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

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

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

Open this paper in CoChat — free account

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

Crossref · 10.1038/nrn1888

Cites

77 of 77 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

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
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
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

Flagged first, then by citations

Show all 78 references

1 of the 78 references couldn’t be matched to a known paper.

Sorted by citations

Items 501–600 of 1,000

Prior Expectations in Visual Speed Perception Predict Encoding Characteristics of Neurons in Area MT
Zhang, L.-Q., & Stocker, A. A. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1920-21.2022 · Cited by 35
Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning
Averbeck, B. B. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2121331119 · Cited by 37
Self-citation
Dynamic Patterns of Spontaneous Ongoing Activity in the Visual Cortex of Anesthetized and Awake Monkeys are Different
Omer, D. B., Fekete, T., Ulchin, Y., Hildesheim, R., & Grinvald, A. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy099 · Cited by 36
Characterization of Visuomotor/Imaginary Movements in EEG: An Information Theory and Complex Network Approach
Baravalle, R., Guisande, N., Granado, M., Rosso, O. A., & Montani, F. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00115 · Cited by 34
Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making
Klaes, C., Schneegans, S., Schöner, G., & Gail, A. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002774 · Cited by 33
Strength of Correlations in Strongly Recurrent Neuronal Networks
Darshan, R., van Vreeswijk, C., & Hansel, D. (2018) · Physical Review X · doi:10.1103/physrevx.8.031072 · Cited by 33
Consequences of tuning network function by tonic and phasic locus coeruleus output and stress: Regulating detection and discrimination of peripheral stimuli
Devilbiss, D. M. (2018) · Brain Research · doi:10.1016/j.brainres.2018.06.015 · Cited by 33
Stimulus-choice (mis)alignment in primate area MT
Zhao, Y., Yates, J. L., Levi, A. J., Huk, A. C., & Park, I. M. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007614 · Cited by 34
Cognitive experience alters cortical involvement in goal-directed navigation
Arlt, C., Barroso-Luque, R., Kira, S., Bruno, C. A., Xia, N., Chettih, S. N., Soares, S., Pettit, N. L., & Harvey, C. D. (2022) · eLife · doi:10.7554/elife.76051 · Cited by 34
Error-based analysis of optimal tuning functions explains phenomena observed in sensory neurons
Yaeli, S. (2010) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2010.00130 · Cited by 33
Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish
Yang, Z., Li, M., Xia, F., Luo, Q., Fu, L., & Gong, H. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.001797 · Cited by 33
A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information
Delis, I., Berret, B., Pozzo, T., & Panzeri, S. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00054 · Cited by 33
Insights into decision making using choice probability
Crapse, T. B., & Basso, M. A. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00335.2015 · Cited by 34
Correlation of Synaptic Inputs in the Visual Cortex of Awake, Behaving Mice
Arroyo, S., Bennett, C., & Hestrin, S. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.008 · Cited by 33
The language of the brain: real-world neural population codes
Pruszynski, J. A., & Zylberberg, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.06.005 · Cited by 33
Sound-identity processing in early areas of the auditory ventral stream in the macaque
Kuśmierek, P., Ortiz-Rios, M., & Rauschecker, J. P. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00793.2011 · Cited by 32
Trial time warping to discriminate stimulus-related from movement-related neural activity
Pérez, O., Kass, R. E., & Merchant, H. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.10.019 · Cited by 32
Interplay between Graph Topology and Correlations of Third Order in Spiking Neuronal Networks
Jovanović, S., & Rotter, S. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004963 · Cited by 33
Error-Robust Modes of the Retinal Population Code
