ArticleNature communications2022
Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Combining sampling and attractor dynamics in spiking models of head direction systems.PLoS computational biology · 2026Article
- Learning and inference with correlated neural variability.PNAS nexus · 2025Article
- Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention.Communications biology · 2024Article
- Chaotic neural dynamics facilitate probabilistic computations through sampling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Fractional neural sampling as a theory of spatiotemporal probabilistic computations in neural circuits.Nature communications · 2022Article
- Natural gradient enables fast sampling in spiking neural networks.Advances in neural information processing systems · 2022Article
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2 authors.
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Abstract
A range of perceptual and cognitive processes have been characterized from the perspective of probabilistic representations and inference. To understand the neural circuit mechanism underlying these probabilistic computations, we develop a theory based on complex spatiotemporal dynamics of neural population activity. We first implement and explore this theory in a biophysically realistic, spiking neural circuit. Population activity patterns emerging from the circuit capture realistic variability or fluctuations of neural dynamics both in time and in space. These activity patterns implement a type of probabilistic computations that we name fractional neural sampling (FNS). We further develop a mathematical model to reveal the algorithmic nature of FNS and its computational advantages for representing multimodal distributions, a major challenge faced by existing theories. We demonstrate that FNS provides a unified account of a diversity of experimental observations of neural spatiotemporal dynamics and perceptual processes such as visual perception inference, and that FNS makes experimentally testable predictions.
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