2025-02-18 スイス連邦工科大学ローザンヌ校 (EPFL)
<関連情報>
- https://actu.epfl.ch/news/a-geometric-deep-learning-method-for-decoding-br-2/
- https://www.nature.com/articles/s41592-024-02582-2
MARBLE:幾何学的深層学習を用いた神経集団ダイナミクスの解釈可能な表現 MARBLE: interpretable representations of neural population dynamics using geometric deep learning
Adam Gosztolai,Robert L. Peach,Alexis Arnaudon,Mauricio Barahona & Pierre Vandergheynst
Nature Methods Published:17 February 2025
DOI:https://doi.org/10.1038/s41592-024-02582-2
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.
