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A Neural Circuit Model of Flexible Sensorimotor Mapping: Learning and Forgetting on Multiple Timescales
Open accessAbstract
Volitional behavior relies on the brain's ability to remap sensory flow to motor programs whenever demanded by a changed behavioral context. To investigate the circuit basis of such flexible behavior, we have developed a biophysically based decision-making network model of spiking neurons for arbitrary sensorimotor mapping. The model quantitatively reproduces behavioral and prefrontal single-cell data from an experiment in which monkeys learn visuomotor associations that are reversed unpredictably from time to time. We show that when synaptic modifications occur on multiple timescales, the model behavior becomes flexible only when needed: slow components of learning usually dominate the decision process. However, if behavioral contexts change frequently enough, fast components of plasticity take over, and the behavior exhibits a quick forget-and-learn pattern. This model prediction is confirmed by monkey data. Therefore, our work reveals a scenario for conditional associative learning that is distinct from instant switching between sets of well-established sensorimotor associations.
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“Specifically, we used binary synapses that undergo a stochastic Hebbian learning rule, namely that synaptic plasticity depends on coactivation of presynaptic and postsynaptic neurons and takes place probabilistically (Fusi, 2002; Fusi et al., 2007).”
From Decision Making in Recurrent Neuronal Circuits (2008) · cites this paper as Fusi et al., 2007
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Cites
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
Daw, N. D., Niv, Y., & Dayan, P. (2005) · Nature Neuroscience · doi:10.1038/nn1560 · Cited by 2,533
Uncertainty, Neuromodulation, and Attention
Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774
Inferotemporal cortex and object vision
Tanaka, K. (1996) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.19.030196.000545 · Cited by 1,402
Multiple reward signals in the brain
Schultz, W. (2000) · Nature Reviews Neuroscience · doi:10.1038/35044563 · Cited by 1,303
Probabilistic decision making by slow reverberation in cortical circuits
Wang, X.-J. (2002) · Neuron · doi:10.1016/s0896-6273(02)01092-9 · Cited by 1,242
Self-citationSingle neurons in prefrontal cortex encode abstract rules
Wallis, J. D., Anderson, K. C., & Miller, E. K. (2001) · Nature · doi:10.1038/35082081 · Cited by 1,075
Self-citationMatching Behavior and the Representation of Value in the Parietal Cortex
Sugrue, L. P., Corrado, G. S., & Newsome, W. T. (2004) · Science · doi:10.1126/science.1094765 · Cited by 1,030
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Items 101–199 of 199
Temporal context and conditional associative learning
Hamid, O. H., Wendemuth, A., & Braun, J. (2010) · BMC Neuroscience · doi:10.1186/1471-2202-11-45 · Cited by 16
Synaptic encoding of temporal contiguity
Ostojic, S. (2013) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2013.00032 · Cited by 15
The Cognitive Body: From Dynamic Modulation to Anticipation
Montebelli, A., Lowe, R., & Ziemke, T. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02565-5_8 · Cited by 12
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Liu, Y., & Wang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-50501-y · Cited by 12
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Bröker, F., Marshall, L., Bestmann, S., & Dayan, P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0205974 · Cited by 12
Esposito, U., Giugliano, M., & Vasilaki, E. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00175 · Cited by 10
Érdi, P., Kiss, T., & Ujfalussy, B. B. (2010) · Springer eBooks · doi:10.1007/978-1-4419-0996-1_18 · Cited by 10
Iigaya, K., Ahmadian, Y., Sugrue, L. P., Corrado, G. S., Loewenstein, Y., Newsome, W. T., & Fusi, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/141309 · Cited by 11
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Ye, W., Mai, W., & Hu, G. (2018) · Nonlinear Dynamics · doi:10.1007/s11071-018-4337-4 · Cited by 10
Mandelblat-Cerf, Y., Novick, I., & Vaadia, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021626 · Cited by 10
Bugmann, G. (2011) · Brain Research · doi:10.1016/j.brainres.2011.09.028 · Cited by 11
A characterization of the neural representation of confidence during probabilistic learning
Bounmy, T., Eger, E., & Meyniel, F. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2022.119849 · Cited by 16
Medial Frontal Circuit Dynamics Represents Probabilistic Choices for Unfamiliar Sensory Experience
Handa, T., Takekawa, T., Harukuni, R., Isomura, Y., & Fukai, T. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx031 · Cited by 8
Saitō, H., Katahira, K., Okanoya, K., & Okada, M. (2014) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2014.00018 · Cited by 9
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Min, B., Bliss, D. P., Sarma, A., Freedman, D. J., & Wang, X. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.15.151506 · Cited by 7
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Sure enough: efficient Bayesian learning and choice
Foley, B. R., & Marjoram, P. (2017) · Animal Cognition · doi:10.1007/s10071-017-1107-5 · Cited by 8
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Bhat, A. A., Mahajan, G., & Mehta, A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025048 · Cited by 7
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Soo, W. W. M., Goudar, V., & Wang, X.-J. (2023) · neural information processing systems · doi:10.52202/075280-1391 · Cited by 6
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Soltani, A., Chaisangmongkon, W., & Wang, X.-J. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805308-9.00013-0 · Cited by 5
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Mahajan, G., & Mehta, A. (2011) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/95/48008 · Cited by 4
Statistical Mechanics of Reward-Modulated Learning in Decision-Making Networks
Katahira, K., Okanoya, K., & Okada, M. (2012) · Neural Computation · doi:10.1162/neco_a_00264 · Cited by 5
