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
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition
Wang, X.-J. (2010) · Physiological Reviews · doi:10.1152/physrev.00035.2008 · Cited by 1,939
Pathological synchronization in Parkinson's disease: networks, models and treatments
Hammond, C., Bergman, H., & Brown, P. (2007) · Trends in Neurosciences · doi:10.1016/j.tins.2007.05.004 · Cited by 1,619
Whole-brain functional imaging at cellular resolution using light-sheet microscopy
Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M., & Keller, P. J. (2013) · Nature Methods · doi:10.1038/nmeth.2434 · Cited by 1,392
Decoding reveals the contents of visual working memory in early visual areas
Harrison, S. A., & Tong, F. (2009) · Nature · doi:10.1038/nature07832 · Cited by 1,347
Spatial Neglect and Attention Networks
Corbetta, M., & Shulman, G. L. (2011) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-061010-113731 · Cited by 1,318
Top-down influences on visual processing
Gilbert, C. D., & Li, W. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3476 · Cited by 1,224
Neuromorphic computing using non-volatile memory
Burr, G. W., Shelby, R. M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K. R., Ishii, M., Narayanan, P., Fumarola, A., et al. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1259585 · Cited by 1,196
Attention improves performance primarily by reducing interneuronal correlations
Cohen, M. R., & Maunsell, J. H. R. (2009) · Nature Neuroscience · doi:10.1038/nn.2439 · Cited by 1,163
Dynamic Shifts of Limited Working Memory Resources in Human Vision
Bays, P. M., & Husain, M. (2008) · Science · doi:10.1126/science.1158023 · Cited by 1,177
Stochastic phase-change neurons
Tůma, T., Pantazi, A., Gallo, M. L., Sebastian, A., & Eleftheriou, E. S. (2016) · Nature Nanotechnology · doi:10.1038/nnano.2016.70 · Cited by 1,107
Measuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,059
Representational geometry: integrating cognition, computation, and the brain
Kriegeskorte, N., & Kievit, R. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.06.007 · Cited by 898
Extracting information from neuronal populations: information theory and decoding approaches
Quiroga, R. Q., & Panzeri, S. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2578 · Cited by 827
Why neurons mix: high dimensionality for higher cognition
Fusi, S., Miller, E. K., & Rigotti, M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.01.010 · Cited by 766
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.013 · Cited by 710
Electrophysiological correlates of the brain's intrinsic large-scale functional architecture
He, B. J., Snyder, A. Z., Zempel, J. M., Smyth, M. D., & Raichle, M. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0807010105 · Cited by 670
Correlation between neural spike trains increases with firing rate
de la Rocha, J., Doiron, B., Shea-Brown, E., Josić, K., & Reyes, A. (2007) · Nature · doi:10.1038/nature06028 · Cited by 644
High-dimensional geometry of population responses in visual cortex
Stringer, C., Pachitariu, M., Steinmetz, N. A., Carandini, M., & Harris, K. D. (2019) · Nature · doi:10.1038/s41586-019-1346-5 · Cited by 640
Spatial and Temporal Scales of Neuronal Correlation in Primary Visual Cortex
Smith, M. A., & Kohn, A. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.2929-08.2008 · Cited by 600
Laminar Structure of Spontaneous and Sensory-Evoked Population Activity in Auditory Cortex
Sakata, S., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.020 · Cited by 604
How advances in neural recording affect data analysis
Stevenson, I. H., & Kording, K. P. (2011) · Nature Neuroscience · doi:10.1038/nn.2731 · Cited by 590
Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M.-A., Morito, Y., Tanabe, H. C., Sadato, N., & Kamitani, Y. (2008) · Neuron · doi:10.1016/j.neuron.2008.11.004 · Cited by 543
Information-limiting correlations
Moreno‐Bote, R., Beck, J. M., Kanitscheider, I., Pitkow, X., Latham, P. E., & Pouget, A. (2014) · Nature Neuroscience · doi:10.1038/nn.3807 · Cited by 565
Self-citationInformation Transduction Capacity of Noisy Biochemical Signaling Networks
Cheong, R., Rhee, A., Wang, C. J., Nemenman, I. M., & Levchenko, A. (2011) · Science · doi:10.1126/science.1204553 · Cited by 534
Distinct behavioural and network correlates of two interneuron types in prefrontal cortex
Kvitsiani, D., Ranade, S., Hangya, B., Taniguchi, H., Huang, J. Z., & Kepecs, A. (2013) · Nature · doi:10.1038/nature12176 · Cited by 516
Spike-Phase Coding Boosts and Stabilizes Information Carried by Spatial and Temporal Spike Patterns
Kayser, C., Montemurro, M. A., Logothetis, N. K., & Panzeri, S. (2009) · Neuron · doi:10.1016/j.neuron.2009.01.008 · Cited by 507
Neural mechanisms of selective visual attention.
