ArticleNature neuroscience2020
Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference.
Article in Nature neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed, 131 citations in OpenAlex.
- Locomotion optimizes sensory representations through a computational principle shared by rodents and primates.Science advances · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- 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
- Size tuning of neural response variability in laminar circuits of macaque primary visual cortex.eLife · 2026Article
- Natural scene segmentation dynamics reveal iterative Bayesian inference.bioRxiv : the preprint server for biology · 2026Article
- Increased Perceptual Reliability Reduces Membrane Potential Variability in Cortical Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- The neural basis for uncertainty processing in hierarchical decision making.Nature communications · 2025Article
- Neural variability reliably encodes interindividual differences in the perception of pain intensity.PLoS biology · 2025Article
- Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Article
- Relating natural image statistics to patterns of response covariability in macaque primary visual cortex.Nature communications · 2025Article
- Spatial reasoning via recurrent neural dynamics in mouse retrosplenial cortex.Nature neuroscience · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- Nonlinear recurrent inhibition through facilitating serotonin release in the raphe.Nature neuroscience · 2025Article
- Stabilized Supralinear Network Model of Responses to Surround Stimuli in Primary Visual Cortex.eNeuro · 2025Article
- Expectation-driven sensory adaptations support enhanced acuity during categorical perception.Nature neuroscience · 2025Article
- Large-scale interactions in predictive processing: oscillatory versus transient dynamics.Trends in cognitive sciences · 2025Review
- Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory-inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization and stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of recordings from awake monkeys. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function-fast sampling-based inference-and predict further properties of these motifs that can be tested in future experiments.
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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 OpenQuestion graph.