ArticleNature neuroscience2024
Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks.
Article in Nature neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics.iScience · 2026Review
- Neural Reward Processing in Digital Addiction: A Dynamical Systems Theory of Reward Instability.Brain sciences · 2026Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.Nature communications · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and Optophysiology Simulation Testbed.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- InputDSA : Demixing then comparing recurrent and externally driven dynamics.... International Conference on Learning Representations · 2026Article
- Leveraging insights from neuroscience to build adaptive artificial intelligence.Nature neuroscience · 2026Review
- Broadband synergy versus oscillatory redundancy in the visual cortex.bioRxiv : the preprint server for biology · 2025Article
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- Selective coupling and decoupling coordinate distributed brain networks for precise action.bioRxiv : the preprint server for biology · 2025Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.ArXiv · 2025Article
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- Article
- Neuronal Decoding of Decisions in Multidimensional Feature Space Using a Gated Recurrent Variational Autoencoder.bioRxiv : the preprint server for biology · 2025Article
- Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed.bioRxiv : the preprint server for biology · 2025Article
- Prioritized learning of cross-population neural dynamics.Journal of neural engineering · 2025Article
- Dynamical Modeling of Behaviorally Relevant Spatiotemporal Patterns in Neural Imaging Data.Proceedings of machine learning research · 2025Article
- Neural Encoding and Decoding at Scale.ArXiv · 2025Article
- BRAID: Input-driven nonlinear dynamical modeling of neural-behavioral data.... International Conference on Learning Representations · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural-behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural-behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural-behavioral data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.