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 501–600 of 1,006
Cognition is an emergent property
Miller, E. K., Brincat, S. L., & Roy, J. E. (2024) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2024.101388 · Cited by 37
Self-citationCapone, C., Pastorelli, E., Golosio, B., & Paolucci, P. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-45525-0 · Cited by 42
Exploring Cognition with Brain–Machine Interfaces
Andersen, R. A., Aflalo, T., Bashford, L., Bjånes, D. A., & Kellis, S. (2022) · Annual Review of Psychology · doi:10.1146/annurev-psych-030221-030214 · Cited by 39
A Quadrantic Bias in Prefrontal Representation of Visual-Mnemonic Space
Leavitt, M. L., Pieper, F., Sachs, A. J., & Martínez-Trujillo, J. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx142 · Cited by 38
Population activity structure of excitatory and inhibitory neurons
Bittner, S. R., Williamson, R. C., Snyder, A. C., Litwin-Kumar, A., Doiron, B., Chase, S. M., Smith, M. A., & Yu, B. M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181773 · Cited by 37
Flexible weighting of diverse inputs makes hippocampal function malleable
Aly, M., & Turk-Browne, N. B. (2018) · Neuroscience Letters · doi:10.1016/j.neulet.2017.05.063 · Cited by 36
Representation of Spatial and Feature Information in the Monkey Dorsal and Ventral Prefrontal Cortex
Constantinidis, C., & Qi, X.-L. (2018) · Frontiers in Integrative Neuroscience · doi:10.3389/fnint.2018.00031 · Cited by 38
Zhang, Y., Denman, A. J., Liang, B., Werner, C. T., Beacher, N. J., Chen, R., Li, Y., Shaham, Y., Barbera, G., & Lin, D. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.022 · Cited by 37
Spatial and temporal distribution of visual information coding in lateral prefrontal cortex
Kadohisa, M., Kusunoki, M., Petrov, P., Sigala, N., Buckley, M. J., Gaffan, D., & Duncan, J. (2014) · European Journal of Neuroscience · doi:10.1111/ejn.12754 · Cited by 36
Hara, Y., Pestilli, F., & Gardner, J. L. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00012 · Cited by 36
A prefrontal-thalamic circuit encodes social information for social recognition
Chen, Z., Han, Y., Ma, Z., Wang, X., Xu, S., Tang, Y. Y., Vyssotski, A. L., Si, B., & Zhan, Y. (2024) · Nature Communications · doi:10.1038/s41467-024-45376-y · Cited by 35
Self-citationThalamocortical architectures for flexible cognition and efficient learning
Scott, D. N., Mukherjee, A., Nassar, M. R., & Halassa, M. M. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.05.006 · Cited by 37
Dominey, P. F. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00500 · Cited by 35
State Dependency of Chemosensory Coding in the Gustatory Thalamus (VPMpc) of Alert Rats
Liu, H., & Fontanini, A. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0839-15.2015 · Cited by 36
Melancholy, anhedonia, apathy: the search for separable behaviors and neural circuits in depression
Post, R. J., & Warden, M. R. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.02.018 · Cited by 39
Self-citationNeuronal spike-rate adaptation supports working memory in language processing
Fitz, H., Uhlmann, M., den Broek, D. V., Duarte, R., Peter Hagoort, A., & Petersson, K. M. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2000222117 · Cited by 35
Valence processing in the PFC: Reconciling circuit-level and systems-level views
Coley, A. A., Padilla-Coreano, N., Patel, R. R., & Tye, K. M. (2021) · International review of neurobiology · doi:10.1016/bs.irn.2020.12.002 · Cited by 39
