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The importance of mixed selectivity in complex cognitive tasks
Open accessAbstract
Single-neuron activity in the prefrontal cortex (PFC) is tuned to mixtures of multiple task-related aspects. Such mixed selectivity is highly heterogeneous, seemingly disordered and therefore difficult to interpret. We analysed the neural activity recorded in monkeys during an object sequence memory task to identify a role of mixed selectivity in subserving the cognitive functions ascribed to the PFC. We show that mixed selectivity neurons encode distributed information about all task-relevant aspects. Each aspect can be decoded from the population of neurons even when single-cell selectivity to that aspect is eliminated. Moreover, mixed selectivity offers a significant computational advantage over specialized responses in terms of the repertoire of input-output functions implementable by readout neurons. This advantage originates from the highly diverse nonlinear selectivity to mixtures of task-relevant variables, a signature of high-dimensional neural representations. Crucially, this dimensionality is predictive of animal behaviour as it collapses in error trials. Our findings recommend a shift of focus for future studies from neurons that have easily interpretable response tuning to the widely observed, but rarely analysed, mixed selectivity neurons.
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“Additional efficiency is achieved because conceptually similar representations reuse neural populations during simulation (e.g. Rigotti et al., 2013).”
From The theory of constructed emotion: an active inference account of interoception and categorization (2016) · cites this paper as Rigotti et al., 2013
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Recent advances in physical reservoir computing: A review
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The theory of constructed emotion: an active inference account of interoception and categorization
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Dimensionality reduction for large-scale neural recordings
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Metadata
- Authors
- Rigotti, M., Barak, O., Warden, M. R., Wang, X.-J., Daw, N. D., Miller, E. K., & Fusi, S.
- Affiliations
- Columbia University Irving Medical Center (US)
- Massachusetts Institute of Technology (US)
- Yale University (US)
- New York University (US)
- Corresponding author
- Fusi, Stefano
- ORCID
- Venue
- Nature
- Published
- 2013-05
- Volume
- 497
- Issue
- 7451
- Pages
- 585-590
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4412347
- OpenAlex
- W2033708181
- Cited by
- 1,639
- References
- 24
- Citations by year
- 2026: 146 · 2025: 171 · 2024: 220 · 2023: 149 · 2022: 139 · 2021: 143 · 2020: 110 · 2019: 104 · 2018: 106 · 2017: 102 · 2016: 101 · 2015: 66 · 2014: 65 · 2013: 14
- FWCI
- 44.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- PMC
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (133357)
- National Institute of Mental Health (R01MH091174, R37MH087027, 5-R37-MH087027-04)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- mixed selectivity, prefrontal cortex, single-unit activity, population coding, error trials
- MeSH terms
- Animals, Haplorhini, Behavior, Animal, Cognition, Memory, Models, Neurological, Neurons, Prefrontal Cortex, Single-Cell Analysis
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