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Torres, J. J., Baroni, F., Latorre, R., & Varona, P. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.031 · Cited by 4
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Tavoni, G., Balasubramanian, V., & Gold, J. I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/563346 · Cited by 4
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Prat-Ortega, G., Wimmer, K., Roxin, A., & de la Rocha, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.23.110460 · Cited by 4
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Duque, D., & de la Rocha, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.17.476581 · Cited by 3
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Seo, H., & Lee, D. (2009) · Nature · doi:10.1038/461050a · Cited by 3
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Pereyra, A. E., Mininni, C. J., & Zanutto, B. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-93313-6 · Cited by 2
Iigaya, K., Fonseca, M. S., Murakami, M., Mainen, Z. F., & Dayan, P. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/215400 · Cited by 2
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Goel, G., Chen, N., & Wierman, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07785 · Cited by 2
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Humann, J., Fischer, A. G., & Ullsperger, M. (2020) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/8qzu2 · Cited by 2
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Muro, G. D., & Ferrari, S. (2009) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2009.5164744 · Cited by 1
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Ye, W., Liu, S., Liu, X., & Yu, Y. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.05.001 · Cited by 1
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Takiyama, K. (2016) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.85.114801 · Cited by 1
Time Series from a Nonlinear Dynamical Systems Perspective
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Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0010 · Cited by 1
Funahashi, S., Gao, B., Takeda, K., Watanabe, Y., Wu, J., & Yan, T. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad280 · Cited by 1
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Neurodynamical modeling of arbitrary visuomotor tasks
Loh, M. (2008) · Repositori digital de la UPF (Universitat Pompeu Fabra) · Cited by 1
Montebelli, A., Lowe, R., & Ziemke, T. (2010) · Institution of Engineering and Technology eBooks · doi:10.1049/pbce071e_ch9 · Cited by 1
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Histed, M. H., Pasupathy, A., & Miller, E. K. (2009) · DSpace@MIT (Massachusetts Institute of Technology)
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Rigotti, M. (2010)
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Lindsay, T. H. (2012) · ProQuest Demo Repository
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Azimi, M., Oemisch, M., & Womelsdorf, T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/369496
Quintanar-Zilinskas, V. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/621201
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0011
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0012
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Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0005
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0013
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Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0004
Qiu, S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.15.426814
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Monfared, Z., Mikhaeil, J. M., & Durstewitz, D. (2021) · arXiv (Cornell University)
程振波, 邓志东, & 杨博 (2009) · Science in China Series C-Life Sciences (in Chinese) · doi:10.1360/zc2009-39-8-783
Simpson, E. H. (2018) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.733505841.793549124
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.003.0014
Schoenbaum, G. M., & Costa, K. M. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735754165.793563139
Collins, A. G. E. (2010) · OpenGrey (Institut de l'Information Scientifique et Technique)
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Self-citationIigaya, K., Larsen, T., FONG, T. W., & O’Doherty, J. P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.08.574767
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.002.0006
Buzsáki, G. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190905385.002.0003
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Hebbian mechanisms and temporal contiguity for unsupervised task-set learning
Bouchacourt, F. (2016) · theses.fr (ABES) · doi:10.70675/8f77cdc9z8ebcz4463z8487z25d33b53d315
Flexibility in value-based decision-making
Jahn, C. I. (2018) · theses.fr (ABES) · doi:10.70675/24ffd7ecz04aez45dez9bfdzff312b4881a8
Metadata
- Authors
- Fusi, S., Asaad, W. F., Miller, E. K., & Wang, X.-J.
- Affiliations
- University of Zurich (CH)
- Massachusetts General Hospital (US)
- Massachusetts Institute of Technology (US)
- Yale University (US)
- Corresponding author
- Wang, Xiao-Jing
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2007-04
- Volume
- 54
- Issue
- 2
- Pages
- 319-333
- ISSN
- 0896-6273 (ISSN-L)
- PMC
- PMC2833020
- OpenAlex
- W2101666653
- Cited by
- 199
- References
- 58
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 8 · 2022: 6 · 2021: 6 · 2020: 8 · 2019: 29 · 2018: 11 · 2017: 8 · 2016: 15 · 2015: 5 · 2014: 6 · 2013: 9 · 2012: 8
- FWCI
- 8.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (106556)
- National Institutes of Health (5r01ns035145-15)
- National Institute of Mental Health (R01MH062349)
- National Institute on Drug Abuse (DA016455, R01DA016455)
- National Institute of Neurological Disorders and Stroke (R01NS050944, NS50944, R01-NS035145, 5R01NS35145-9)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- sensorimotor mapping, behavioral flexibility, associative learning, forgetting, synaptic plasticity, multiple time scales, prefrontal cortex, spiking neuron models, conditional learning
- MeSH terms
- Algorithms, Animals, Haplorhini, Association Learning, Cues, Decision Making, Learning, Memory, Models, Neurological, Neurons, Psychomotor Performance, Mental Recall, Synapses, Neural Networks, Computer, Prefrontal Cortex
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