Donoghue, T., Cao, R., Han, C. Z., Holman, C. M., Brandmeir, N. J., Wang, S., & Jacobs, J. (2023) · Hippocampus · doi:10.1002/hipo.23539 · Cited by 35
Blackman, R. K., Crowe, D. A., DeNicola, A. L., Sakellaridi, S., MacDonald, A. W., & Chafee, M. V. (2016) · Journal of Neuroscience · doi:10.1523/jneurosci.3578-15.2016 · Cited by 35
A Low Power Trainable Neuromorphic Integrated Circuit That Is Tolerant to Device Mismatch
Thakur, C. S., Wang, R., Hamilton, T. J., Tapson, J. C., & André, V. S. (2016) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2015.2512743 · Cited by 35
Rarely categorical, highly separable representations along the cortical hierarchy
Posani, L., Wang, S., Muscinelli, S. P., Paninski, L., & Fusi, S. (2026) · Nature · doi:10.1038/s41586-026-10668-4 · Cited by 48
Self-citationLearning to control the brain through adaptive closed-loop patterned stimulation
Tafazoli, S., MacDowell, C. J., Che, Z., Letai, K. C., Steinhardt, C. R., & Buschman, T. J. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abb860 · Cited by 36
Bae, J. W., Jeong, H., Yoon, Y. J., Bae, C., Lee, H., Paik, S., & Jung, M. W. (2021) · Nature Communications · doi:10.1038/s41467-021-24565-z · Cited by 34
Distributed functions of prefrontal and parietal cortices during sequential categorical decisions
Zhou, Y., Rosen, M. C., Swaminathan, S. K., Masse, N. Y., Zhu, O., & Freedman, D. J. (2021) · eLife · doi:10.7554/elife.58782 · Cited by 34
Metacognitive resources for adaptive learning⋆
Cortese, A. (2021) · Neuroscience Research · doi:10.1016/j.neures.2021.09.003 · Cited by 39
Cognitive strategies shift information from single neurons to populations in prefrontal cortex
Chiang, F.-K., Wallis, J. D., & Rich, E. L. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.021 · Cited by 34
Domanski, A. P. F., Kucewicz, M. T., Russo, E., Tricklebank, M. D., Robinson, E. S. J., Durstewitz, D., & Jones, M. W. (2023) · Current Biology · doi:10.1016/j.cub.2023.02.029 · Cited by 34
Cognitive experience alters cortical involvement in goal-directed navigation
Arlt, C., Barroso-Luque, R., Kira, S., Bruno, C. A., Xia, N., Chettih, S. N., Soares, S., Pettit, N. L., & Harvey, C. D. (2022) · eLife · doi:10.7554/elife.76051 · Cited by 34
Visuo-frontal interactions during social learning in freely moving macaques
Franch, M. C., Yellapantula, S., Parajuli, A., Kharas, N., Wright, A. A., Aazhang, B., & Dragoi, V. (2024) · Nature · doi:10.1038/s41586-024-07084-x · Cited by 33
Temporal multiplexing of perception and memory codes in IT cortex
She, L., Benna, M. K., Shi, Y., Fusi, S., & Tsao, D. Y. (2024) · Nature · doi:10.1038/s41586-024-07349-5 · Cited by 33
Self-citationKikumoto, A., Bhandari, A., Shibata, K., & Badre, D. (2024) · Nature Communications · doi:10.1038/s41467-024-52777-6 · Cited by 33
Moneta, N., Grossman, S., & Schuck, N. W. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.10.005 · Cited by 37
Direct brain recordings reveal prefrontal cortex dynamics of memory development
Johnson, E. L., Tang, L., Yin, Q., Asano, E., & Ofen, N. (2018) · Science Advances · doi:10.1126/sciadv.aat3702 · Cited by 33
Temporal signals underlying a cognitive process in the dorsal premotor cortex
Rossi-Pool, R., Zizumbo, J., Álvarez, M., Vergara, J., Zainos, A., & Romo, R. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820474116 · Cited by 33
Mixed Selectivity in Macaque Medial Parietal Cortex during Eye-Hand Reaching
Diomedi, S., Vaccari, F., Filippini, M., Fattori, P., & Galletti, C. (2020) · iScience · doi:10.1016/j.isci.2020.101616 · Cited by 34
