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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.”
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Cites
Neural correlations, population coding and computation
Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743
Correlated neuronal discharge rate and its implications for psychophysical performance
Zohary, E., Shadlen, M. N., & Newsome, W. T. (1994) · Nature · doi:10.1038/370140a0 · Cited by 1,237
Attention improves performance primarily by reducing interneuronal correlations
Cohen, M. R., & Maunsell, J. H. R. (2009) · Nature Neuroscience · doi:10.1038/nn.2439 · Cited by 1,163
Self-citationMeasuring and interpreting neuronal correlations
Cohen, M. R., & Kohn, A. (2011) · Nature Neuroscience · doi:10.1038/nn.2842 · Cited by 1,059
Self-citationSpatial attention decorrelates intrinsic activity fluctuations in macaque area V4
Mitchell, J. F., Sundberg, K. A., & Reynolds, J. H. (2009) · Neuron · doi:10.1016/j.neuron.2009.09.013 · Cited by 710
Information-limiting correlations
Moreno‐Bote, R., Beck, J. M., Kanitscheider, I., Pitkow, X., Latham, P. E., & Pouget, A. (2014) · Nature Neuroscience · doi:10.1038/nn.3807 · Cited by 565
Partitioning neuronal variability
Goris, R. L. T., Movshon, J. A., & Simoncelli, E. P. (2014) · Nature Neuroscience · doi:10.1038/nn.3711 · Cited by 528
Correlations and Neuronal Population Information
Kohn, A., Coen-Cagli, R., Kanitscheider, I., & Pouget, A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013851 · Cited by 372
Decision-related activity in sensory neurons reflects more than a neuron’s causal effect
Nienborg, H., & Cumming, B. G. (2009) · Nature · doi:10.1038/nature07821 · Cited by 365
Perceptual Learning: Toward a Comprehensive Theory
Watanabe, T., & Sasaki, Y. (2014) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015214 · Cited by 358
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Towards the neural population doctrine
Saxena, S., & Cunningham, J. P. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.02.002 · Cited by 334
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
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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
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Panzeri, S., Moroni, M., Safaai, H., & Harvey, C. D. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00606-4 · Cited by 260
Waschke, L., Donoghue, T., Fiedler, L., Smith, S. E., Garrett, D. D., Voytek, B., & Obleser, J. (2021) · eLife · doi:10.7554/elife.70068 · Cited by 251
Representation, Pattern Information, and Brain Signatures: From Neurons to Neuroimaging
Kragel, P. A., Koban, L., Barrett, L. F., & Wager, T. D. (2018) · Neuron · doi:10.1016/j.neuron.2018.06.009 · Cited by 212
Jazayeri, M., & Ostojic, S. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.08.002 · Cited by 210
High-precision coding in visual cortex
Stringer, C., Michaelos, M., Tsyboulski, D. A., Lindo, S. E., & Pachitariu, M. (2021) · Cell · doi:10.1016/j.cell.2021.03.042 · Cited by 184
Circuit Models of Low-Dimensional Shared Variability in Cortical Networks
Huang, C., Ruff, D. A., Pyle, R., Rosenbaum, R., Cohen, M. R., & Doiron, B. (2018) · Neuron · doi:10.1016/j.neuron.2018.11.034 · Cited by 144
Self-citationNajafi, F., Elsayed, G. F., Cao, R., Pnevmatikakis, E. A., Latham, P. E., Cunningham, J. P., & Churchland, A. K. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.045 · Cited by 136
Slow Drift of Neural Activity as a Signature of Impulsivity in Macaque Visual and Prefrontal Cortex
Cowley, B. R., Snyder, A. C., Acar, K., Williamson, R. C., Yu, B. M., & Smith, M. A. (2020) · Neuron · doi:10.1016/j.neuron.2020.07.021 · Cited by 137
Population coding of valence in the basolateral amygdala
Zhang, X., & Li, B. (2018) · Nature Communications · doi:10.1038/s41467-018-07679-9 · Cited by 147
Correlations enhance the behavioral readout of neural population activity in association cortex
