ArticleProceedings of the National Academy of Sciences of the United States of America2024
Chaotic neural dynamics facilitate probabilistic computations through sampling.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 22 citations in OpenAlex.
- Synchrony within Aβ mechanoreceptor subtypes governs signal propagation to primary somatosensory cortex.The Journal of physiology · 2026Article
- Multi-chaotic signal identification employing a causal cross-correlation neural network.iScience · 2026Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.Nature communications · 2026Article
- Decoding self-motion: insights into visual-vestibular causal inference.Frontiers in human neuroscience · 2026Review
- Focal thalamic infrared neural stimulation propagates dynamical transformations in auditory cortex.Journal of neural engineering · 2025Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Unraveling Audiovisual Perception Across Space and Time: A Neuroinspired Computational Architecture.The European journal of neuroscience · 2025Article
- Protected Chaos in a Topological Lattice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Computation-through-Dynamics Benchmark: Simulated datasets and quality metrics for dynamical models of neural activity.bioRxiv : the preprint server for biology · 2025Article
- Identification of recurrent dynamics in distributed neural populations.PLoS computational biology · 2025Article
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
Abstract
Cortical neurons exhibit highly variable responses over trials and time. Theoretical works posit that this variability arises potentially from chaotic network dynamics of recurrently connected neurons. Here, we demonstrate that chaotic neural dynamics, formed through synaptic learning, allow networks to perform sensory cue integration in a sampling-based implementation. We show that the emergent chaotic dynamics provide neural substrates for generating samples not only of a static variable but also of a dynamical trajectory, where generic recurrent networks acquire these abilities with a biologically plausible learning rule through trial and error. Furthermore, the networks generalize their experience in the stimulus-evoked samples to the inference without partial or all sensory information, which suggests a computational role of spontaneous activity as a representation of the priors as well as a tractable biological computation for marginal distributions. These findings suggest that chaotic neural dynamics may serve for the brain function as a Bayesian generative model.
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