Prentice, J., Marre, O., Ioffe, M. L., Loback, A., Tkačik, G., & Berry, M. J. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005148 · Cited by 35
Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History
Jasper, A. I., Tanabe, S., & Kohn, A. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0035-19.2019 · Cited by 34
Ignoring correlated activity causes a failure of retinal population codes
Ruda, K., Zylberberg, J., & Field, G. D. (2020) · Nature Communications · doi:10.1038/s41467-020-18436-2 · Cited by 33
Multisensory task demands temporally extend the causal requirement for visual cortex in perception
Lohuis, M. N. O., Pie, J. L., Marchesi, P., Montijn, J. S., de Kock, C. P. J., Pennartz, C. M. A., & Olcese, U. (2022) · Nature Communications · doi:10.1038/s41467-022-30600-4 · Cited by 32
Functional interactions among neurons within single columns of macaque V1
Trepka, E. B., Zhu, S., Xia, R., Chen, X., & Moore, T. (2022) · eLife · doi:10.7554/elife.79322 · Cited by 35
Gamma oscillations as a mechanism for selective information transmission
Gielen, S. C. A. M., Крупа, М. М., & Zeitler, M. (2010) · Biological Cybernetics · doi:10.1007/s00422-010-0390-x · Cited by 33
Image Processing with Spiking Neuron Networks
Meftah, B., Lézoray, O., Chaturvedi, S. A., Khurshid, A. A., & Benyettou, A. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-29694-9_20 · Cited by 32
Modeling correlated noise is necessary to decode uncertainty
van Bergen, R. S., & Jehee, J. F. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.015 · Cited by 34
Optogenetically induced low-frequency correlations impair perception
Nandy, A. S., Nassi, J. J., Jadi, M. P., & Reynolds, J. H. (2019) · eLife · doi:10.7554/elife.35123 · Cited by 33
Energy-efficient population coding constrains network size of a neuronal array system
Yu, L., Zhang, C., Liu, L., & Yu, Y. (2016) · Scientific Reports · doi:10.1038/srep19369 · Cited by 32
On the use of information theory for the analysis of the relationship between neural and imaging signals
Panzeri, S., Magri, C., & Logothetis, N. K. (2008) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2008.02.019 · Cited by 32
Distinct feedforward and feedback pathways for cell-type specific attention effects
Spyropoulos, G., Schneider, M., van Kempen, J., Gieselmann, M. A., Thiele, A., & Vinck, M. A. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.020 · Cited by 33
Adult neurogenesis improves spatial information encoding in the mouse hippocampus
Frechou, M. A., Martin, S. S., McDermott, K. D., Huaman, E. A., Gökhan, Ş., Tomé, W. A., Coen-Cagli, R., & Gonçalves, J. T. (2024) · Nature Communications · doi:10.1038/s41467-024-50699-x · Cited by 32
Activation of Parallel Fiber Feedback by Spatially Diffuse Stimuli Reduces Signal and Noise Correlations via Independent Mechanisms in a Cerebellum-Like Structure
Simmonds, B., & Chacron, M. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004034 · Cited by 31
Feature selectivity is stable in primary visual cortex across a range of spatial frequencies
Jeon, B. B., Swain, A. D., Good, J. T., Chase, S. M., & Kuhlman, S. J. (2018) · Scientific Reports · doi:10.1038/s41598-018-33633-2 · Cited by 34
Applications of Phase Change Materials in Electrical Regime From Conventional Storage Memory to Novel Neuromorphic Computing
Liu, Z., & Wang, L. (2020) · IEEE Access · doi:10.1109/access.2020.2990536 · Cited by 31
Coherence resonance in neuronal populations: Mean-field versus network model
Baspinar, E., Schülen, L., Olmi, S., & Zakharova, A. (2021) · Physical review. E · doi:10.1103/physreve.103.032308 · Cited by 31
Cortical state dynamics and selective attention define the spatial pattern of correlated variability in neocortex
Shi, Y.-L., Steinmetz, N. A., Moore, T., Boahen, K. A., & Engel, T. A. (2022) · Nature Communications · doi:10.1038/s41467-021-27724-4 · Cited by 31
Coordinated multiplexing of information about separate objects in visual cortex
Jun, N. Y., Ruff, D. A., Kramer, L. E., Bowes, B., Tokdar, S. T., Cohen, M. R., & Groh, J. M. (2022) · eLife · doi:10.7554/elife.76452 · Cited by 31
Role of Input Correlations in Shaping the Variability and Noise Correlations of Evoked Activity in the Neocortex
Bujan, A. F., Aertsen, A., & Kumar, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.4536-14.2015 · Cited by 36
A normalization model suggests that attention changes the weighting of inputs between visual areas
