The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citation
Cited by
Sorted by citations
Items 1,001–1,006 of 1,006
Flexible neural connectivity under constraints on total connection strength
Ocker, G. K., & Buice, M. A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008080 · Cited by 5
Dehaqani, M. A., Emadi, N., Vahabie, A., Zandvakili, A., & Esteky, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-12236-y · Cited by 5
Analog Cognition and Consciousness
Miller, E. K., Brincat, S. L., & Roy, J. E. (2026) · The Journal of Neuroscience · doi:10.1523/jneurosci.0711-26.2026 · Cited by 4
“…cortical (and subcortical) neurons are not organized around single functions and instead multiplex di ff erent kinds of information (Asaad et al., 2000; Warden and Miller, 2007; Cromer et al., 2010; Rigotti et al., 2013; Akam and Kullmann, 2014; Ramakrishnan et al., 2017; Lankarany et al., 2019).”
InfluentialSelf-citationNeuroscience undermines the higher-order theorists' preferred solution to the targetless HOT problem
Kozuch, B. (2026) · Philosophical Psychology · doi:10.1080/09515089.2026.2629514
2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
Circuit mechanisms for disentangled representations and generalization in the hippocampus
Tang, W., Chang, H., Liu, C., Perez-Hernandez, S., Zheng, W. Y., Park, J., Karaba, L. A., Oliva, A., & Fernandez-Ruiz, A. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02491-x
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
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