Moore, T., & Zirnsak, M. (2017) · Annual Review of Psychology · doi:10.1146/annurev-psych-122414-033400 · Cited by 471
Neural correlates of reliability-based cue weighting during multisensory integration
Fetsch, C. R., Pouget, A., DeAngelis, G. C., & Angelaki, D. E. (2012) · Nature Neuroscience · doi:10.1038/nn.2983 · Cited by 459
Self-citationMallet, N. P., Pogosyan, A. H., Sharott, A., Csicsvari, J., Bolam, J. P., Brown, P., & Magill, P. J. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0123-08.2008 · Cited by 475
Spintronic Nanodevices for Bioinspired Computing
Grollier, J., Querlioz, D., & Stiles, M. D. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2597152 · Cited by 447
Hebart, M. N., Görgen, K., & Haynes, J. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00088 · Cited by 455
Belitski, A., Gretton, A., Magri, C., Murayama, Y., Montemurro, M. A., Logothetis, N. K., & Panzeri, S. (2008) · Journal of Neuroscience · doi:10.1523/jneurosci.0009-08.2008 · Cited by 420
Ecker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., Maltezos, S., Murphy, C. M., Robertson, D., Williams, S. C., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.5413-09.2010 · Cited by 415
Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex
Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K., & Poggio, T. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90248.2008 · Cited by 416
Correcting for the Sampling Bias Problem in Spike Train Information Measures
Panzeri, S., Senatore, R., Montemurro, M. A., & Petersen, R. S. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00559.2007 · Cited by 404
Neuromorphic computing at scale
Kudithipudi, D., Schuman, C. D., Vineyard, C. M., Pandit, T., Merkel, C. E., Kubendran, R. C., Aimone, J. B., Orchard, G., Mayr, C., Benosman, R. B., et al. (2025) · Nature · doi:10.1038/s41586-024-08253-8 · Cited by 441
Functional organization and population dynamics in the mouse primary auditory cortex
Rothschild, G., Nelken, I., & Mizrahi, A. (2010) · Nature Neuroscience · doi:10.1038/nn.2484 · Cited by 410
Nieder, A. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.40 · Cited by 406
Macroscopic Description for Networks of Spiking Neurons
Montbrió, E., Pazó, D., & Roxin, A. (2015) · Physical Review X · doi:10.1103/physrevx.5.021028 · Cited by 375
Space and time in visual context
Schwartz, O., Hsu, A., & Dayan, P. (2007) · Nature reviews. Neuroscience · doi:10.1038/nrn2155 · Cited by 374
Correlations and Neuronal Population Information
Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013851 · Cited by 372
Self-citationBuzsáki, G. (2019) · doi:10.1093/oso/9780190905385.001.0001 · Cited by 383
de Vries, S. E. J., Lecoq, J. A., Buice, M. A., Groblewski, P. A., Ocker, G. K., Oliver, M., Feng, D. H., Cain, N., Ledochowitsch, P., Millman, D. J., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0550-9 · Cited by 359
Distinct timescales of population coding across cortex
Runyan, C. A., Piasini, E., Panzeri, S., & Harvey, C. D. (2017) · Nature · doi:10.1038/nature23020 · Cited by 345
Neuronal Mechanisms of Visual Attention
Maunsell, J. H. R. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035431 · Cited by 346
Time course of visual perception: Coarse-to-fine processing and beyond
HEGDE, J. (2007) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2007.09.001 · Cited by 348
Large-scale neural recordings call for new insights to link brain and behavior
Urai, A. E., Doiron, B., Leifer, A. M., & Churchland, A. K. (2022) · Nature Neuroscience · doi:10.1038/s41593-021-00980-9 · Cited by 347
