TY - JOUR
T1 - A Neural Circuit Model of Flexible Sensorimotor Mapping
T2 - Learning and Forgetting on Multiple Timescales
AU - Fusi, Stefano
AU - Asaad, Wael F.
AU - Miller, Earl K.
AU - Wang, Xiao Jing
N1 - Funding Information:
This work was supported by the NIH grant DA016455 (X.-J.W. and S.F.), the NIH-CRCNS grant NS50944 (X.-J.W.), the SNF grant PP00A-106556 (S.F.), and the NINDS grant 5R01NS35145-9 (E.K.M.), and it was partly done when X.-J.W. was at Brandeis University and S.F. was affiliated with the Institute of Physiology in Bern (Switzerland), visiting X.-J.W. in his lab at Brandeis. The authors are grateful to C.D. Salzman, L.F. Abbott, and N. Brunel for helpful comments on the manuscript and to N. Daw for useful discussions.
PY - 2007/4/19
Y1 - 2007/4/19
N2 - 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.
AB - 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.
KW - SYSNEURO
UR - http://www.scopus.com/inward/record.url?scp=34147179944&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34147179944&partnerID=8YFLogxK
U2 - 10.1016/j.neuron.2007.03.017
DO - 10.1016/j.neuron.2007.03.017
M3 - Article
C2 - 17442251
AN - SCOPUS:34147179944
SN - 0896-6273
VL - 54
SP - 319
EP - 333
JO - Neuron
JF - Neuron
IS - 2
ER -