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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.
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“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
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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
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Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J., & Cohen, M. R. (2018) · Science · doi:10.1126/science.aao0284 · Cited by 188
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Kriegeskorte, N., & Wei, X.-X. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00502-3 · Cited by 187
Reservoir Computing Properties of Neural Dynamics in Prefrontal Cortex
Enel, P., Procyk, E., Quilodran, R., & Dominey, P. F. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004967 · Cited by 186
Generating Spike Trains with Specified Correlation Coefficients
Macke, J. H., Berens, P., Ecker, A. S., Tolias, A. S., & Bethge, M. (2008) · Neural Computation · doi:10.1162/neco.2008.02-08-713 · Cited by 190
Separability and geometry of object manifolds in deep neural networks
Cohen, U., Chung, S., Lee, D. D., & Sompolinsky, H. (2020) · Nature Communications · doi:10.1038/s41467-020-14578-5 · Cited by 201
Measuring Integrated Information from the Decoding Perspective
Oizumi, M., Amari, S.-I., Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004654 · Cited by 184
Inhibitory stabilization and cortical computation
Sadeh, S., & Clopath, C. (2020) · Nature reviews. Neuroscience · doi:10.1038/s41583-020-00390-z · Cited by 191
Wimmer, K., Compte, A., Roxin, A., Peixoto, D., Renart, A., & de la Rocha, J. (2015) · Nature Communications · doi:10.1038/ncomms7177 · Cited by 184
Control of working memory by phase–amplitude coupling of human hippocampal neurons
Daume, J., Kamiński, J., Gómez-Palacio-Schjetnan, A., Salimpour, Y., Khan, U., Kyzar, M., Reed, C. M., Anderson, W. S., Valiante, T. A., Mamelak, A. N., et al. (2024) · Nature · doi:10.1038/s41586-024-07309-z · Cited by 187
An increase of inhibition drives the developmental decorrelation of neural activity
Chini, M., Pfeffer, T. A., & Hanganu‐Opatz, I. L. (2022) · eLife · doi:10.7554/elife.78811 · Cited by 192
Alpha/beta power decreases track the fidelity of stimulus-specific information
Griffiths, B., Mayhew, S. D., Mullinger, K. J., Jorge, J., Charest, I., Wimber, M., & Hanslmayr, S. (2019) · eLife · doi:10.7554/elife.49562 · Cited by 184
Altered Cortical Ensembles in Mouse Models of Schizophrenia
Hamm, J. P., Peterka, D. S., Gogos, J. A., & Yuste, R. (2017) · Neuron · doi:10.1016/j.neuron.2017.03.019 · Cited by 175
Mapping a Complete Neural Population in the Retina
Marre, O., Amodei, D., Deshmukh, N. R., Sadeghi, K., Soo, F. S., Holy, T. E., & Berry, M. J. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0723-12.2012 · Cited by 173
Impact of Network Structure and Cellular Response on Spike Time Correlations
Trousdale, J., Hu, Y., Shea‐Brown, E., & Josić́, K. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002408 · Cited by 179
High-precision coding in visual cortex
Stringer, C., Michaelos, M., Tsyboulski, D. A., Lindo, S. E., & Pachitariu, M. (2021) · Cell · doi:10.1016/j.cell.2021.03.042 · Cited by 184
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Adesnik, H., & Abdeladim, L. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00902-9 · Cited by 174
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Zhou, S., & Yu, Y. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00046 · Cited by 178
Maximizing Sensory Dynamic Range by Tuning the Cortical State to Criticality
Gautam, S. H., Hoang, T. T., McClanahan, K., Grady, S. K., & Shew, W. L. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004576 · Cited by 164
Sparse and powerful cortical spikes
Wolfe, J. H., Houweling, A. R., & Brecht, M. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.03.006 · Cited by 168
Inferring decoding strategies from choice probabilities in the presence of correlated variability
