MARBLE: interpretable representations of neural population dynamics using geometric deep learning
File(s)
Published version
Author(s)
Gosztolai, Adam
Peach, Robert L
Arnaudon, Alexis
Barahona, Mauricio
Vandergheynst, Pierre
Type
Journal Article
Abstract
The dynamics of neuron populations commonly evolve on low-dimensional manifolds. Thus, we need methods that learn the dynamical processes over neural manifolds to infer interpretable and consistent latent representations. We introduce a representation learning method, MARBLE, which decomposes on-manifold dynamics into local flow fields and maps them into a common latent space using unsupervised geometric deep learning. In simulated nonlinear dynamical systems, recurrent neural networks and experimental single-neuron recordings from primates and rodents, we discover emergent low-dimensional latent representations that parametrize high-dimensional neural dynamics during gain modulation, decision-making and changes in the internal state. These representations are consistent across neural networks and animals, enabling the robust comparison of cognitive computations. Extensive benchmarking demonstrates state-of-the-art within- and across-animal decoding accuracy of MARBLE compared to current representation learning approaches, with minimal user input. Our results suggest that a manifold structure provides a powerful inductive bias to develop decoding algorithms and assimilate data across experiments.
Date Issued
2025-03-01
Date Acceptance
2024-11-26
Citation
Nature Methods, 2025, 22 (3), pp.612-620
ISSN
1548-7091
Publisher
Nature Research
Start Page
612
End Page
620
Journal / Book Title
Nature Methods
Volume
22
Issue
3
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39962310
PII: 10.1038/s41592-024-02582-2
Publication Status
Published
Coverage Spatial
United States
Date Publish Online
2025-02-17