ArticleNature methods2025
MARBLE: interpretable representations of neural population dynamics using geometric deep learning.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
- Shared latent representations of speech production for cross-patient speech decoding.Nature communications · 2026Article
- Self-supervised reservoir computing with spatial-temporal encoding for identifying critical transitions.Nature communications · 2026Article
- Comparing Dynamical Models Through Diffeomorphic Vector Field Alignment.Neural computation · 2026Article
- Primate neuroethology: a new synthesis.Trends in cognitive sciences · 2026Review
- Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.Neural computation · 2026Article
- Dynamic Compression Flows for Neuroscience Data.bioRxiv : the preprint server for biology · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- InputDSA : Demixing then comparing recurrent and externally driven dynamics.... International Conference on Learning Representations · 2026Article
- Model Predictive Control on the Neural Manifold.Neural computation · 2025Article
- Article
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
- Ultralow-Dimensionality Reduction for Identifying Critical Transitions by Spatial-Temporal PCA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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5 authors.
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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.
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