Haefner, R. M., Gerwinn, S., Macke, J. H., & Bethge, M. (2013) · Nature Neuroscience · doi:10.1038/nn.3309 · Cited by 163
Origin of information-limiting noise correlations
Kanitscheider, I., Coen-Cagli, R., & Pouget, A. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508738112 · Cited by 166
Self-citationPeeling the Onion of Brain Representations
Kriegeskorte, N., & Diedrichsen, J. (2019) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061906 · Cited by 173
Classical Statistics and Statistical Learning in Imaging Neuroscience
Bzdok, D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00543 · Cited by 158
Decoding of MSTd Population Activity Accounts for Variations in the Precision of Heading Perception
Gu, Y., Fetsch, C. R., Adeyemo, B., DeAngelis, G. C., & Angelaki, D. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.04.026 · Cited by 165
Pharmacological Fingerprints of Contextual Uncertainty
Marshall, L., Mathys, C., Ruge, D., de Berker, A. O., Dayan, P., Stephan, K. E., & Bestmann, S. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002575 · Cited by 158
Zhaoping, L. (2014) · doi:10.1093/acprof:oso/9780199564668.001.0001 · Cited by 160
Plasticity-driven individualization of olfactory coding in mushroom body output neurons
Hige, T., Aso, Y., Rubin, G. M., & Turner, G. C. (2015) · Nature · doi:10.1038/nature15396 · Cited by 160
A computational perspective on autism
Rosenberg, A., Patterson, J. S., & Angelaki, D. E. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510583112 · Cited by 156
Perceptual Learning Selectively Refines Orientation Representations in Early Visual Cortex
Jehee, J. F. M., Ling, S., Swisher, J. D., van Bergen, R. S., & Tong, F. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.6112-11.2012 · Cited by 161
The what, where and how of delay activity
Sreenivasan, K. K., & D’Esposito, M. (2019) · Nature reviews. Neuroscience · doi:10.1038/s41583-019-0176-7 · Cited by 168
Ratté, S., Hong, S., Schutter, E. D., & Prescott, S. A. (2013) · Neuron · doi:10.1016/j.neuron.2013.05.030 · Cited by 164
Efficient coding in heterogeneous neuronal populations
Chelaru, M. I., & Dragoi, V. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807744105 · Cited by 155
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Cole, M. W., Ito, T., Cocuzza, C. V., & Sánchez-Romero, R. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1713-20.2021 · Cited by 164
Herrero, J. L., Gieselmann, M. A., Sanayei, M., & Thiele, A. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.029 · Cited by 154
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Rabinowitz, N. C., Willmore, B. D. B., King, A. J., & Schnupp, J. W. H. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001710 · Cited by 149
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Carboni, R., & Ielmini, D. (2019) · Advanced Electronic Materials · doi:10.1002/aelm.201900198 · Cited by 150
Information Processing and Dynamics in Minimally Cognitive Agents
Beer, R. D., & Williams, P. L. (2014) · Cognitive Science · doi:10.1111/cogs.12142 · Cited by 150
Differential Encoding of Time by Prefrontal and Striatal Network Dynamics
Bakhurin, K. I., Goudar, V., Shobe, J. L., Claar, L. D., Buonomano, D. V., & Masmanidis, S. C. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.1789-16.2016 · Cited by 155
Bellay, T., Klaus, A., Seshadri, S., & Plenz, D. (2015) · eLife · doi:10.7554/elife.07224 · Cited by 148
Feedback determines the structure of correlated variability in primary visual cortex
Bondy, A., Haefner, R. M., & Cumming, B. G. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0089-1 · Cited by 146
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Bromberg-Martin, E. S., Hikosaka, O., & Nakamura, K. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.0015-10.2010 · Cited by 147
Origins of correlated activity in an olfactory circuit
Kazama, H., & Wilson, R. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2376 · Cited by 147
Neuronal oscillations: unavoidable and useful?