Valente, M., Pica, G., Bondanelli, G., Moroni, M., Runyan, C. A., Morcos, A. S., Harvey, C. D., & Panzeri, S. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00845-1 · Cited by 123
Kapoor, V., Dwarakanath, A., Safavi, S., Werner, J., Besserve, M., Panagiotaropoulos, T. I., & Logothetis, N. K. (2022) · Nature Communications · doi:10.1038/s41467-022-28897-2 · Cited by 151
Stable task information from an unstable neural population
Rule, M. E., Loback, A. R., Raman, D. V., Driscoll, L. N., Harvey, C. D., & O'Leary, T. (2020) · eLife · doi:10.7554/elife.51121 · Cited by 106
Principles of Corticocortical Communication: Proposed Schemes and Design Considerations
Kohn, A., Jasper, A. I., Semedo, J. D., Gokcen, E., Machens, C. K., & Yu, B. M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.07.001 · Cited by 108
Information-Limiting Correlations in Large Neural Populations
Bartolo, R., Saunders, R. C., Mitz, A. R., & Averbeck, B. B. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2072-19.2019 · Cited by 103
Kafashan, M., Jaffe, A. W., Chettih, S. N., Nogueira, R., Arandia-Romero, I., Harvey, C. D., Moreno-Bote, R., & Drugowitsch, J. (2021) · Nature Communications · doi:10.1038/s41467-020-20722-y · Cited by 96
Learning is shaped by abrupt changes in neural engagement
Hennig, J. A., Oby, E. R., Golub, M. D., Bahureksa, L., Sadtler, P. T., Quick, K. M., Ryu, S. I., Tyler‐Kabara, E. C., Batista, A. P., Chase, S. M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00822-8 · Cited by 83
Learning and attention increase visual response selectivity through distinct mechanisms
Poort, J., Wilmes, K. A., Blot, A., Chadwick, A., Sahani, M., Clopath, C., Mrsic‐Flogel, T. D., Hofer, S. B., & Khan, A. G. (2021) · Neuron · doi:10.1016/j.neuron.2021.11.016 · Cited by 84
Optimal policy for attention-modulated decisions explains human fixation behavior
Jang, A. I., Sharma, R., & Drugowitsch, J. (2021) · eLife · doi:10.7554/elife.63436 · Cited by 94
Single-trial cross-area neural population dynamics during long-term skill learning
Veuthey, T. L., Derosier, K., Kondapavulur, S., & Ganguly, K. (2020) · Nature Communications · doi:10.1038/s41467-020-17902-1 · Cited by 83
Computation noise in human learning and decision-making: origin, impact, function
Findling, C., & Wyart, V. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.018 · Cited by 83
Ruff, D. A., & Cohen, M. R. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0477-1 · Cited by 79
Self-citationTo look or not to look: dissociating presaccadic and covert spatial attention
Li, H.-H., Hanning, N. M., & Carrasco, M. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.05.002 · Cited by 69
Cognition as a Window into Neuronal Population Space
Ruff, D. A., Ni, A. M., & Cohen, M. R. (2018) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-080317-061936 · Cited by 63
Self-citationFunctional MRI and EEG Index Complementary Attentional Modulations
Itthipuripat, S., Sprague, T. C., & Serences, J. T. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.2519-18.2019 · Cited by 56
Silences, spikes and bursts: Three‐part knot of the neural code
Friedenberger, Z., Harkin, E. F., Tóth, K., & Naud, R. (2023) · The Journal of Physiology · doi:10.1113/jp281510 · Cited by 63
Antonini, A., Sattin, A., Moroni, M., Bovetti, S., Moretti, C., Succol, F., Forli, A., Vecchia, D., Rajamanickam, V. P., Bertoncini, A., et al. (2020) · eLife · doi:10.7554/elife.58882 · Cited by 56
Priority coding in the visual system
Rust, N. C., & Cohen, M. R. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00582-9 · Cited by 59
Self-citationSrinath, R., Ruff, D. A., & Cohen, M. R. (2021) · Current Biology · doi:10.1016/j.cub.2021.09.076 · Cited by 57
Self-citationGeneral learning ability in perceptual learning
Yang, J., Yan, F.-F., Chen, L., Xi, J., Fan, S., Zhang, P., Lu, Z., & Huang, C. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2002903117 · Cited by 48
Individual variability of neural computations underlying flexible decisions
Pagan, M., Tang, V. D., Aoi, M., Pillow, J. W., Mante, V., Sussillo, D., & Brody, C. D. (2024) · Nature · doi:10.1038/s41586-024-08433-6 · Cited by 76
