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.
Integrity signals
Registry
Cites
Statistics
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?
Under conditions where single neurons are noisy and unreliable or when diverse stimulus features need to be integrated, population coding can be more effective.
“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
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 · see citing works
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 49,574 · see citing works
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 · see citing works
The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,875 · see citing works
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 · see citing works
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 · see citing works
Casella, G., & Berger, R. L. (1993) · Chapman & Hall/CRC eBooks · doi:10.1201/9781003456285 · Cited by 5,835 · see citing works
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 · see citing works
Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Erkki Oja, A. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,003 · see citing works
Neuronal Population Coding of Movement Direction
Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986) · Science · doi:10.1126/science.3749885 · Cited by 3,187 · see citing works
Visual Feature Integration and the Temporal Correlation Hypothesis
Singer, W., & Gray, C. M. (1995) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.18.030195.003011 · Cited by 3,157 · see citing works
Some informational aspects of visual perception.
Attneave, F. (1954) · Psychological Review · doi:10.1037/h0054663 · Cited by 2,938 · see citing works
Natural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460 · see citing works
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237 · see citing works
The Correlation Theory of Brain Function
von der Malsburg, C. (1994) · Physics of neural networks · doi:10.1007/978-1-4612-4320-5_2 · Cited by 1,086 · see citing works
Predictive coding: a fresh view of inhibition in the retina
Srinivasan, M. V., Laughlin, S. B., & Dubs, A. (1982) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1982.0085 · Cited by 1,042 · see citing works
Entropy and Information in Neural Spike Trains
Strong, S. P., Köberle, R., van Steveninck, R. R. D. R., & Bialek, W. (1998) · Physical Review Letters · doi:10.1103/physrevlett.80.197 · Cited by 969 · see citing works
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 · see citing works
Practising orientation identification improves orientation coding in V1 neurons
Schoups, A., Vogels, R., Qian, N., & Orban, G. (2001) · Nature · doi:10.1038/35087601 · Cited by 856 · see citing works
Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
Hung, C. P., Kreiman, G., Poggio, T., & DiCarlo, J. J. (2005) · Science · doi:10.1126/science.1117593 · Cited by 854 · see citing works
The Effect of Correlated Variability on the Accuracy of a Population Code
Abbott, L. F., & Dayan, P. (1999) · Neural Computation · doi:10.1162/089976699300016827 · Cited by 833 · see citing works
Dynamics of neuronal interactions in monkey cortex in relation to behavioural events
Vaadia, E., Haalman, I., Abeles, M., Bergman, H., Prut, Y., Slovin, H., & Aertsen, A. (1995) · Nature · doi:10.1038/373515a0 · Cited by 802 · see citing works
Information processing with population codes
Pouget, A., Dayan, P., & Zemel, R. S. (2000) · Nature reviews. Neuroscience · doi:10.1038/35039062 · Cited by 744 · see citing works
Self-citationPopulation coding of saccadic eye movements by neurons in the superior colliculus
Lee, C., Rohrer, W. H., & Sparks, D. L. (1988) · Nature · doi:10.1038/332357a0 · Cited by 740 · see citing works
Braitenberg, V., & Schüz, A. (1991) · Studies of brain function · doi:10.1007/978-3-662-02728-8 · Cited by 659 · see citing works
Simple models for reading neuronal population codes.
Seung, H. S., & Sompolinsky, H. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.22.10749 · Cited by 623 · see citing works
Redundancy reduction revisited
Barlow, H. B. (2001) · Network Computation in Neural Systems · doi:10.1080/net.12.3.241.253 · Cited by 608 · see citing works
A model for visual shape recognition.
Milner, P. M. (1974) · Psychological Review · doi:10.1037/h0037149 · Cited by 598 · see citing works
Towards a Theory of Early Visual Processing
Atick, J. J., & Redlich, A. (1990) · Neural Computation · doi:10.1162/neco.1990.2.3.308 · Cited by 552 · see citing works
Synergy, Redundancy, and Independence in Population Codes
Schneidman, E., Bialek, W., & Berry, M. J. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-37-11539.2003 · Cited by 502 · see citing works
Vector reconstruction from firing rates
Salinas, E., & Abbott, L. F. (1994) · Journal of Computational Neuroscience · doi:10.1007/bf00962720 · Cited by 485 · see citing works
Attention activates winner-take-all competition among visual filters
Lee, D. K., Itti, L., Christof Koch, A., & Braun, J. (1999) · Nature Neuroscience · doi:10.1038/7286 · Cited by 458 · see citing works
The Effect of Perceptual Learning on Neuronal Responses in Monkey Visual Area V4
Yang, T., & Maunsell, J. H. R. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.4442-03.2004 · Cited by 427 · see citing works
How independent are the messages carried by adjacent inferior temporal cortical neurons?
