The emergence of movement units through learning with noisy efferent signals and delayed sensory feedback

by: Michael Kositsky, Andrew G Barto

Neurocomputing, Vol. 44-46 (June 2002), pp. 889-895.

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摘要

Rapid human arm movements often have velocity profiles consisting of several bell-shaped acceleration-deceleration phases, sometimes overlapping in time and sometimes appearing separately. We show how such sub-movement sequences can emerge naturally as an optimal control policy is approximated by a reinforcement learning system in the face of uncertainty and feedback delay. The system learns to generate sequences of pulse-step commands, producing fast initial sub-movements followed by several slow corrective sub-movements that often begin before the initial sub-movement has completed. These results suggest how the nervous system might efficiently control a stochastic motor plant under uncertainty and feedback delay.

@article{citeulike:401498, abstract = {Rapid human arm movements often have velocity profiles consisting of several bell-shaped acceleration-deceleration phases, sometimes overlapping in time and sometimes appearing separately. We show how such sub-movement sequences can emerge naturally as an optimal control policy is approximated by a reinforcement learning system in the face of uncertainty and feedback delay. The system learns to generate sequences of pulse-step commands, producing fast initial sub-movements followed by several slow corrective sub-movements that often begin before the initial sub-movement has completed. These results suggest how the nervous system might efficiently control a stochastic motor plant under uncertainty and feedback delay.}, author = {Kositsky, Michael and Barto, Andrew G. }, citeulike-article-id = {401498}, doi = {10.1016/S0925-2312(02)00488-5}, journal = {Neurocomputing}, month = {June}, pages = {889--895}, posted-at = {2005-11-19 21:21:51}, priority = {2}, title = {The emergence of movement units through learning with noisy efferent signals and delayed sensory feedback}, url = {http://dx.doi.org/10.1016/S0925-2312(02)00488-5}, volume = {44-46}, year = {2002} }

RIS record

TY - JOUR ID - citeulike:401498 L3 - citeulike-article-id:401498 TI - The emergence of movement units through learning with noisy efferent signals and delayed sensory feedback JF - Neurocomputing VL - 44-46 SP - 889 EP - 895 N2 - Rapid human arm movements often have velocity profiles consisting of several bell-shaped acceleration-deceleration phases, sometimes overlapping in time and sometimes appearing separately. We show how such sub-movement sequences can emerge naturally as an optimal control policy is approximated by a reinforcement learning system in the face of uncertainty and feedback delay. The system learns to generate sequences of pulse-step commands, producing fast initial sub-movements followed by several slow corrective sub-movements that often begin before the initial sub-movement has completed. These results suggest how the nervous system might efficiently control a stochastic motor plant under uncertainty and feedback delay. AU - Kositsky, Michael AU - Barto, Andrew PY - 2002/06// UR - http://dx.doi.org/10.1016/S0925-2312(02)00488-5 ER -

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