State Dependence of Noise Correlations in Macaque Primary Visual Cortex
Ecker, A. S., Berens, P., Cotton, R. J., Subramaniyan, M., Denfield, G. H., Cadwell, C. R., Smirnakis, S. M., Bethge, M., & Tolias, A. S. (2014) · Neuron · doi:10.1016/j.neuron.2014.02.006 · Cited by 338
Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns
Yamashita, O., Sato, M.-A., Yoshioka, T., Tong, F., & Kamitani, Y. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.05.050 · Cited by 319
Towards the neural population doctrine
Saxena, S., & Cunningham, J. P. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.002 · Cited by 334
Deconstructing multivariate decoding for the study of brain function
Hebart, M. N., & Baker, C. I. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.005 · Cited by 315
Dopamine neurons share common response function for reward prediction error
Eshel, N., Tian, J., Bukwich, M., & Uchida, N. (2016) · Nature Neuroscience · doi:10.1038/nn.4239 · Cited by 321
Williams, A. H., Kim, T. H., Wang, F. L., Vyas, S., Ryu, S. I., Shenoy, K. V., Schnitzer, M. J., Kolda, T. G., & Ganguli, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.05.015 · Cited by 320
Tutorial: Brain-inspired computing using phase-change memory devices
Sebastian, A., Gallo, M. L., Burr, G. W., Kim, S., BrightSky, M. J., & Eleftheriou, E. S. (2018) · Journal of Applied Physics · doi:10.1063/1.5042413 · Cited by 302
Mazzoni, A., Panzeri, S., Logothetis, N. K., & Brunel, N. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000239 · Cited by 309
Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice
Li, Y., Mathis, A., Grewe, B. F., Osterhout, J. A., Ahanonu, B., Schnitzer, M. J., Murthy, V. N., & Dulac, C. (2017) · Cell · doi:10.1016/j.cell.2017.10.015 · Cited by 291
The Detection of Visual Contrast in the Behaving Mouse
Busse, L., Ayaz, A., Dhruv, N. T., Katzner, S., Saleem, A. B., Schölvinck, M. L., Zaharia, A. D., & Carandini, M. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.6689-10.2011 · Cited by 297
The Role of Human Orbitofrontal Cortex in Value Comparison for Incommensurable Objects
FitzGerald, T. H. B., Seymour, B., & Dolan, R. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.0717-09.2009 · Cited by 279
Internally organized mechanisms of the head direction sense
Peyrache, A., Lacroix, M. M., Petersen, P. C., & Buzsáki, G. (2015) · Nature Neuroscience · doi:10.1038/nn.3968 · Cited by 274
Technologies for imaging neural activity in large volumes
Ji, N., Freeman, J., & Smith, S. L. (2016) · Nature Neuroscience · doi:10.1038/nn.4358 · Cited by 277
On the importance of single directions for generalization
Morcos, A. S., Barrett, D. G. T., Rabinowitz, N. C., & Botvinick, M. (2018) · arXiv · doi:10.48550/arxiv.1803.06959 · Cited by 305
How Structure Determines Correlations in Neuronal Networks
Pernice, V., Staude, B., Cardanobile, S., & Rotter, S. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002059 · Cited by 280
Effects of locomotion extend throughout the mouse early visual system
Erisken, S., Vaiceliunaite, A., Jurjut, O., Fiorini, M., Katzner, S., & Busse, L. (2014) · Current Biology · doi:10.1016/j.cub.2014.10.045 · Cited by 256
Spatial Organization of Neuronal Population Responses in Layer 2/3 of Rat Barrel Cortex
Kerr, J. N. D., de Kock, C. P. J., Greenberg, D. S., Bruno, R. M., Sakmann, B. J., & Helmchen, F. (2007) · Journal of Neuroscience · doi:10.1523/jneurosci.2210-07.2007 · Cited by 264
Decoding the activity of neuronal populations in macaque primary visual cortex