A common probabilistic framework for perceptual and statistical learning
Fiser, J., & Lengyel, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.007 · Cited by 44
A Network Perspective on Sensorimotor Learning
Sohn, H., Meirhaeghe, N., Rajalingham, R., & Jazayeri, M. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.11.007 · Cited by 45
Forli, A., Pisoni, M., Printz, Y., Yizhar, O., & Fellin, T. (2021) · eLife · doi:10.7554/elife.63359 · Cited by 48
On the Cross-Validation Bias due to Unsupervised Preprocessing
Moscovich, A., & Rosset, S. (2022) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12537 · Cited by 55
Sanayei, M., Chen, X., Chicharro, D., Distler, C., Panzeri, S., & Thiele, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06698-w · Cited by 46
McBride, E. G., Lee, S.-Y. J., & Callaway, E. M. (2019) · Current Biology · doi:10.1016/j.cub.2019.03.065 · Cited by 39
The diversity and specificity of functional connectivity across spatial and temporal scales
Engel, T. A., Schölvinck, M. L., & Lewis, C. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118692 · Cited by 42
How learning unfolds in the brain: toward an optimization view
Hennig, J. A., Oby, E. R., Losey, D. M., Batista, A. P., Yu, B. M., & Chase, S. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.005 · Cited by 38
Perceptual Decision-Making: A Field in the Midst of a Transformation
Najafi, F., & Churchland, A. K. (2018) · Neuron · doi:10.1016/j.neuron.2018.10.017 · Cited by 41
The Effects of Population Tuning and Trial-by-Trial Variability on Information Encoding and Behavior
Nogueira, R., Peltier, N. E., Anzai, A., DeAngelis, G. C., Martínez-Trujillo, J., & Moreno‐Bote, R. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0859-19.2019 · Cited by 38
Bridging neuronal correlations and dimensionality reduction
Umakantha, A., Morina, R., Cowley, B. R., Snyder, A. C., Smith, M. A., & Yu, B. M. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.028 · Cited by 40
Multisensory task demands temporally extend the causal requirement for visual cortex in perception
Lohuis, M. N. O., Pie, J. L., Marchesi, P., Montijn, J. S., de Kock, C. P. J., Pennartz, C. M. A., & Olcese, U. (2022) · Nature Communications · doi:10.1038/s41467-022-30600-4 · Cited by 32
The quest for interpretable models of neural population activity
Whiteway, M. R., & Butts, D. A. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.07.004 · Cited by 38
Task-induced neural covariability as a signature of approximate Bayesian learning and inference
Lange, R. D., & Haefner, R. M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009557 · Cited by 45
Decision-related feedback in visual cortex lacks spatial selectivity
Quinn, K. R., Seillier, L., Butts, D. A., & Nienborg, H. (2021) · Nature Communications · doi:10.1038/s41467-021-24629-0 · Cited by 34
Horrocks, E. A. B., Rodrigues, F. R., & Saleem, A. B. (2024) · Nature Communications · doi:10.1038/s41467-024-50563-y · Cited by 35
Contribution of Sensory Encoding to Measured Bias
Jin, M., & Glickfeld, L. L. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.0076-19.2019 · Cited by 29
Characterizing and interpreting the influence of internal variables on sensory activity
Lange, R. D., & Haefner, R. M. (2017) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2017.07.006 · Cited by 22
Noise Correlations for Faster and More Robust Learning
Nassar, M. R., Scott, D., & Bhandari, A. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.3045-20.2021 · Cited by 27
Perceptual learning as a result of concerted changes in prefrontal and visual cortex
Jing, R., Yang, C., Huang, X., & Li, W. (2021) · Current Biology · doi:10.1016/j.cub.2021.08.007 · Cited by 25
Statistical neuroscience in the single trial limit
Williams, A. H., & Linderman, S. W. (2021) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2021.10.008 · Cited by 24
Mosheiff, N., Ermentrout, B., & Huang, C. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010843 · Cited by 24
