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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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.
“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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- 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.
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