@article{gosztolai_marble_2025,
	title = {{MARBLE}: interpretable representations of neural population dynamics using geometric deep learning},
	copyright = {2025 The Author(s)},
	issn = {1548-7105},
	shorttitle = {{MARBLE}},
	url = {https://www.nature.com/articles/s41592-024-02582-2},
	doi = {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.},
	language = {en},
	urldate = {2025-02-18},
	journal = {Nature Methods},
	author = {Gosztolai, Adam and Peach, Robert L. and Arnaudon, Alexis and Barahona, Mauricio and Vandergheynst, Pierre},
	month = feb,
	year = {2025},
	keywords = {Computational neuroscience, Computational platforms and environments, Machine learning, Software},
	pages = {1--9},
}