Graf, A. B. A., Kohn, A., Jazayeri, M., & Movshon, J. A. (2011) · Nature Neuroscience · doi:10.1038/nn.2733 · Cited by 254
The structures and functions of correlations in neural population codes
Panzeri, S., Moroni, M., Safaai, H., & Harvey, C. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00606-4 · Cited by 260
Moving Sensory Adaptation beyond Suppressive Effects in Single Neurons
Solomon, S. G., & Kohn, A. (2014) · Current Biology · doi:10.1016/j.cub.2014.09.001 · Cited by 247
Andersen, R. A., Hwang, E. J., & Mulliken, G. H. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.100503 · Cited by 244
The mechanics of state-dependent neural correlations
Doiron, B., Litwin-Kumar, A., Rosenbaum, R., Ocker, G. K., & Josić́, K. (2016) · Nature Neuroscience · doi:10.1038/nn.4242 · Cited by 240
Noise in Neural Populations Accounts for Errors in Working Memory
Bays, P. M. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3204-13.2014 · Cited by 250
Miura, K., Mainen, Z. F., & Uchida, N. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.021 · Cited by 241
Interpreting encoding and decoding models
Kriegeskorte, N., & Douglas, P. K. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.04.002 · Cited by 251
Synergy, redundancy, and multivariate information measures: an experimentalist’s perspective
Timme, N. M., Alford, W. T., Flecker, B., & Beggs, J. M. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0458-4 · Cited by 233
Ostojic, S., Brunel, N., & Hakim, V. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1275-09.2009 · Cited by 235
Circuit Mechanisms of Sensorimotor Learning
Makino, H., Hwang, E. J., Hedrick, N. G., & Komiyama, T. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.029 · Cited by 228
“Enhanced discriminability during perceptual learning may, for example, depend on the relationship between two forms of correlation structures in the ensemble activity: similarity in tuning properties between a pair of neurons, known as signal correlation, and trial-by-trial response fluctuations to identical stimuli,…”
Cracking the Neural Code for Sensory Perception by Combining Statistics, Intervention, and Behavior
Panzeri, S., Harvey, C. D., Piasini, E., Latham, P. E., & Fellin, T. (2017) · Neuron · doi:10.1016/j.neuron.2016.12.036 · Cited by 240
Self-citationThe Nature of Shared Cortical Variability
Lin, I.-C., Okun, M., Carandini, M., & Harris, K. D. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.035 · Cited by 232
A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordings
Magri, C., Whittingstall, K., Singh, V., Logothetis, N. K., & Panzeri, S. (2009) · BMC Neuroscience · doi:10.1186/1471-2202-10-81 · Cited by 231
Fundamental bounds on the fidelity of sensory cortical coding
Rumyantsev, O. I., Lecoq, J. A., Hernandez, O., Zhang, Y., Savall, J., Chrapkiewicz, R., Li, J., Zeng, H., Ganguli, S., & Schnitzer, M. J. (2020) · Nature · doi:10.1038/s41586-020-2130-2 · Cited by 226
Re-evaluating Circuit Mechanisms Underlying Pattern Separation
Cayco-Gajic, N. A., & Silver, R. A. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.044 · Cited by 234
Neural population coding: combining insights from microscopic and mass signals
Panzeri, S., Macke, J. H., Groß, J., & Kayser, C. (2015) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.01.002 · Cited by 236
Tudusciuc, O., & Nieder, A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705495104 · Cited by 218
Perceptual Learning Reduces Interneuronal Correlations in Macaque Visual Cortex