Gawne, T. J., & Richmond, B. (1993) · Journal of Neuroscience · doi:10.1523/jneurosci.13-07-02758.1993 · Cited by 425 · see citing works
Ghosh, M., Casella, G., & Berger, R. L. (1994) · Journal of the American Statistical Association · doi:10.2307/2290879 · Cited by 416 · see citing works
Direct physiological evidence for scene segmentation by temporal coding.
Engel, A. K., Peter König, B., & Singer, W. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.20.9136 · Cited by 399 · see citing works
Reading population codes: a neural implementation of ideal observers
Denève, S., Latham, P. E., & Pouget, A. (1999) · Nature Neuroscience · doi:10.1038/11205 · Cited by 392 · see citing works
Self-citationThe Upward Bias in Measures of Information Derived from Limited Data Samples
Treves, A., & Panzeri, S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.2.399 · Cited by 352 · see citing works
Size and distribution of movement fields in the monkey superior colliculus
Sparks, D. L., Holland, R., & Guthrie, B. L. (1976) · Brain Research · doi:10.1016/0006-8993(76)90003-2 · Cited by 349 · see citing works
Correlations and the encoding of information in the nervous system
Panzeri, S., Schultz, S. R., Treves, A., & Rolls, E. T. (1999) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.1999.0736 · Cited by 341 · see citing works
Roskies, A. L. (1999) · Neuron · doi:10.1016/s0896-6273(00)80817-x · Cited by 334 · see citing works
Population coding in neuronal systems with correlated noise
Sompolinsky, H., Yoon, H., Kang, K., & Shamir, M. (2001) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.64.051904 · Cited by 319 · see citing works
Cognitive spatial-motor processes
Georgopoulos, A. P., Crutcher, M. D., & Schwartz, A. B. (1989) · Experimental Brain Research · doi:10.1007/bf00248541 · Cited by 318 · see citing works
Retinal ganglion cells act largely as independent encoders
Nirenberg, S., Carcieri, S., Jacobs, A. L., & Latham, P. E. (2001) · Nature · doi:10.1038/35079612 · Cited by 316 · see citing works
Self-citationCurrent Problems in Animal Behaviour
Curry‐Lindahl, K., Thorpe, W. H., & Zangwill, O. L. (1962) · Journal of Wildlife Management · doi:10.2307/3798617 · Cited by 316 · see citing works
Physiological Correlates of Perceptual Learning in Monkey V1 and V2
Ghose, G. M., Yang, T., & Maunsell, J. H. R. (2002) · Journal of Neurophysiology · doi:10.1152/jn.00690.2001 · Cited by 310 · see citing works
Effects of Attention on the Reliability of Individual Neurons in Monkey Visual Cortex
McAdams, C. J., & Maunsell, J. H. R. (1999) · Neuron · doi:10.1016/s0896-6273(01)80034-9 · Cited by 286 · see citing works
Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus
Dan, Y., Alonso, J., Usrey, W. M., & Reid, R. C. (1998) · Nature Neuroscience · doi:10.1038/2217 · Cited by 266 · see citing works
Hyvärinen, A., & Hoyer, P. O. (2001) · Vision Research · doi:10.1016/s0042-6989(01)00114-6 · Cited by 260 · see citing works
Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer
Nadal, J., & Parga, N. (1994) · Network Computation in Neural Systems · doi:10.1088/0954-898x_5_4_008 · Cited by 254 · see citing works
Population Coding of Stimulus Location in Rat Somatosensory Cortex
Petersen, R. S., Panzeri, S., & Diamond, M. E. (2001) · Neuron · doi:10.1016/s0896-6273(01)00481-0 · Cited by 249 · see citing works
Rolls, E. T., Treves, A., & Tovée, M. J. (1997) · Experimental Brain Research · doi:10.1007/pl00005615 · Cited by 247 · see citing works
Paradiso, M. A. (1988) · Biological Cybernetics · doi:10.1007/bf00363954 · Cited by 244 · see citing works
Seriès, P., Latham, P. E., & Pouget, A. (2004) · Nature Neuroscience · doi:10.1038/nn1321 · Cited by 240 · see citing works
Self-citationSynergy, Redundancy, and Independence in Population Codes, Revisited
Latham, P. E., & Nirenberg, S. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.5319-04.2005 · Cited by 227 · see citing works
Self-citationRedundancy in the Population Code of the Retina
Puchalla, J., Schneidman, E., Harris, R. A., & Berry, M. J. (2005) · Neuron · doi:10.1016/j.neuron.2005.03.026 · Cited by 226 · see citing works
Effects of Noise Correlations on Information Encoding and Decoding
Averbeck, B. B., & Lee, D. (2006) · Journal of Neurophysiology · doi:10.1152/jn.00919.2005 · Cited by 225 · see citing works
Self-citationThe `Ideal Homunculus': decoding neural population signals
Oram, M. W., Földiák, P., Perrett, D. I., Oram, M. W., & Sengpiel, F. (1998) · Trends in Neurosciences · doi:10.1016/s0166-2236(97)01216-2 · Cited by 221 · see citing works
Decoding neuronal spike trains: How important are correlations?
