CNS learns stable, accurate, and efficient movements using a simple algorithm

David W. Franklin, Etienne Burdet, Peng Tee Keng, Rieko Osu, Chee Meng Chew, Theodore E. Milner, Mitsuo Kawato

研究成果: Article査読

227 被引用数 (Scopus)

抄録

We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.

本文言語English
ページ(範囲)11165-11173
ページ数9
ジャーナルJournal of Neuroscience
28
44
DOI
出版ステータスPublished - 2008 10月 29
外部発表はい

ASJC Scopus subject areas

  • 神経科学(全般)

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