RT Journal Article
SR Electronic
T1 CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 11165
OP 11173
DO 10.1523/JNEUROSCI.3099-08.2008
VO 28
IS 44
A1 David W. Franklin
A1 Etienne Burdet
A1 Keng Peng Tee
A1 Rieko Osu
A1 Chee-Meng Chew
A1 Theodore E. Milner
A1 Mitsuo Kawato
YR 2008
UL http://www.jneurosci.org/content/28/44/11165.abstract
AB 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.