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The importance of mixed selectivity in complex cognitive tasks
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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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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Maass, W., Natschläger, T., & Markram, H. (2002) · Neural Computation · doi:10.1162/089976602760407955 · Cited by 4,025
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Jaeger, H., & Haas, H. (2004) · Science · doi:10.1126/science.1091277 · Cited by 3,811
Mullin, A., & Rosenblatt, F. F. (1963) · American Mathematical Monthly · doi:10.2307/2312103 · Cited by 2,211
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790
Cortex: Statistics and Geometry of Neuronal Connectivity
Braitenberg, V., & Schüz, A. (1998) · doi:10.1007/978-3-662-03733-1 · Cited by 1,178 · cited 1×
“Random connectivity in small brain regions has been suggested based on anatomical reconstructions [21] and recently observed in the connections from the olfactory bulb to the olfactory cortex [22] (see also [14] for a general discussion).”
BackgroundGenerating Coherent Patterns of Activity from Chaotic Neural Networks
Sussillo, D., & Abbott, L. F. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.018 · Cited by 1,092
State-dependent computations: spatiotemporal processing in cortical networks
Buonomano, D. V., & Maass, W. (2009) · Nature reviews. Neuroscience · doi:10.1038/nrn2558 · Cited by 950
An adaptive coding model of neural function in prefrontal cortex
Duncan, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35097575 · Cited by 874
Neural Activity in the Primate Prefrontal Cortex during Associative Learning
Asaad, W. F., Rainer, G., & Miller, E. K. (1998) · Neuron · doi:10.1016/s0896-6273(00)80658-3 · Cited by 593
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Camera, G. L., Bouret, S., & Richmond, B. J. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00165 · Cited by 9
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Strock, A., Hinaut, X., & Rougier, N. P. (2019) · Neural Computation · doi:10.1162/neco_a_01249 · Cited by 9
Cazin, N., Scleidorovich, P., Weitzenfeld, A., & Dominey, P. F. (2020) · Biological Cybernetics · doi:10.1007/s00422-020-00820-2 · Cited by 9
Miconi, T. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/057729 · Cited by 9
Capturing the diversity of biological tuning curves using generative adversarial networksPreprint
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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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