Stochastic neural field model of stimulus-dependent variability in cortical neurons
Bressloff, P. C. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006755 · Cited by 23
Reinforcement regulates timing variability in thalamus
Wang, J., Hosseini, E. A., Meirhaeghe, N., Akkad, A., & Jazayeri, M. (2020) · eLife · doi:10.7554/elife.55872 · Cited by 26
Low rank mechanisms underlying flexible visual representations
Ruff, D. A., Xue, C., Kramer, L. E., Baqai, F., & Cohen, M. R. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2005797117 · Cited by 20
Self-citationData-Driven Approaches to Understanding Visual Neuron Activity
Butts, D. A. (2019) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091718-014731 · Cited by 19
Pagan, M., Tang, V. D., Aoi, M., Pillow, J. W., Mante, V., Sussillo, D., & Brody, C. D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.28.518207 · Cited by 21
Pairwise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex
Koren, V., Andrei, A. R., Hu, M., Dragoi, V., & Obermayer, K. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108367 · Cited by 23
Internally generated population activity in cortical networks hinders information transmission
Huang, C., Pouget, A., & Doiron, B. (2022) · Science Advances · doi:10.1126/sciadv.abg5244 · Cited by 20
A general decoding strategy explains the relationship between behavior and correlated variability
Ni, A. M., Huang, C., Doiron, B., & Cohen, M. R. (2022) · eLife · doi:10.7554/elife.67258 · Cited by 18
Self-citationHigh precision coding in visual cortexPreprint
Stringer, C., Michaelos, M., & Pachitariu, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/679324 · Cited by 20
Visual exposure enhances stimulus encoding and persistence in primary cortex
Lazăr, A., Lewis, C., Fries, P., Singer, W., & Nikolić, D. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105276118 · Cited by 19
Efficient coding theory of dynamic attentional modulation
Młynarski, W. F., & Tkačik, G. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001889 · Cited by 19
Wasmuht, D., Parker, A. J., & Krug, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-47786-1 · Cited by 20
Dynamics of cortical contrast adaptation predict perception of signals in noise
Angeloni, C. F., Młynarski, W. F., Piasini, E., Williams, A. M., Wood, K. C., Garami, L., Hermundstad, A. M., & Geffen, M. N. (2023) · Nature Communications · doi:10.1038/s41467-023-40477-6 · Cited by 18
Signatures of task learning in neural representations
Gurnani, H., & Cayco-Gajic, N. A. (2023) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2023.102759 · Cited by 19
Investigating the representation of uncertainty in neuronal circuits
Dehaene, G., Coen-Cagli, R., & Pouget, A. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008138 · Cited by 18
Compact deep neural network models of the visual cortex
Cowley, B. R., Stan, P. L., Pillow, J. W., & Smith, M. A. (2026) · Nature · doi:10.1038/s41586-026-10150-1 · Cited by 24
Acar, K., Kiorpes, L., Movshon, J. A., & Smith, M. A. (2019) · Journal of Neurophysiology · doi:10.1152/jn.00232.2019 · Cited by 18
Kang, I., & Maunsell, J. H. R. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.2587-19.2020 · Cited by 17
Brain-wide ongoing activity is responsible for significant cross-trial BOLD variability
Zhang, Q., Cramer, S. R., Ma, Z., Turner, K. L., Gheres, K. W., Liu, Y., Drew, P. J., & Zhang, N. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac016 · Cited by 18
EEG Signals Index a Global Signature of Arousal Embedded in Neuronal Population Recordings
Johnston, R., Snyder, A. C., Schibler, R. S., & Smith, M. A. (2022) · eNeuro · doi:10.1523/eneuro.0012-22.2022 · Cited by 20
Behavioral read-out from population value signals in primate orbitofrontal cortex
McGinty, V. B., & Lupkin, S. M. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01473-7 · Cited by 25
Targeted V1 comodulation supports task-adaptive sensory decisions
Haimerl, C., Ruff, D. A., Cohen, M. R., Savin, C., & Simoncelli, E. P. (2023) · Nature Communications · doi:10.1038/s41467-023-43432-7 · Cited by 16
