CNS learns stable, accurate, and efficient movements using a simple algorithm.
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.
Franklin
David W
DW
ATR Computational Neuroscience Laboratories, Keihanna Science City, Kyoto 619-0288, Japan. dwf25@cam.ac.uk
Burdet
Etienne
E
Tee
Keng Peng
KP
Osu
Rieko
R
Chew
Chee-Meng
CM
Milner
Theodore E
TE
Kawato
Mitsuo
M
D000222:Adaptation, Physiological / Q000502:physiology; D000328:Adult; D000465:Algorithms; D002490:Central Nervous System / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D009068:Movement / Q000502:physiology*; D004856:Postural Balance / Q000502:physiology; D011597:Psychomotor Performance / Q000502:physiology