The DOI matches its Crossref record; 77 of the 78 references it cites were found in a registry or an index.
Neural correlations, population coding and computation
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.
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
“Under conditions where single neurons are noisy and unreliable or when diverse stimulus features need to be integrated, population coding can be more effective (Averbeck et al., 2006).”
From Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice (2017) · cites this paper as Averbeck et al., 2006
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
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 9,885
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
Independent Component Analysis
Hyvärinen, A., Hoyer, P. O., & Inki, M. (2004) · The MIT Press eBooks · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,938
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
Independent Component Analysis
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
Cited by
Sorted by citations
Items 1,001–1,001 of 1,001
A physiological perspective on the neuroscience of eating
Geary, N. (2014) · Physiology & Behavior · doi:10.1016/j.physbeh.2014.03.022 · Cited by 7
Metadata
- Authors
- Averbeck, B. B., Latham, P. E., & Pouget, A.
- Affiliations
- University of Rochester (US)
- Gatsby Computational Neuroscience Unit (GB)
- Corresponding author
- Pouget, Alexandre
- Venue
- Nature reviews. Neuroscience
- Published
- 2006-05
- Volume
- 7
- Issue
- 5
- Pages
- 358-366
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- 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
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