Self-citationFunctional network properties of the auditory cortex
Lestang, J.-H., Cai, H., Averbeck, B. B., & Cohen, Y. E. (2023) · Hearing Research · doi:10.1016/j.heares.2023.108768 · Cited by 13
Task-Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation
Pedraja, F., Hofmann, V., Goulet, J., & Engelmann, J. (2019) · Journal of Neuroscience · doi:10.1523/jneurosci.1024-19.2019 · Cited by 15
Circuit models of low dimensional shared variability in cortical networksPreprint
Huang, C., Ruff, D. A., Pyle, R., Rosenbaum, R., Cohen, M. R., & Doiron, B. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/217976 · Cited by 15
Self-citationCheng, Y.-A., Sanayei, M., Chen, X., Jia, K., Li, S., Fang, F., Watanabe, T., Thiele, A., & Zhang, R. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02149-x · Cited by 20
Methylphenidate as a causal test of translational and basic neural coding hypotheses
Ni, A. M., Bowes, B., Ruff, D. A., & Cohen, M. R. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2120529119 · Cited by 13
Self-citationCalibrating vision: Concepts and questions
Bosten, J. M., Coen-Cagli, R., Franklin, A., Solomon, S. G., & Webster, M. A. (2022) · Vision Research · doi:10.1016/j.visres.2022.108131 · Cited by 13
Park, J., Kim, S., Kim, H. R., & Lee, J. (2023) · Science Advances · doi:10.1126/sciadv.adg4156 · Cited by 15
Understanding Sensory Information Processing Through Simultaneous Multi-area Population Recordings
Zavitz, E., & Price, N. S. C. (2019) · Frontiers in Neural Circuits · doi:10.3389/fncir.2018.00115 · Cited by 17
Task-specific modulation of PFC activity for matching-rule governed decision-making
Parto-Dezfouli, M., Zarei, M. M., Constantinidis, C., & Daliri, M. R. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02191-7 · Cited by 16
Weak evidence for neural correlates of task-switching in macaque V1
Lange, R. D., Gómez-Laberge, C., Berezovskii, V. K., Pletenev, A., Sherdil, A., Hartmann, T. S., Haefner, R. M., & Born, R. T. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00085.2022 · Cited by 14
Steinfeld, R., Tacão-Monteiro, A., & Renart, A. (2024) · Current Biology · doi:10.1016/j.cub.2024.04.040 · Cited by 17
Yin, C., Melin, M. D., Rojas-Bowe, G., Sun, X. R., Couto, J., Gluf, S., Kostiuk, A., Musall, S., & Churchland, A. K. (2025) · Neuron · doi:10.1016/j.neuron.2025.06.001 · Cited by 23
A distinct population of heterogeneously color-tuned neurons in macaque visual cortex
Nigam, S., Pojoga, S., & Dragoi, V. (2021) · Science Advances · doi:10.1126/sciadv.abc5837 · Cited by 14
Heller, C. R., & David, S. V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271136 · Cited by 12
Learning nonnative speech sounds changes local encoding in the adult human cortex
Yi, H., Chandrasekaran, B., Nourski, K. V., Rhone, A. E., Schuerman, W. L., Howard, M. A., Chang, E. F., & Leonard, M. K. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2101777118 · Cited by 13
Decision-related activity and movement selection in primate visual cortex
Laamerad, P., Liu, L. D., & Pack, C. C. (2024) · Science Advances · doi:10.1126/sciadv.adk7214 · Cited by 11
Noise correlations and perceptual inference
Bányai, M., & Orbán, G. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.09.002 · Cited by 10
Stimulation of the ventral tegmental area induces visual cortical plasticity at the neuronal level
Herpers, J., Arsenault, J. T., Vanduffel, W., & Vogels, R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109998 · Cited by 12
Ruff, D. A., & Cohen, M. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/372888 · Cited by 12
Self-citationHassen, S. B. H., & Hamed, S. B. (2020) · Current Opinion in Physiology · doi:10.1016/j.cophys.2020.07.015 · Cited by 10
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Kass, R. E., Bong, H., Olarinre, M., Qi, X., & Urban, K. N. (2023) · Journal of Neurophysiology · doi:10.1152/jn.00131.2023 · Cited by 9
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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