Ruff, D. A., & Cohen, M. R. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1619857114 · Cited by 32
Contribution of spiking activity in the primary auditory cortex to detection in noise
Christison-Lagay, K. L., Bennur, S., & Cohen, Y. E. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00521.2017 · Cited by 31
Attention decouples action potentials from the phase of local field potentials in macaque visual cortical area MT
Esghaei, M., Daliri, M. R., & Treue, S. (2018) · BMC Biology · doi:10.1186/s12915-018-0551-2 · Cited by 31
CHIME: CMOS-Hosted in vivo Microelectrodes for Massively Scalable Neuronal Recordings
Köllő, M., Rácz, R., Hanna, M.-E., Obaid, A., Angle, M. R., Wray, W., Kong, Y., Hierlemann, A. R., Müller, J., Melosh, N. A., et al. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00834 · Cited by 31
Leveraging Nonhuman Primate Multisensory Neurons and Circuits in Assessing Consciousness Theory
Noel, J.-P., Ishizawa, Y., Patel, S. R., Eskandar, E. N., & Wallace, M. T. (2019) · The Journal of Neuroscience · doi:10.1523/jneurosci.0934-19.2019 · Cited by 30
Flexible neural population dynamics govern the speed and stability of sensory encoding in mouse visual cortex
Horrocks, E. A. B., Rodrigues, F. R., & Saleem, A. B. (2024) · Nature Communications · doi:10.1038/s41467-024-50563-y · Cited by 35
Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli
Schmeltzer, C., Kihara, A. H., Sokolov, I. M., & Rüdiger, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121794 · Cited by 30
Efficiency turns the table on neural encoding, decoding and noise
Denève, S., & Chalk, M. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.03.002 · Cited by 30
Activity Correlations between Direction-Selective Retinal Ganglion Cells Synergistically Enhance Motion Decoding from Complex Visual Scenes
Kühn, N. K., & Gollisch, T. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.003 · Cited by 30
Time resolution dependence of information measures for spiking neurons: scaling and universality
Marzen, S., DeWeese, M. R., & Crutchfield, J. P. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00105 · Cited by 30
A neural network for online spike classification that improves decoding accuracy
Issar, D., Williamson, R. C., Khanna, S. B., & Smith, M. A. (2020) · Journal of Neurophysiology · doi:10.1152/jn.00641.2019 · Cited by 30
Strong and weak principles of neural dimension reduction
Humphries, M. D. (2021) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.24619 · Cited by 37
Multidimensional population activity in an electrically coupled inhibitory circuit in the cerebellar cortex
Gurnani, H., & Silver, R. A. (2021) · Neuron · doi:10.1016/j.neuron.2021.03.027 · Cited by 32
Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons
Rikhye, R. V., Yıldırım, M., Hu, M., Breton‐Provencher, V., & Sur, M. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3176-20.2021 · Cited by 31
Priming of probabilistic attentional templates
Kristjansson, A. K. (2022) · Psychonomic Bulletin & Review · doi:10.3758/s13423-022-02125-w · Cited by 32
A neurophysiologically plausible population code model for human contrast discrimination
Goris, R. L. T., Wichmann, F. A., & Henning, G. B. (2009) · Journal of Vision · doi:10.1167/9.7.15 · Cited by 32
Functional triplet motifs underlie accurate predictions of single-trial responses in populations of tuned and untuned V1 neurons
Dechery, J. B., & MacLean, J. N. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006153 · Cited by 31
Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons
Zhang, W., Wu, S., Josić́, K., & Doiron, B. (2023) · Nature Communications · doi:10.1038/s41467-023-41743-3 · Cited by 31
Stochasticity from function — Why the Bayesian brain may need no noise
Dold, D., Bytschok, I., Kungl, Á. F., Baumbach, A., Breitwieser, O., Senn, W., Schemmel, J., Meier, K., & Petrovici, M. A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.08.002 · Cited by 34
Ipsilateral Stimulus Encoding in Primary and Secondary Somatosensory Cortex of Awake Mice
Pala, A., & Stanley, G. B. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.1417-21.2022 · Cited by 29
Low Intensity TMS Enhances Perception of Visual Stimuli
Abrahamyan, A., Clifford, C. W. G., Arabzadeh, E., & Harris, J. A. (2015) · Brain stimulation · doi:10.1016/j.brs.2015.06.012 · Cited by 29
