Journal
NEURON
Volume 54, Issue 2, Pages 319-333Publisher
CELL PRESS
DOI: 10.1016/j.neuron.2007.03.017
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Funding
- NIDA NIH HHS [R01 DA016455-05, DA016455, R01 DA016455] Funding Source: Medline
- NIMH NIH HHS [R01 MH062349, R01 MH062349-05] Funding Source: Medline
- NINDS NIH HHS [R01 NS050944, 5R01NS35145-9, NS50944, R01 NS035145] Funding Source: Medline
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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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