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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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Decision Making in Recurrent Neuronal Circuits
Wang, X.-J. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.034 · Cited by 679
“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).”
MethodSelf-citationEmotion, Cognition, and Mental State Representation in Amygdala and Prefrontal Cortex
Salzman, C. D., & Fusi, S. (2010) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135256 · Cited by 628
Self-citationNeuromorphic Electronic Circuits for Building Autonomous Cognitive Systems
Chicca, E., Stefanini, F., Bartolozzi, C., & Indiveri, G. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2313954 · Cited by 571
Buzsáki, G. (2019) · doi:10.1093/oso/9780190905385.001.0001 · Cited by 383
The Persistence and Transience of Memory
Richards, B. A., & Frankland, P. W. (2017) · Neuron · doi:10.1016/j.neuron.2017.04.037 · Cited by 338
Encoding and decoding in parietal cortex during sensorimotor decision-making
Park, I. M., Meister, M. L. R., Huk, A. C., & Pillow, J. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3800 · Cited by 273
Fractals in the nervous system: conceptual implications for theoretical neuroscience
Werner, G. (2010) · Frontiers in Physiology · doi:10.3389/fphys.2010.00015 · Cited by 252
Network Resets in Medial Prefrontal Cortex Mark the Onset of Behavioral Uncertainty
Karlsson, M. P., Tervo, D. G. R., & Karpova, A. Y. (2012) · Science · doi:10.1126/science.1226518 · Cited by 252
Fritz, J. B., David, S. V., Radtke‐Schuller, S., Yin, P., & Shamma, S. (2010) · Nature Neuroscience · doi:10.1038/nn.2598 · Cited by 231
Dual Adaptation Supports a Parallel Architecture of Motor Memory
Lee, J.-Y., & Schweighofer, N. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1294-09.2009 · Cited by 202
Long-Term Retention Explained by a Model of Short-Term Learning in the Adaptive Control of Reaching
Joiner, W. M., & Smith, M. A. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90706.2008 · Cited by 195
Histed, M. H., Pasupathy, A., & Miller, E. K. (2009) · Neuron · doi:10.1016/j.neuron.2009.06.019 · Cited by 187
Self-citationAbstract context representations in primate amygdala and prefrontal cortex
Saez, A., Rigotti, M., Ostojic, S., Fusi, S., & Salzman, C. D. (2015) · Neuron · doi:10.1016/j.neuron.2015.07.024 · Cited by 183
Self-citationSynthesizing cognition in neuromorphic electronic systems
Neftci, E., Binas, J., Rutishauser, U., Chicca, E., Indiveri, G., & Douglas, R. J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1212083110 · Cited by 153
A Shared Resource between Declarative Memory and Motor Memory
Keisler, A., & Shadmehr, R. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.4160-10.2010 · Cited by 144
Roxin, A., & Ledberg, A. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000046 · Cited by 139
The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain
Zylberberg, A., Slezak, D. F., Roelfsema, P. R., Dehaene, S., & Sigman, M. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000765 · Cited by 141
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Self-citationIigaya, K., Fonseca, M. S., Murakami, M., Mainen, Z. F., & Dayan, P. (2018) · Nature Communications · doi:10.1038/s41467-018-04840-2 · Cited by 120
Lo, C., Boucher, L., Paré, M., Schall, J. D., & Wang, X.-J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.6164-08.2009 · Cited by 124
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Siniscalchi, M. J., Phoumthipphavong, V., Ali, F., Lozano, M., & Kwan, A. C. (2016) · Nature Neuroscience · doi:10.1038/nn.4342 · Cited by 112
Asaad, W. F., & Eskandar, E. N. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3793-11.2011 · Cited by 105
Self-citationReinforcement learning in populations of spiking neurons
Urbanczik, R., & Senn, W. (2008) · Nature Neuroscience · doi:10.1038/nn.2264 · Cited by 94
How to set the switches on this thing
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Multiple Timescales of Memory in Lateral Habenula and Dopamine Neurons
Bromberg-Martin, E. S., Matsumoto, M., Nakahara, H., & Hikosaka, O. (2010) · Neuron · doi:10.1016/j.neuron.2010.06.031 · Cited by 97
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Arandia-Romero, I., Tanabe, S., Drugowitsch, J., Kohn, A., & Moreno‐Bote, R. (2016) · Neuron · doi:10.1016/j.neuron.2016.01.044 · Cited by 89
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Self-citationHow can a Bayesian approach inform neuroscience?
O’Reilly, J. X., Jbabdi, S., & Behrens, T. E. J. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08010.x · Cited by 98
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Johnston, K., DeSouza, J. F. X., & Everling, S. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5953-08.2009 · Cited by 84
Simple substrates for complex cognition
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The many faces of forgetting: Toward a constructive view of forgetting in everyday life.
Fawcett, J. M., & Hulbert, J. C. (2020) · Journal of Applied Research in Memory and Cognition · doi:10.1016/j.jarmac.2019.11.002 · Cited by 89
Feature-specific prediction errors and surprise across macaque fronto-striatal circuits
Oemisch, M., Westendorff, S., Azimi, M., Hassani, S. A., Ardid, S., Tiesinga, P. H. E., & Womelsdorf, T. (2019) · Nature Communications · doi:10.1038/s41467-018-08184-9 · Cited by 72
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Eckhoff, P. A., Wong‐Lin, K., & Holmes, P. J. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.5024-08.2009 · Cited by 67
Talbot, Z. N., Sparks, F. T., Dvořák, D., Curran, B. M., Alarcón, J. M., & Fenton, A. A. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.043 · Cited by 70
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Iigaya, K., Ahmadian, Y., Sugrue, L. P., Corrado, G. S., Loewenstein, Y., Newsome, W. T., & Fusi, S. (2019) · Nature Communications · doi:10.1038/s41467-019-09388-3 · Cited by 58
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Koval, M. J., Lomber, S. G., & Everling, S. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.1258-11.2011 · Cited by 56
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Goudar, V., Peysakhovich, B., Freedman, D. J., Buffalo, E. A., & Wang, X. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01293-9 · Cited by 58
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Lo, C., & Wang, X. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005081 · Cited by 21
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Hiratani, N., & Fukai, T. (2016) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00041 · Cited by 20
Neiman, T., & Loewenstein, Y. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2068-12.2013 · Cited by 17
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Self-citationFoucault, C., & Meyniel, F. (2021) · eLife · doi:10.7554/elife.71801 · Cited by 16
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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
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 ›
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