Correlated neuronal discharge rate and its implications for psychophysical performance
Abstract
Single neurons can signal subtle changes in the sensory environment with surprising fidelity, often matching the perceptual sensitivity of trained psychophysical observers. This similarity poses an intriguing puzzle: why is psychophysical sensitivity not greater than that of single neurons? Pooling responses across neurons should average out noise in the activity of single cells, leading to substantially improved psychophysical performance. If, however, noise is correlated among these neurons, the beneficial effects of pooling would be diminished. To assess correlation within a pool, the responses of pairs of neurons were recorded simultaneously during repeated stimulus presentations. We report here that the observed covariation in spike count was relatively weak, the correlation coefficient averaging 0.12. A theoretical analysis revealed, however, that weak correlation can limit substantially the signalling capacity of the pool. In addition, theory suggests a relationship between neuronal responses and psychophysical decisions which may prove useful for identifying cell populations underlying specific perceptual capacities.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The influence of a single neuron on the probability of a post-decision wager is expected to be small because it is one member of a large population of neurons that govern behavior.
“The influence of a single neuron on the probability of a post-decision wager is expected to be small because it is but one member of a large population of neurons that govern the behavior, presumably (33–35).”
From Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex (2009) · cites this paper as [34]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cited by
Sorted by citations
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764 · see citing works
Self-citationNeuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581 · see citing works
Neural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743 · 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
Probabilistic decision making by slow reverberation in cortical circuits
Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242 · see citing works
The Asynchronous State in Cortical Circuits
Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D. (2010) · Science · doi:10.1126/science.1179850 · Cited by 1,106 · see citing works
Psychology and neurobiology of simple decisions
Smith, P. L., & Ratcliff, R. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.01.006 · Cited by 1,051 · see citing works
Representation of Confidence Associated with a Decision by Neurons in the Parietal Cortex
Kiani, R., & Shadlen, M. N. (2009) · Science · doi:10.1126/science.1169405 · Cited by 1,027 · see citing works
Self-citationWaking state: rapid variations modulate neural and behavioral responses
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801 · see citing works
“The level of noise correlations measured experimentally varies widely (e.g. (Ecker et al., 2010; Zohary et al., 1994)) and their impact on the encoding accuracy of a neuronal population is an active field of theoretical research (e.g. (Abbott and Dayan, 1999; Ecker et al., 2011; Moreno-Bote et al.,…”
Temporal structure in spatially organized neuronal ensembles: a role for interneuronal networks
Buzsáki, G., & Chrobak, J. J. (1995) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(95)80012-3 · Cited by 878 · see citing works
Shadlen, M., Britten, K., Newsome, W., & Movshon, J. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-04-01486.1996 · Cited by 824 · see citing works
Self-citationSENSE AND THE SINGLE NEURON: Probing the Physiology of Perception
Parker, A. J., & Newsome, W. T. (1998) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.21.1.227 · Cited by 801 · see citing works
Self-citationSpatial 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
Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.2.1017 · Cited by 741 · see citing works
Statistically optimal perception and learning: from behavior to neural representations
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672 · see citing works
Probabilistic population codes for Bayesian decision making
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645 · see citing works
Self-citationDecorrelated neuronal firing in cortical microcircuits
Ecker, A. S., Berens, P., Keliris, G. A., Bethge, M., Logothetis, N. K., & Tolias, A. S. (2010) · Science · doi:10.1126/science.1179867 · Cited by 640 · see citing works
Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations
Luczak, A., Barthó, P., & Harris, K. D. (2009) · Neuron · doi:10.1016/j.neuron.2009.03.014 · Cited by 593 · 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
Representation of Time by Neurons in the Posterior Parietal Cortex of the Macaque
Leon, M. I., & Shadlen, M. N. (2003) · Neuron · doi:10.1016/s0896-6273(03)00185-5 · Cited by 601 · see citing works
Self-citationBuzsáki, G., & Watson, B. O. (2012) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2012.14.4/gbuzsaki · Cited by 579 · see citing works
Coincident but Distinct Messages of Midbrain Dopamine and Striatal Tonically Active Neurons
Morris, G., Arkadir, D., Nevet, A., Vaadia, E., & Bergman, H. (2004) · Neuron · doi:10.1016/j.neuron.2004.06.012 · Cited by 574 · see citing works
Huk, A. C., & Shadlen, M. N. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.4684-04.2005 · Cited by 538 · see citing works
Self-citationFlexible control of mutual inhibition: a neural model of two-interval discrimination
Machens, C. K., Romo, R., & Brody, C. D. (2005) · Science · doi:10.1126/science.1104171 · Cited by 535 · see citing works
Partitioning neuronal variability
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014) · Nature Neuroscience · doi:10.1038/nn.3711 · Cited by 528 · see citing works
Hofer, S. B., Ko, H., Pichler, B., Vogelstein, J., Ros, H., Zeng, H., Lein, E., Lesica, N. A., & Mrsic-Flogel, T. D. (2011) · Nature Neuroscience · doi:10.1038/nn.2876 · Cited by 470 · see citing works
Kiani, R., Hanks, T. D., & Shadlen, M. N. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4761-07.2008 · Cited by 500 · see citing works
Self-citationvon der Malsburg, C. (1999) · Neuron · doi:10.1016/s0896-6273(00)80825-9 · Cited by 501 · see citing works
Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice
Komiyama, T., Sato, T. R., O’Connor, D. H., Zhang, Y.-X., Huber, D., Hooks, B. M., Gabitto, M., & Svoboda, K. (2010) · Nature · doi:10.1038/nature08897 · Cited by 447 · see citing works
Kreiter, A., & Singer, W. (1996) · The Journal of Neuroscience · doi:10.1523/jneurosci.16-07-02381.1996 · Cited by 477 · see citing works
Impact of Correlated Synaptic Input on Output Firing Rate and Variability in Simple Neuronal Models
Salinas, E., & Sejnowski, T. J. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-16-06193.2000 · Cited by 472 · 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
Small modulation of ongoing cortical dynamics by sensory input during natural vision.
Fiser, J., Chiu, C., & Weliky, M. (2004) · Nature · doi:10.1038/nature02907 · Cited by 423 · see citing works
Probabilistic Interpretation of Population Codes
Zemel, R. S., Dayan, P., & Pouget, A. (1998) · Neural Computation · doi:10.1162/089976698300017818 · Cited by 333 · see citing works
Not Noisy, Just Wrong: The Role of Suboptimal Inference in Behavioral Variability
Beck, J. M., Ma, W. J., Pitkow, X., Latham, P. E., & Pouget, A. (2012) · Neuron · doi:10.1016/j.neuron.2012.03.016 · Cited by 316 · see citing works
Bayesian Spiking Neurons I: Inference
Deneve, S. (2008) · Neural Computation · doi:10.1162/neco.2008.20.1.91 · Cited by 313 · see citing works
Neural population code for fine perceptual decisions in area MT
Purushothaman, G., & Bradley, D. C. (2005) · Nature Neuroscience · doi:10.1038/nn1373 · Cited by 309 · see citing works
Internally Mediated Developmental Desynchronization of Neocortical Network Activity
Golshani, P., Gonçalves, J. T., Khoshkhoo, S., Mostany, R., Smirnakis, S., & Portera-Cailliau, C. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.2012-09.2009 · Cited by 298 · see citing works
Response variability of neurons in primary visual cortex (V1) of alert monkeys
Gur, M., Beylin, A., & Snodderly, D. M. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-08-02914.1997 · Cited by 277 · see citing works
On the dynamics of the spontaneous activity in neuronal networks
Mazzoni, A., Broccard, F. D., Garcia-Perez, E., Bonifazi, P., Ruaro, M. E., & Torre, V. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000439 · Cited by 279 · see citing works
Peters (2010) · doi:10.1016/j.neuron.2010.03.027 · Cited by 244 · see citing works
Stochastic models of decisions about motion direction: Behavior and physiology
Ditterich, J. (2006) · Neural Networks · doi:10.1016/j.neunet.2006.05.042 · Cited by 233 · see citing works
Ratcliff, R., Hasegawa, Y. T., Hasegawa, R. P., Smith, P. L., & Segraves, M. A. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00393.2006 · Cited by 236 · 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
