The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Learning and attention reveal a general relationship between population activity and behavior
Open accessAbstract
The neuronal population is the key unit: The responses of pairs of neurons to repeated presentations of the same stimulus are typically correlated, and an identical neuronal population can perform many functions. This suggests that the relevant units of computation are not single neurons but subspaces of the complete population activity. To test this idea, Ni et al. measured the relationship between neuronal population activity and performance in monkeys. They investigated attention, which improves perception of attended stimuli, and perceptual learning, which improves perception of well-practiced stimuli. These two processes operate on different time scales and are usually studied using different perceptual tasks. Manipulation of attention and learning in the same behavioral trials and the same neuronal populations revealed the dimensions of population activity that matter most for behavior. Science , this issue p. 463
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“Gain regulation has been suggested to enable networks of neurons to produce distributed representations of stimulus features223, and to allow downstream decoders to be optimized for diverse stimuli by separating those features224.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [224]
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Cites
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
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
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
Self-citationMeasuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,059
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
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
Partitioning neuronal variability
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014) · Nature Neuroscience · doi:10.1038/nn.3711 · Cited by 528
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
Decision-related activity in sensory neurons reflects more than a neuron’s causal effect
Nienborg, H., & Cumming, B. G. (2009) · Nature · doi:10.1038/nature07821 · Cited by 365
Perceptual Learning: Toward a Comprehensive Theory
Watanabe, T., & Sasaki, Y. (2014) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015214 · Cited by 358
Cited by
Sorted by citations
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
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
Mechanisms underlying gain modulation in the cortex
Ferguson, K. A., & Cardin, J. A. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0253-y · Cited by 327
Behavior needs neural variability
Waschke, L., Kloosterman, N. A., Obleser, J., & Garrett, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.01.023 · Cited by 317
Neuromodulation of brain state and behavior
McCormick, D. A., Nestvogel, D. B., & He, B. J. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100219-105424 · Cited by 303
Metadata
- Authors
- Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J., & Cohen, M. R.
- Affiliations
- University of Pittsburgh (US)
- Corresponding author
- Cohen, M. R.
- Venue
- Science
- Published
- 2018-01-26
- Volume
- 359
- Issue
- 6374
- Pages
- 463-465
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC6571104
- OpenAlex
- W2785016696
- Cited by
- 188
- References
- 22
- Citations by year
- 2026: 21 · 2025: 12 · 2024: 13 · 2023: 23 · 2022: 21 · 2021: 39 · 2020: 23 · 2019: 22 · 2018: 11
- FWCI
- 14.03
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (4R00EY020844-03, R01 EY022930, Core Grant P30 EY008098s)
- McKnight Endowment Fund for Neuroscience
- Alfred P. Sloan Foundation
- Simons Foundation
- Esther A. and Joseph Klingenstein Fund
- Whitehall Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Keywords
- correlated variability, population activity, perceptual learning, attention, behavioral performance, decision-making, trial-to-trial variability, perceptual performance
- MeSH terms
- Animals, Haplorhini, Attention, Behavior, Evoked Potentials, Learning, Male, Neurons, Perception
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