Disorder and the Neural Representation of Complex Odors
Krishnamurthy, K., Hermundstad, A. M., Mora, T., Walczak, A. M., & Balasubramanian, V. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.917786 · Cited by 33
Recurrent networks endowed with structural priors explain suboptimal animal behavior
Molano‐Mazón, M., Shao, Y., Duque, D., Yang, G. R., Ostojic, S., & de la Rocha, J. (2023) · Current Biology · doi:10.1016/j.cub.2022.12.044 · Cited by 39
Subspace partitioning in the human prefrontal cortex resolves cognitive interference
Weber, J., Iwama, G. Y., Solbakk, A., Blenkmann, A. O., Larsson, P. G., Ivanović, J., Knight, R. T., Endestad, T., & Helfrich, R. F. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220523120 · Cited by 33
Isaac, J., Karkare, S. C., Balasubramanian, H., Schappaugh, N., Javier, J. L., Rashid, M., & Murugan, M. (2024) · Nature Communications · doi:10.1038/s41467-024-52294-6 · Cited by 35
Error-Robust Modes of the Retinal Population Code
Prentice, J., Marre, O., Ioffe, M. L., Loback, A., Tkačik, G., & Berry, M. J. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005148 · Cited by 35
Jackson, J. B., & Woolgar, A. (2018) · Cortex · doi:10.1016/j.cortex.2018.07.006 · Cited by 35
Multidimensional Neural Selectivity in the Primate Amygdala
Putnam, P. T., & Gothard, K. M. (2019) · eNeuro · doi:10.1523/eneuro.0153-19.2019 · Cited by 37
Achieving stable dynamics in neural circuits
Kozachkov, L., Lundqvist, M., Slotine, J.-J. E., & Miller, E. K. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007659 · Cited by 34
Self-citationThe hierarchical construction of value
O’Doherty, J. P., Rutishauser, U., & Iigaya, K. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.03.027 · Cited by 35
Lingering Neural Representations of Past Task Features Adversely Affect Future Behavior
Rangel, B. O., Hazeltine, E., & Wessel, J. R. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0464-22.2022 · Cited by 33
Neural mechanisms underlying the hierarchical construction of perceived aesthetic value
Iigaya, K., Yi, S., Wahle, I. A., Tanwisuth, S., Cross, L., & O’Doherty, J. P. (2023) · Nature Communications · doi:10.1038/s41467-022-35654-y · Cited by 40
Task-phase-specific dynamics of basal forebrain neuronal ensembles
Tingley, D., Alexander, A. S., Kolbu, S., de Sa, V. R., Chiba, A. A., & Nitz, D. A. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00174 · Cited by 32
Robust Encoding of Spatial Information in Orbitofrontal Cortex and Striatum
Yoo, S. B. M., Sleezer, B. J., & Hayden, B. Y. (2018) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01259 · Cited by 33
Working Memory: From Neural Activity to the Sentient Mind
Jaffe, R. J., & Constantinidis, C. (2021) · Comprehensive physiology · doi:10.1002/cphy.c210005 · Cited by 33
Dubey, A., Markowitz, D. A., & Pesaran, B. (2023) · Neuron · doi:10.1016/j.neuron.2023.06.025 · Cited by 33
Beyond hippocampus: Thalamic and prefrontal contributions to an evolving memory
Yadav, N., Toader, A. C., & Rajasethupathy, P. (2024) · Neuron · doi:10.1016/j.neuron.2023.12.021 · Cited by 31
Proca, A. M., Rosas, F. E., Luppi, A. I., Bor, D., Crosby, M., & Mediano, P. A. M. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012178 · Cited by 41
Aperiodic and oscillatory systems underpinning human domain-general cognition
Lu, R., Dermody, N., Duncan, J., & Woolgar, A. (2024) · Communications Biology · doi:10.1038/s42003-024-07397-7 · Cited by 32
Information processing in the CNS: a supramolecular chemistry?
