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Bayesian inference with probabilistic population codes
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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“The agreement between this simple model and the data affirms the plausibility of the “Bayesian sequential sampling” framework (41).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [41]
In the citing paper
“From this perspective, our neural recordings support the idea of a “Bayesian brain” (66) and a neural mechanism of decision making that does not flip into a fixed point or attractor state but instead approximates the formation of a probability distribution (41, 67).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [41]
In the citing paper
“It is probably also consistent with other models that exploit a broader population of LIP neurons to encode posterior probability (22, 41).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [41]
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Metadata
- 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
- Venue
- Nature Neuroscience
- Published
- 2006-10-22
- Volume
- 9
- Issue
- 11
- Pages
- 1432-1438
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- 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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