ArticleNature communications2023
Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons.
Article in Nature communications, 2023. 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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Who cites it
10 citing papers in PubMed.
- Population coding under the scale invariance of high-dimensional noise.Science advances · 2026Article
- Combining sampling and attractor dynamics in spiking models of head direction systems.PLoS computational biology · 2026Article
- The neural code of perceptual inference.Molecular brain · 2026Review
- Myelin-Proteinoids Interactions in Neural Signaling.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Cooperative coding of continuous variables in networks with sparsity constraint.PLoS computational biology · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- Chaotic neural dynamics facilitate probabilistic computations through sampling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons.Nature communications · 2023Article
- Visual-Based Spatial Coordinate Dominates Probabilistic Multisensory Inference in Macaque MST-d Disparity Encoding.Brain sciences · 2022Article
- Natural gradient enables fast sampling in spiking neural networks.Advances in neural information processing systems · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.
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Registered trials
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