Tozzi, A. (2015) · Cognitive Neurodynamics · doi:10.1007/s11571-015-9337-1 · Cited by 32
Context value updating and multidimensional neuronal encoding in the retrosplenial cortex
Sun, W., Choi, I., Stoyanov, S., Senkov, O., Ponimaskin, E. G., Winter, Y., Pakan, J. M. P., & Dityatev, A. E. (2021) · Nature Communications · doi:10.1038/s41467-021-26301-z · Cited by 31
Encoding of social novelty by sparse GABAergic neural ensembles in the prelimbic cortex
Zhao, Z., Zeng, F., Wang, H., Wu, R., Chen, L., Wu, Y., Li, S., Shao, J., Wang, Y., Wu, J., et al. (2022) · Science Advances · doi:10.1126/sciadv.abo4884 · Cited by 31
Understanding Central Processes
Hazeltine, E., & Schumacher, E. H. (2015) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/bs.plm.2015.09.006 · Cited by 34
Survey of Stochastic-Based Computation Paradigms
Alawad, M., & Lin, M. (2016) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2016.2598726 · Cited by 31
van den Brink, R. L., Hagena, K., Wilming, N., Murphy, P. R., Büchel, C., & Donner, T. H. (2022) · Neuron · doi:10.1016/j.neuron.2022.11.009 · Cited by 29
Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics
Soldado-Magraner, J., Mante, V., & Sahani, M. (2024) · Science Advances · doi:10.1126/sciadv.adl4743 · Cited by 33
Population coding of strategic variables during foraging in freely moving macaques
Shahidi, N., Franch, M. C., Parajuli, A., Schrater, P., Wright, A. A., Pitkow, X., & Dragoi, V. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01575-w · Cited by 30
Insanally, M. N., Albanna, B. F., Toth, J., DePasquale, B., Fadaei, S. S., Gupta, T., Lombardi, O., Kuchibhotla, K. V., Rajan, K., & Froemke, R. C. (2024) · Nature Communications · doi:10.1038/s41467-024-49895-6 · Cited by 30
Construction and use of mental models: Organizing principles for the science of brain and mind
Duncan, J. (2024) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2024.109062 · Cited by 35
How thoughts arise from sights: inferotemporal and prefrontal contributions to vision
Kornblith, S., & Tsao, D. Y. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.08.016 · Cited by 31
A Computational Perspective of the Role of the Thalamus in Cognition
Dehghani, N., & Wimmer, R. D. (2019) · Neural Computation · doi:10.1162/neco_a_01197 · Cited by 33
Strong and weak principles of neural dimension reduction
Humphries, M. D. (2021) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.24619 · Cited by 37
Interactions between rodent visual and spatial systems during navigation
Saleem, A. B., & Busse, L. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00716-7 · Cited by 33
Bellet, M. E., Gay, M., Bellet, J., Jarraya, B., Dehaene, S., van Kerkoerle, T., & Panagiotaropoulos, T. I. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.113952 · Cited by 33
Zhang, Z., Cheng, Z., Lin, Z., Nie, C., & Yang, T. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005925 · Cited by 32
Estimating the functional dimensionality of neural representations
Ahlheim, C., & Love, B. C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.015 · Cited by 30
Konecky, R. O., Smith, M. A., & Olson, C. R. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00541.2016 · Cited by 29
Optimal routing to cerebellum-like structures
Muscinelli, S. P., Wagner, M. J., & Litwin-Kumar, A. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01403-7 · Cited by 30
Selective attention and decision-making have separable neural bases in space and time
Moerel, D., Rich, A. N., & Woolgar, A. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.0224-24.2024 · Cited by 32
Semi-orthogonal subspaces for value mediate a binding and generalization trade-off
Johnston, W. J., Fine, J. M., Yoo, S. B. M., Ebitz, R. B., & Hayden, B. Y. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01758-5 · Cited by 33
Optimal dynamic coding by mixed-dimensionality neurons in the head-direction system of bats
Finkelstein, A., Ulanovsky, N., Tsodyks, M. V., & Aljadeff, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05562-1 · Cited by 30
Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion
Yoo, S. B. M., Tu, J. C., & Hayden, B. Y. (2021) · Nature Communications · doi:10.1038/s41467-021-22195-z · Cited by 32
Toward computational neuroconstructivism: a framework for developmental systems neuroscience
Astle, D. E., Johnson, M. H., & Akarca, D. (2023) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2023.04.009 · Cited by 34
Widespread coding of navigational variables in prefrontal cortex