Nirenberg, S., & Latham, P. E. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1131895100 · Cited by 212 · see citing works
Self-citationCorrelated Neuronal Discharges that Increase Coding Efficiency during Perceptual Discrimination
Romo, R., Hernández, A., Zainos, A., & Salinas, E. (2003) · Neuron · doi:10.1016/s0896-6273(03)00287-3 · Cited by 205 · see citing works
Current problems in animal behaviour
Thorpe, W. H., & Zangwill, O. L. (1961) · Medical Entomology and Zoology · Cited by 192 · see citing works
Coding and transmission of information by neural ensembles
Averbeck, B. B., & Lee, D. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.02.006 · Cited by 190 · see citing works
Self-citationMultineuronal Firing Patterns in the Signal from Eye to Brain
Schnitzer, M. J., & Meister, M. (2003) · Neuron · doi:10.1016/s0896-6273(03)00004-7 · Cited by 177 · see citing works
Pola, G., Thiele, A., HOFFMANN, K. F., & Panzeri, S. (2003) · Network Computation in Neural Systems · doi:10.1088/0954-898x/14/1/303 · Cited by 174 · see citing works
Cognitive spatial-motor processes
Georgopoulos, A. P., & Massey, J. T. (1988) · Experimental Brain Research · doi:10.1007/bf00247577 · Cited by 157 · see citing works
Neural ensemble coding in inferior temporal cortex
Gochin, P. M., Colombo, M., Dorfman, G. A., Gerstein, G. L., & Gross, C. G. (1994) · Journal of Neurophysiology · doi:10.1152/jn.1994.71.6.2325 · Cited by 150 · see citing works
Shamir, M., & Sompolinsky, H. (2004) · Neural Computation · doi:10.1162/089976604773717559 · Cited by 140 · see citing works
Neural activity in prefrontal cortex during copying geometrical shapes
Averbeck, B. B., Crowe, D. A., Chafee, M. V., & Georgopoulos, A. P. (2003) · Experimental Brain Research · doi:10.1007/s00221-003-1417-5 · Cited by 131 · see citing works
Self-citationRedundancy and Synergy of Neuronal Ensembles in Motor Cortex
Narayanan, N. S., Kimchi, E. Y., & Laubach, M. (2005) · Journal of Neuroscience · doi:10.1523/jneurosci.4697-04.2005 · Cited by 131 · see citing works
Adjacent Visual Cortical Complex Cells Share About 20% of Their Stimulus-Related Information
Gawne, T. J., Kjær, T. W., Hertz, J., & Richmond, B. J. (1996) · Cerebral Cortex · doi:10.1093/cercor/6.3.482 · Cited by 117 · see citing works
Sensory discrimination: decision process
Johnson, K. (1980) · Journal of Neurophysiology · doi:10.1152/jn.1980.43.6.1771 · Cited by 116 · see citing works
Representational Accuracy of Stochastic Neural Populations
Wilke, S. D., & Eurich, C. W. (2002) · Neural Computation · doi:10.1162/089976602753284482 · Cited by 107 · see citing works
Neural Noise and Movement-Related Codes in the Macaque Supplementary Motor Area
Averbeck, B. B., & Lee, D. (2003) · Journal of Neuroscience · doi:10.1523/jneurosci.23-20-07630.2003 · Cited by 102 · see citing works
Self-citationOram, M. W., Hatsopoulos, N. G., Richmond, B. J., & Donoghue, J. P. (2001) · Journal of Neurophysiology · doi:10.1152/jn.2001.86.4.1700 · Cited by 102 · see citing works
Population Coding with Correlation and an Unfaithful Model
Wu, S., Nakahara, H., & Амари, Ш.-И. (2001) · Neural Computation · doi:10.1162/089976601300014349 · Cited by 86 · see citing works
Correlations, feature-binding and population coding in primary visual cortex
Golledge, H., Panzeri, S., Zheng, F., Pola, G., Scannell, J. W., Giannikopoulos, D. V., Mason, R. J., Tovée, M. J., & Young, M. P. (2003) · Neuroreport · doi:10.1097/01.wnr.0000073681.00308.9c · Cited by 58 · see citing works
Objective assessment of the functional role of spike train correlations using information measures
Panzeri, S., Golledge, H., Zheng, F., Tovée, M. J., & Young, M. P. (2001) · Visual Cognition · doi:10.1080/13506280143000106 · Cited by 21 · see citing works
Flagged first, then by citations
1 of the 78 references couldn’t be matched to a known paper.
