The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Humans integrate visual and haptic information in a statistically optimal fashion
Ernst, M. O., & Banks, M. S. (2002) · Nature · doi:10.1038/415429a · Cited by 4,844
Neural Information Processing Systems
(2018) · The MIT Press eBooks · doi:10.7551/mitpress/11474.003.0014 · Cited by 3,776
Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
(1994) · Choice Reviews Online · doi:10.5860/choice.31-4365 · Cited by 1,897
Normalization of cell responses in cat striate cortex
Heeger, D. J. (1992) · Visual Neuroscience · doi:10.1017/s0952523800009640 · Cited by 1,835
Neural Basis of a Perceptual Decision in the Parietal Cortex (Area LIP) of the Rhesus Monkey
Shadlen, M. N., & Newsome, W. T. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1916 · Cited by 1,755
Neural correlates of decision variables in parietal cortex
Platt, M. L., & Glimcher, P. W. (1999) · Nature · doi:10.1038/22268 · Cited by 1,721
Perception as Bayesian Inference
(1996) · doi:10.1017/cbo9780511984037 · Cited by 1,425
The statistical reliability of signals in single neurons in cat and monkey visual cortex
Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983) · Vision Research · doi:10.1016/0042-6989(83)90200-6 · Cited by 1,002
Neural computations that underlie decisions about sensory stimuli
Gold, J. I., & Shadlen, M. N. (2001) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01567-9 · Cited by 881
Flagged first, then by citations
Research works printed but not linked: 2
- Hinton, G.E. in Proceedings of the Ninth International Conference on Artificial Neural Network 1–6 (IEEE, London, England, 1999).
- Hoyer, P.O. & Hyvarinen, A. in Neural Information Processing Systems 277–284 (MIT Press, Cambridge, Massachusetts, 2003).
Cited by
Sorted by citations
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
Faisal, A. A., Selen, L. P. J., & Wolpert, D. M. (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2258 · Cited by 2,883
The free-energy principle: a rough guide to the brain?
Friston, K. (2009) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2009.04.005 · Cited by 1,831
The cognitive neuroscience of ageing
Grady, C. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3256 · Cited by 1,286
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