Maisson, D. J.-N., Cervera, R. L., Voloh, B., Conover, I., Zambre, M., Zimmermann, J., & Hayden, B. Y. (2023) · Current Biology · doi:10.1016/j.cub.2023.07.024 · Cited by 33
Boucher, P. O., Wang, T., Carceroni, L., Kane, G. A., Shenoy, K. V., & Chandrasekaran, C. (2023) · Nature Communications · doi:10.1038/s41467-023-41752-2 · Cited by 30
Widge, A. S., & Moritz, C. T. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/024001 · Cited by 29
Low-Beta Oscillations Turn Up the Gain During Category Judgments
Stanley, D. A., Roy, J. E., Aoi, M., Kopell, N., & Miller, E. K. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw356 · Cited by 29
Self-citationDorsolateral prefrontal neurons mediate subjective decisions and their variation in humans
Jamali, M., Grannan, B. L., Haroush, K., Moses, Z. B., Eskandar, E. N., Herrington, T. M., Patel, S. R., & Williams, Z. M. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0378-3 · Cited by 33
Posani, L., Cocco, S., Ježek, K., & Monasson, R. (2017) · Journal of Computational Neuroscience · doi:10.1007/s10827-017-0645-9 · Cited by 30
Neural representation of newly instructed rule identities during early implementation trials
Ruge, H., Schäfer, T. A. J., Zwosta, K., Mohr, H., & Wolfensteller, U. (2019) · eLife · doi:10.7554/elife.48293 · Cited by 30
Flexing the principal gradient of the cerebral cortex to suit changing semantic task demands
Gao, Z., Zheng, L. J., Krieger‐Redwood, K., Halai, A. D., Margulies, D. S., Smallwood, J., & Jefferies, E. (2022) · eLife · doi:10.7554/elife.80368 · Cited by 28
A hippocampus-accumbens code guides goal-directed appetitive behavior
Barnstedt, O., Mocellin, P., & Remy, S. (2024) · Nature Communications · doi:10.1038/s41467-024-47361-x · Cited by 31
Grid Cells in Cognition: Mechanisms and Function
Dong, L. L., & Fiete, I. (2024) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-101323-112047 · Cited by 33
Timescales of learning in prefrontal cortex
Miller, J. A., & Constantinidis, C. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-024-00836-8 · Cited by 30
Time, space, memory and brain–body rhythms
Buzsáki, G. (2025) · Nature reviews. Neuroscience · doi:10.1038/s41583-025-00987-2 · Cited by 32
Dehaqani, M. A., Vahabie, A., Parsa, M., Noudoost, B., & Soltani, A. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy141 · Cited by 29
Representational Organization of Novel Task Sets during Proactive Encoding
Palenciano, A. F., González‐García, C., Arco, J. E., Pessoa, L., & Ruz, M. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0725-19.2019 · Cited by 35
Loriette, C., Amengual, J. L., & Hamed, S. B. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.811736 · Cited by 32
Frontal and temporal coding dynamics in successive steps of complex behavior
Kadohisa, M., Kusunoki, M., Mitchell, D. J., Bhatia, C., Buckley, M. J., & Duncan, J. (2022) · Neuron · doi:10.1016/j.neuron.2022.11.004 · Cited by 28
Learning shapes neural geometry in the primate prefrontal cortex
Wójcik, M. J., Stroud, J. P., Wasmuht, D., Kusunoki, M., Kadohisa, M., Buckley, M. J., Costa, R. P., Myers, N. E., Hunt, L. T., Duncan, J., et al. (2026) · Nature Neuroscience · doi:10.1038/s41593-026-02333-w · Cited by 30
Koyluoglu, O. O., Pertzov, Y., Manohar, S., Husain, M., & Fiete, I. (2017) · eLife · doi:10.7554/elife.22225 · Cited by 28
Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity
Gilmer, J. I., & Person, A. L. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.0588-17.2017 · Cited by 28
Value-Related Neuronal Responses in the Human Amygdala during Observational Learning
Aquino, T. G., Minxha, J., Dunne, S., Ross, I. B., Mamelak, A. N., Rutishauser, U., & O’Doherty, J. P. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2897-19.2020 · Cited by 28
Abstract deliberation by visuomotor neurons in prefrontal cortex
Charlton, J. A., & Goris, R. L. T. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01635-1 · Cited by 28
Primate Thalamic Nuclei Select Abstract Rules and Shape Prefrontal Dynamics
Phillips, J. M., Afrasiabi, M., Kambi, N. A., Redinbaugh, M. J., Steely, S., Johnson, E. R., Cheng, X., Fayyad, M., Mohanta, S., Carís, A., et al. (2024) · Neuron · doi:10.1016/j.neuron.2025.03.021 · Cited by 28
Approaches to inferring multi-regional interactions from simultaneous population recordings
Kang, B., & Druckmann, S. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.10.004 · Cited by 27
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