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Top-down influences on visual processing
Gilbert, C. D., & Li, W. (2013) · Nature Reviews Neuroscience · doi:10.1038/nrn3476 · Cited by 1,224 · see citing works
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 1,158 · see citing works
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 1,126 · see citing works
Dynamic Shifts of Limited Working Memory Resources in Human Vision
Bays, P. M., & Husain, M. (2008) · Science · doi:10.1126/science.1158023 · Cited by 1,111 · see citing works
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 · see citing works
Measuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,016 · see citing works
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 · see citing works
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 799 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 591 · see citing works
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 581 · see citing works
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 580 · see citing works
How advances in neural recording affect data analysis
Stevenson, I. H., & Kording, K. P. (2011) · Nature Neuroscience · doi:10.1038/nn.2731 · Cited by 564 · see citing works
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 · see citing works
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 541 · see citing works
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 524 · see citing works
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 · see citing works
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 488 · see citing works
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 · see citing works
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 · see citing works
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 459 · see citing works
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 432 · see citing works
Hebart, M. N., Görgen, K., & Haynes, J. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00088 · Cited by 422 · see citing works
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 · see citing works
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 · see citing works
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 408 · see citing works
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 · see citing works
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 401 · see citing works
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 390 · see citing works
Nieder, A. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.40 · Cited by 389 · see citing works
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 · see citing works
Space and time in visual context
Schwartz, O., Hsu, A., & Dayan, P. (2007) · Nature reviews. Neuroscience · doi:10.1038/nrn2155 · Cited by 360 · see citing works
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 360 · see citing works
Self-citationBuzsáki, G. (2019) · doi:10.1093/oso/9780190905385.001.0001 · Cited by 358 · see citing works
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 343 · see citing works
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 333 · see citing works
Neuronal Mechanisms of Visual Attention
Maunsell, J. H. R. (2015) · Annual Review of Vision Science · doi:10.1146/annurev-vision-082114-035431 · Cited by 332 · see citing works
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 328 · see citing works
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 324 · see citing works
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 323 · see citing works
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 · see citing works
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 317 · see citing works
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 · see citing works
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 306 · see citing works
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 306 · see citing works
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 299 · see citing works
Mazzoni, A., Panzeri, S., Logothetis, N. K., & Brunel, N. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000239 · Cited by 298 · see citing works
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 · see citing works
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 286 · see citing works
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 · see citing works
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 · see citing works
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 271 · see citing works
On the importance of single directions for generalizationPreprint
Morcos, A. S., Barrett, D. G. T., Rabinowitz, N. C., & Botvinick, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.06959 · Cited by 269 · see citing works
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 266 · see citing works
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 · see citing works
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 252 · see citing works
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 247 · see citing works
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 244 · see citing works
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 239 · see citing works
Andersen, R. A., Hwang, E. J., & Mulliken, G. H. (2009) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.100503 · Cited by 238 · see citing works
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 236 · see citing works
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 235 · see citing works
Miura, K., Mainen, Z. F., & Uchida, N. (2012) · Neuron · doi:10.1016/j.neuron.2012.04.021 · Cited by 235 · see citing works
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 235 · see citing works
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 · see citing works
Ostojic, S., Brunel, N., & Hakim, V. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1275-09.2009 · Cited by 231 · see citing works
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 · see citing works
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 227 · see citing works
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 223 · see citing works
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 223 · see citing works
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 221 · see citing works
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 220 · see citing works
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 219 · see citing works
Tudusciuc, O., & Nieder, A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705495104 · Cited by 218 · see citing works
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 218 · see citing works
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 214 · see citing works
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 212 · see citing works
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 211 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 198 · see citing works
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 195 · see citing works
Making better decisions in groups
Bang, D., & Frith, C. (2017) · Royal Society Open Science · doi:10.1098/rsos.170193 · Cited by 194 · see citing works
Majaj, N. J., Hong, H., Solomon, E. A., & DiCarlo, J. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5181-14.2015 · Cited by 193 · see citing works
Pattern-information analysis: From stimulus decoding to computational-model testing
Kriegeskorte, N. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.01.061 · Cited by 191 · see citing works
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 190 · see citing works
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 · see citing works
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 188 · see citing works
Bowers, J. S. (2009) · Psychological Review · doi:10.1037/a0014462 · Cited by 188 · see citing works
- 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