Neural tuning and representational geometry
Kriegeskorte, N., & Wei, X.-X. (2021) · Nature Reviews Neuroscience · doi:10.1038/s41583-021-00502-3 · Cited by 187 · 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
Attention stabilizes the shared gain of V4 populations
Rabinowitz, N. C., Goris, R. L., Cohen, M., & Simoncelli, E. P. (2015) · eLife · doi:10.7554/elife.08998 · Cited by 194 · see citing works
Learning and attention reveal a general relationship between population activity and behavior
Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J., & Cohen, M. R. (2018) · Science · doi:10.1126/science.aao0284 · Cited by 188 · see citing works
Thalamic synchrony and the adaptive gating of information flow to cortex
Wang, Q., Webber, R. M., & Stanley, G. B. (2010) · Nature Neuroscience · doi:10.1038/nn.2670 · Cited by 166 · see citing works
Lesions of prefrontal cortex reduce attentional modulation of neuronal responses and synchrony in V4
Gregoriou, G. G., Rossi, A. F., Ungerleider, L. G., & Desimone, R. (2014) · Nature Neuroscience · doi:10.1038/nn.3742 · Cited by 177 · see citing works
Laminar dependence of neuronal correlations in visual cortex
Smith, M. A., Jia, X., Zandvakili, A., & Kohn, A. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00846.2012 · Cited by 129 · see citing works
Erickson, C. A., Jagadeesh, B., & Desimone, R. (2000) · Nature Neuroscience · doi:10.1038/80664 · Cited by 115 · see citing works
On the Structure of Neuronal Population Activity under Fluctuations in Attentional State
Ecker, A. S., Denfield, G. H., Bethge, M., & Tolias, A. S. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2044-15.2016 · Cited by 88 · see citing works
Cortical–hippocampal coupling during manifold exploration in motor cortex
Kim, J., Joshi, A., Frank, L., & Ganguly, K. (2023) · Nature · doi:10.1038/s41586-022-05533-z · Cited by 77 · see citing works
Joshi, S., & Gold, J. I. (2022) · eLife · doi:10.7554/elife.63490 · Cited by 71 · see citing works
Fast Remapping of Sensory Stimuli onto Motor Actions on the Basis of Contextual Modulation
Salinas, E. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.4569-03.2004 · Cited by 77 · see citing works
Single Neuron Firing Properties Impact Correlation-Based Population Coding
Hong, S., Ratté, S., Prescott, S. A., & De Schutter, E. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.3735-11.2012 · Cited by 72 · see citing works
Auditory cortex shapes sound responses in the inferior colliculus
Blackwell, J. M., Lesicko, A. M., Rao, W., De Biasi, M., & Geffen, M. N. (2020) · eLife · doi:10.7554/elife.51890 · Cited by 68 · see citing works
Cognition as a Window into Neuronal Population Space
Ruff, D. A., Ni, A. M., & Cohen, M. R. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061936 · Cited by 63 · see citing works
Elevated Correlations in Neuronal Ensembles of Mouse Auditory Cortex Following Parturition
Rothschild, G., Cohen, L., Mizrahi, A., & Nelken, I. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.4656-12.2013 · Cited by 46 · see citing works
Balanced Synaptic Input Shapes the Correlation between Neural Spike Trains
Litwin-Kumar, A., Oswald, A.-M. M., Urban, N. N., & Doiron, B. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002305 · Cited by 46 · see citing works
Kim, H. R., Pitkow, X., Angelaki, D. E., & DeAngelis, G. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00005.2016 · Cited by 32 · see citing works
- Authors
- Zohary, E., Shadlen, M. N., & Newsome, W. T.
- Affiliations
- Stanford Medicine (US)
- Venue
- Nature
- Published
- 1994-07
- Volume
- 370
- Issue
- 6485
- Pages
- 140-143
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2004478098
- Cited by
- 1,237
- References
- 0
- Citations by year
- 2026: 29 · 2025: 33 · 2024: 33 · 2023: 41 · 2022: 46 · 2021: 43 · 2020: 40 · 2019: 69 · 2018: 43 · 2017: 45 · 2016: 44 · 2015: 72 · 2014: 62 · 2013: 69 · 2012: 60
- FWCI
- 5.96
- Type
- journal article
- Language
- en
- Update notices
- correction (1994-09-22)
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- neuronal discharge rate, spike count correlations, sensory coding, neural noise, signal detection, perceptual decision making, single neuron responses, population coding
- MeSH terms
- Action Potentials, Animals, Discrimination, Psychological, Fixation, Ocular, Macaca mulatta, Mathematics, Neurons, Afferent, Psychomotor Performance, Temporal Lobe, Visual Pathways, Visual Perception
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