Gu, Y., Liu, S., Fetsch, C. R., Yang, Y., Fok, S., Sunkara, A., DeAngelis, G. C., & Angelaki, D. E. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.015 · Cited by 224
Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention
Cohen, M. R., & Maunsell, J. H. R. (2011) · Neuron · doi:10.1016/j.neuron.2011.04.029 · Cited by 223
Long-term modification of cortical synapses improves sensory perception
Froemke, R. C., Carcea, I., Barker, A. J., Yuan, K., Seybold, B., Martins, A. R. O., Zaika, N., Bernstein, H. L., Wachs, M. A., Levis, P. A., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3274 · Cited by 218
Task activations produce spurious but systematic inflation of task functional connectivity estimates
Cole, M. W., Ito, T., Schultz, D. H., Mill, R. D., Chen, R. J., & Cocuzza, C. V. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.054 · Cited by 218
Perceptual Decision-Making as Probabilistic Inference by Neural Sampling
Haefner, R. M., Berkes, P., & Fiser, J. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.020 · Cited by 207
Locomotion Enhances Neural Encoding of Visual Stimuli in Mouse V1
Dadarlat, M. C., & Stryker, M. P. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.2728-16.2017 · Cited by 205
Neural-like computing with populations of superparamagnetic basis functions
Mizrahi, F. A., Hirtzlin, T., Fukushima, A., Kubota, H., Yuasa, S., Grollier, J., & Querlioz, D. (2018) · Nature Communications · doi:10.1038/s41467-018-03963-w · Cited by 204
Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex
Nikolić, D., Häusler, S., Singer, W., & Maass, W. (2009) · PLoS Biology · doi:10.1371/journal.pbio.1000260 · Cited by 201
Chen, X., Mu, Y., Hu, Y., Kuan, A. T., Nikitchenko, M., Randlett, O., Chen, A. B., Gavornik, J. P., Sompolinsky, H., Engert, F., et al. (2018) · Neuron · doi:10.1016/j.neuron.2018.09.042 · Cited by 199
Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging
Kragel, P. A., Koban, L., Barrett, L. F., & Wager, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.06.009 · Cited by 212
Massively parallel microwire arrays integrated with CMOS chips for neural recording
Obaid, A., Hanna, M.-E., Wu, Y., Köllő, M., Rácz, R., Angle, M. R., Müller, J., Brackbill, N., Wray, W., Franke, F., et al. (2020) · Science Advances · doi:10.1126/sciadv.aay2789 · Cited by 201
Making better decisions in groups
Bang, D., & Frith, C. (2017) · Royal Society Open Science · doi:10.1098/rsos.170193 · Cited by 210
Majaj, N. J., Hong, H., Solomon, E. A., & DiCarlo, J. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5181-14.2015 · Cited by 204
Pattern-information analysis: From stimulus decoding to computational-model testing
Kriegeskorte, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.061 · Cited by 197
Action video game play facilitates the development of better perceptual templates
Bejjanki, V. R., Zhang, R., Li, R., Pouget, A., Green, C. S., Lu, Z., & Bavelier, D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1417056111 · Cited by 208
Self-citationMultiscale optical Ca2+ imaging of tonal organization in mouse auditory cortex
Issa, J. B., Haeffele, B. D., Agarwal, A., Bergles, D. E., Young, E. D., & Yue, D. T. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.009 · Cited by 188
Attention can either increase or decrease spike count correlations in visual cortex
Ruff, D. A., & Cohen, M. R. (2014) · Nature Neuroscience · doi:10.1038/nn.3835 · Cited by 196
Bowers, J. S. (2009) · Psychological Review · doi:10.1037/a0014462 · Cited by 200
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