Selective Changes in Noise Correlations Contribute to an Enhanced Representation of Saccadic Targets in Prefrontal Neuronal Ensembles
Dehaqani, M. A., Vahabie, A., Parsa, M., Noudoost, B., & Soltani, A. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy141 · Cited by 29
The Nature of Incomplete Neutralization in German: Implications for Laboratory Phonology
Winter, B. (2016) · Unipub UB Graz (Universität Graz) · Cited by 28
Visual experience orthogonalizes visual cortical stimulus responses via population code transformation
Failor, S., Carandini, M., & Harris, K. D. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.115235 · Cited by 33
Six principles of visual cortical dynamics
Roland, P. E. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00028 · Cited by 28
Perceptual Learning Reconfigures the Effects of Visual Adaptation
McGovern, D. P., Roach, N. W., & Webb, B. S. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.1363-12.2012 · Cited by 28
Coding “What” and “When” in the Archer Fish Retina
Vasserman, G., Shamir, M., Simon, A. B., & Segev, R. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000977 · Cited by 28
Overproduction of Neurons Is Correlated with Enhanced Cortical Ensembles and Increased Perceptual Discrimination
Fang, W., & Yuste, R. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.09.040 · Cited by 30
Dopamine Is Signaled by Mid-frequency Oscillations and Boosts Output Layers Visual Information in Visual Cortex
Zaldivar, D., Goense, J. B. M., Lowe, S., Logothetis, N. K., & Panzeri, S. (2018) · Current Biology · doi:10.1016/j.cub.2017.12.006 · Cited by 28
Developmental deprivation-induced perceptual and cortical processing deficits in awake-behaving animals
Yao, J. D., & Sanes, D. H. (2018) · eLife · doi:10.7554/elife.33891 · Cited by 28
Nonlinear receptive fields evoke redundant retinal coding of natural scenes
Karamanlis, D., Khani, M. H., Schreyer, H. M., Zapp, S. J., Mietsch, M., & Gollisch, T. (2024) · Nature · doi:10.1038/s41586-024-08212-3 · Cited by 27
Fast Coding of Orientation in Primary Visual Cortex
Shriki, O., Kohn, A., & Shamir, M. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002536 · Cited by 27
Spike generation estimated from stationary spike trains in a variety of neurons in vivo
Spanne, A., Geborek, P., Bengtsson, F., & Jörntell, H. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00199 · Cited by 28
Adaptation to second order stimulus features by electrosensory neurons causes ambiguity
Zhang, Z. D., & Chacron, M. J. (2016) · Scientific Reports · doi:10.1038/srep28716 · Cited by 27
Neural Synchrony Gives Rise to Amplitude- and Duration-Invariant Encoding Consistent With Perception of Natural Communication Stimuli
Metzen, M. G., Hofmann, V., & Chacron, M. J. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00079 · Cited by 27
Theta oscillations as a substrate for medial prefrontal-hippocampal assembly interactions
Nardin, M., Kaefer, K., Stella, F., & Csicsvari, J. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.113015 · Cited by 27
Age-dependent changes in prefrontal intrinsic connectivity
Zhou, X., Zhu, D., Katsuki, F., Qi, X.-L., Lees, C. J., Bennett, A. J., Salinas, E., Stanford, T. R., & Constantinidis, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1316594111 · Cited by 27
Anodal transcranial direct current stimulation over auditory cortex degrades frequency discrimination by affecting temporal, but not place, coding
Tang, M. F., & Hammond, G. R. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12280 · Cited by 27
Measure of Correlation Orthogonal to Change in Firing Rate
Амари, Ш.-И. (2008) · Neural Computation · doi:10.1162/neco.2008.03-08-729 · Cited by 27
Adaptive shaping of cortical response selectivity in the vibrissa pathway
Zheng, H. J. V., Wang, Q., & Stanley, G. B. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00978.2014 · Cited by 28
An evaluation of psychophysical models of auditory change perception.
Micheyl, C., Kaernbach, C., & Demany, L. (2008) · Psychological Review · doi:10.1037/a0013572 · Cited by 28
Structure of Spike Count Correlations Reveals Functional Interactions between Neurons in Dorsolateral Prefrontal Cortex Area 8a of Behaving Primates
Leavitt, M. L., Pieper, F., Sachs, A. J., Joober, R., & Martínez-Trujillo, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061503 · Cited by 32