Singer, W. (2017) · European Journal of Neuroscience · doi:10.1111/ejn.13796 · Cited by 153
Closed-loop all-optical interrogation of neural circuits in vivo
Zhang, Z., Russell, L. E., Packer, A. M., Gauld, O. M., & Häusser, M. (2018) · Nature Methods · doi:10.1038/s41592-018-0183-z · Cited by 147
Devilbiss, D. M., & Waterhouse, B. D. (2010) · Journal of Neurophysiology · doi:10.1152/jn.00445.2010 · Cited by 139
Small Networks Encode Decision-Making in Primary Auditory Cortex
Francis, N. A., Winkowski, D. E., Sheikhattar, A., Armengol, K., Babadi, B., & Kanold, P. O. (2018) · Neuron · doi:10.1016/j.neuron.2018.01.019 · Cited by 145
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Cichy, R. M., & Oliva, A. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.001 · Cited by 138
Accessing the Neural Drive to Muscle and Translation to Neurorehabilitation Technologies
Farina, D., & Negro, F. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2183586 · Cited by 137
Noise correlations improve response fidelity and stimulus encoding
Cafaro, J., & Rieke, F. M. (2010) · Nature · doi:10.1038/nature09570 · Cited by 140
Contini, E. W., Wardle, S. G., & Carlson, T. A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.02.013 · Cited by 142
Najafi, F., Elsayed, G. F., Cao, R., Pnevmatikakis, E. A., Latham, P. E., Cunningham, J. P., & Churchland, A. K. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.045 · Cited by 136
Self-citationSegregation of Tactile Input Features in Neurons of the Cuneate Nucleus
Jörntell, H., Bengtsson, F., Geborek, P., Spanne, A., Terekhov, A. V., & Hayward, V. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.038 · Cited by 134
Decoding Visual Inputs From Multiple Neurons in the Human Temporal Lobe
Quiroga, R. Q., Reddy, L., Koch, C., & Fried, I. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00125.2007 · Cited by 131
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Smith, M. A., Jia, X., Zandvakili, A., & Kohn, A. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00846.2012 · Cited by 129
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Winkowski, D. E., & Kanold, P. O. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3101-12.2013 · Cited by 129
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Franke, F., Michele, F., Sevelev, M., Roska, B., Hierlemann, A. R., & da Silveira, R. A. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.037 · Cited by 135
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Montijn, J. S., Meijer, G. T., Lansink, C. S., & Pennartz, C. M. A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.07.065 · Cited by 126
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Bejjanki, V. R., Beck, J. M., Lu, Z., & Pouget, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2796 · Cited by 127
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Martin, A. E. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00120 · Cited by 130
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Poulet, J. F. A., & Crochet, S. (2019) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2018.00064 · Cited by 126
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Engelhard, B., Ozeri-Engelhard, N., Israel, Z. H., Bergman, H., & Vaadia, E. (2013) · Neuron · doi:10.1016/j.neuron.2012.11.015 · Cited by 121
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Tremblay, S., Pieper, F., Sachs, A. J., & Martínez-Trujillo, J. (2014) · Neuron · doi:10.1016/j.neuron.2014.11.021 · Cited by 124
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Recurrent dynamics in the cerebral cortex: Integration of sensory evidence with stored knowledge
Singer, W. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101043118 · Cited by 114
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Weisenburger, S., & Vaziri, A. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-072116-031458 · Cited by 110
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Iurilli, G., & Datta, S. R. (2017) · Neuron · doi:10.1016/j.neuron.2017.02.010 · Cited by 107
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Townsend, R. G., & Gong, P. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006643 · Cited by 107
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Medina, J. F., & Lisberger, S. G. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.1323-07.2007 · Cited by 108
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Berens, P., Ecker, A. S., Cotton, R. J., Ma, W. J., Bethge, M., & Tolias, A. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1335-12.2012 · Cited by 109
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Funamizu, A., Kühn, B., & Doya, K. (2016) · Nature Neuroscience · doi:10.1038/nn.4390 · Cited by 109
Sakura, M., Lambrinos, D., & Labhart, T. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00784.2007 · Cited by 106
Montani, F., Ince, R. A. A., Senatore, R., Arabzadeh, E., Diamond, M. E., & Panzeri, S. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2009.0082 · Cited by 106
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Tsuchiya, N., Kawasaki, H., Oya, H., Howard, M. A., & Adolphs, R. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003892 · Cited by 102
A principle of economy predicts the functional architecture of grid cells
Wei, X.-X., Prentice, J., & Balasubramanian, V. (2015) · eLife · doi:10.7554/elife.08362 · Cited by 108
Inhibitory control of correlated intrinsic variability in cortical networks
Stringer, C., Pachitariu, M., Steinmetz, N. A., Okun, M., Barthó, P., Harris, K. D., Sahani, M., & Lesica, N. A. (2016) · eLife · doi:10.7554/elife.19695 · Cited by 103
Visual Decision-Making in an Uncertain and Dynamic World
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Information-Limiting Correlations in Large Neural Populations
Bartolo, R., Saunders, R. C., Mitz, A. R., & Averbeck, B. B. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2072-19.2019 · Cited by 103
Self-citationLocal field potentials indicate network state and account for neuronal response variability
Kelly, R. C., Smith, M. A., Kass, R. E., & Lee, T. S. (2010) · Journal of Computational Neuroscience · doi:10.1007/s10827-009-0208-9 · Cited by 101
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 ›
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