Cited by 1,457Neural dynamics and brain functionVisual perception and processing mechanismsBayesian Modeling and Causal Inference

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Bayesian inference with probabilistic population codes

Ma, Wei Ji, Beck, Jeffrey M, Latham, Peter E, Pouget, Alexandre·2006·Nature Neuroscience

DOI 10.1038/nn1790

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Abstract

Recent psychophysical experiments indicate that humans perform near-optimal Bayesian inference in a wide variety of tasks, ranging from cue integration to decision making to motor control. This implies that neurons both represent probability distributions and combine those distributions according to a close approximation to Bayes' rule. At first sight, it would seem that the high variability in the responses of cortical neurons would make it difficult to implement such optimal statistical inference in cortical circuits. We argue that, in fact, this variability implies that populations of neurons automatically represent probability distributions over the stimulus, a type of code we call probabilistic population codes. Moreover, we demonstrate that the Poisson-like variability observed in cortex reduces a broad class of Bayesian inference to simple linear combinations of populations of neural activity. These results hold for arbitrary probability distributions over the stimulus, for tuning curves of arbitrary shape and for realistic neuronal variability.

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Authors: Ma, W. J., Beck, J. M., Latham, P. E., & Pouget, A.
Affiliations: University of Rochester (US)
National Hospital for Neurology and Neurosurgery (GB)
Corresponding author: Pouget, Alexandre
ORCID: Ma, Wei Ji
Beck, Jeffrey M
Latham, Peter E
Pouget, Alexandre
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2006-10-22
Volume: 9
Issue: 11
Pages: 1432-1438
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/nn1790
OpenAlex: W2133877521
Cited by: 1,457
References: 49
Citations by year: 2026: 55 · 2025: 79 · 2024: 63 · 2023: 87 · 2022: 62 · 2021: 81 · 2020: 69 · 2019: 93 · 2018: 76 · 2017: 96 · 2016: 104 · 2015: 69 · 2014: 93 · 2013: 74 · 2012: 78
FWCI: 19.76
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Funders: National Institute of Mental Health (R01MH062447, T32MH019942, R01 MH62447, T32 MH19942)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: Bayesian inference, neural population codes, neural variability, shot noise, Bayes' theorem, cue integration, cortical circuits, neural coding
MeSH terms: Algorithms, Bayes Theorem, Cerebral Cortex, Humans, Models, Neurological, Nerve Net, Models, Statistical, Normal Distribution, Poisson Distribution

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Bayesian inference with probabilistic population codes

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