Characterisation of sensitivity and orientation tuning for visually responsive ensembles in the zebrafish tectum
Thompson, A. W., & Scott, E. K. (2016) · Scientific Reports · doi:10.1038/srep34887 · Cited by 26
Sparse Coding in Temporal Association Cortex Improves Complex Sound Discriminability
Feigin, L., Tasaka, G., Maor, I., & Mizrahi, A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3167-20.2021 · Cited by 28
Information theory of adaptation in neurons, behavior, and mood
Sharpee, T. O., Calhoun, A. J., & Chalasani, S. H. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.11.007 · Cited by 26
Poisson or Not Poisson: Differences in Spike Train Statistics between Parietal Cortical Areas
Averbeck, B. B. (2009) · Neuron · doi:10.1016/j.neuron.2009.04.021 · Cited by 26
Self-citation
The Effects of Pooling on Spike Train Correlations
Rosenbaum, R., Trousdale, J., & Josić́, K. (2011) · Frontiers in Neuroscience · doi:10.3389/fnins.2011.00058 · Cited by 27
Dissociation between Neural Signatures of Stimulus and Choice in Population Activity of Human V1 during Perceptual Decision-Making
Choe, K. W., Blake, R., & Lee, S. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1606-13.2014 · Cited by 26
Neural Feedback for Instantaneous Spatiotemporal Modulation of Afferent Pathways in Bi-Directional Brain–Machine Interfaces
Liu, J., Khalil, H. K., & Oweiss, K. G. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2162003 · Cited by 26
Limitations of Neural Map Topography for Decoding Spatial Information
Avitan, L., Pujic, Z., Hughes, N. J., Scott, E. K., & Goodhill, G. J. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.0385-16.2016 · Cited by 26
Noise correlations in the human brain and their impact on pattern classification
Bejjanki, V. R., da Silveira, R. A., Cohen, J. D., & Turk‐Browne, N. B. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005674 · Cited by 27
Receptor crosstalk improves concentration sensing of multiple ligands
Carballo‐Pacheco, M., Desponds, J., Gavrilchenko, T., Mayer, A., Prizak, R., Reddy, G., Nemenman, I. M., & Mora, T. (2019) · Physical review. E · doi:10.1103/physreve.99.022423 · Cited by 26
Concurrent influence of top-down and bottom-up inputs on correlated activity of Macaque extrastriate neurons
Merrikhi, Y., Clark, K., & Noudoost, B. (2018) · Nature Communications · doi:10.1038/s41467-018-07816-4 · Cited by 26
Effect of Heterogeneity on Decorrelation Mechanisms in Spiking Neural Networks: A Neuromorphic-Hardware Study
Pfeil, T., Jordan, J., Tetzlaff, T., Grübl, A., Schemmel, J., Diesmann, M., & Meier, K. (2016) · Physical Review X · doi:10.1103/physrevx.6.021023 · Cited by 27
Revealing nonlinear neural decoding by analyzing choices
Yang, Q., Walker, E. Y., Cotton, R. J., Tolias, A. S., & Pitkow, X. (2021) · Nature Communications · doi:10.1038/s41467-021-26793-9 · Cited by 26
A quantitative model of ensemble perception as summed activation in feature space
Robinson, M. M., & Brady, T. F. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01602-z · Cited by 25
High Stimulus-Related Information in Barrel Cortex Inhibitory Interneurons
Reyes‐Puerta, V., Kim, S., Sun, J., Imbrosci, B., Kilb, W., & Luhmann, H. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004121 · Cited by 26
Triplet correlations among similarly tuned cells impact population coding
Cayco-Gajic, N. A., Zylberberg, J., & Shea‐Brown, E. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00057 · Cited by 28
Input nonlinearities can shape beyond-pairwise correlations and improve information transmission by neural populations
Zylberberg, J., & Shea‐Brown, E. (2015) · Physical Review E · doi:10.1103/physreve.92.062707 · Cited by 26
When and Why Noise Correlations Are Important in Neural Decoding
Eyherabide, H. G., & Samengo, I. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.0357-13.2013 · Cited by 25
Selective Tuning for Contrast in Macaque Area V4
Sani, I., Santandrea, E., Golzar, A., Morrone, M. C., & Chelazzi, L. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.3465-13.2013 · Cited by 25
Mechanisms of Visual Perceptual Learning in Macaque Visual Cortex
Vogels, R. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2009.01051.x · Cited by 25

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

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

Neural correlations, population coding and computation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks