in conjunction with regular RSA in order to make distinct, complementary inferences about neural function.
Keywords: representational similarity analysis, topological data analysis, Persistent homology, Representational geometry, object representation, human, macaque, fMRI
Received: 21 Mar 2025; Accepted: 26 May 2025.
Copyright: © 2025 Brown and Farivar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Shael Brown, McGill Vision Research, McGill University, Montréal, Canada
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