Journal
JOURNAL OF NEUROSCIENCE
Volume 28, Issue 44, Pages 11165-11173Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.3099-08.2008
Keywords
motor control; motor learning; impedance control; internal model; computational algorithm; muscle cocontraction; stability; stiffness
Categories
Funding
- National Institute of Information and Communications Technology of Japan
- Natural Sciences and Engineering Research Council of Canada
- Human Frontier Science Program
- National University of Singapore
- Strategic Information and Communications Research and Development Promotion Programme
- Ministry of Internal Affairs and Communications, Japan
- Natural Sciences and Engineering Research Council, Canada
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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.
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