PERSPECTIVE article
Front. Neural Circuits
Volume 19 - 2025 | doi: 10.3389/fncir.2025.1618351
This article is part of the Research TopicNeuro-inspired computationView all 10 articles
Summary statistics of learning link changing neural representations to behavior
Provisionally accepted
- Harvard University, Cambridge, United States
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How can we make sense of large-scale recordings of neural activity across learning? Theories of neural network learning with their origins in statistical physics offer a potential answer: for a given task, there are often a small set of summary statistics that are sufficient to predict performance as the network learns. Here, we review recent advances in how summary statistics can be used to build theoretical understanding of neural network learning. We then argue for how this perspective can inform the analysis of neural data, enabling better understanding of learning in biological and artificial neural networks.
Keywords: neural networks, Learning, Statistical Physics, representation learning, summary statistics
Received: 25 Apr 2025; Accepted: 11 Aug 2025.
Copyright: © 2025 Zavatone-Veth, Bordelon and Pehlevan. 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:
Jacob A Zavatone-Veth, Harvard University, Cambridge, United States
Blake Bordelon, Harvard University, Cambridge, United States
Cengiz Pehlevan, Harvard University, Cambridge, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.