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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
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Self-citation
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
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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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