Cited by 1,743Neural dynamics and brain functionstochastic dynamics and bifurcationMemory and Neural Mechanisms

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Neural correlations, population coding and computation

Averbeck, Bruno B., Latham, Peter E., Pouget, Alexandre·2006·Nature reviews. Neuroscience

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Abstract

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.

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A Mathematical Theory of Communication
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A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex
Hubel, D. H., & Wiesel, T. N. (1962) · The Journal of Physiology · doi:10.1113/jphysiol.1962.sp006837 · Cited by 13,680
The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,882
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
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Hyvärinen, A., Hoyer, P. O., & Inki, M. (2004) · The MIT Press eBooks · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,938
Statistical Inference
Casella, G., & Berger, R. L. (1993) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003456285 · Cited by 5,835
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
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Hyvärinen, A., Karhunen, J., & Erkki Oja, A. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,003
Neuronal Population Coding of Movement Direction
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986) · Science · doi:10.1126/science.3749885 · Cited by 3,219

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Self-citation
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Authors: Averbeck, B. B., Latham, P. E., & Pouget, A.
Affiliations: University of Rochester (US)
Gatsby Computational Neuroscience Unit (GB)
Corresponding author: Pouget, Alexandre
ORCID: Averbeck, Bruno B.
Latham, Peter E.
Pouget, Alexandre
Venue: Nature reviews. Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2006-05
Volume: 7
Issue: 5
Pages: 358-366
ISSN: 1471-003X (ISSN-L)
1471-0048
DOI: 10.1038/nrn1888
OpenAlex: W1998050452
Cited by: 1,743
References: 78
Citations by year: 2026: 78 · 2025: 103 · 2024: 123 · 2023: 96 · 2022: 77 · 2021: 106 · 2020: 97 · 2019: 127 · 2018: 109 · 2017: 96 · 2016: 105 · 2015: 102 · 2014: 89 · 2013: 96 · 2012: 77
FWCI: 3.38
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature reviews. Neuroscience (publishedVersion)
PubMed (publishedVersion)
UCL Discovery (University College London) (submittedVersion)
Funders: National Science Foundation
National Institutes of Health
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
Keywords: neural correlations, neural noise, population coding, population activity, neural computation, neural variability, multi-electrode recording, information encoding
MeSH terms: Action Potentials, Animals, Brain, Humans, Models, Neurological, Nerve Net, Neurons, Neural Networks, Computer

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