Please use this identifier to cite or link to this item: https://doi.org/10.1523/JNEUROSCI.3099-08.2008
Title: CNS learns stable, accurate, and efficient movements using a simple algorithm
Authors: Franklin, D.W.
Burdet, E. 
Keng, P.T.
Osu, R.
Chew, C.-M. 
Milner, T.E.
Kawato, M.
Keywords: Computational algorithm
Impedance control
Internal model
Motor control
Motor learning
Muscle cocontraction
Stability
Stiffness
Issue Date: 29-Oct-2008
Source: Franklin, D.W.,Burdet, E.,Keng, P.T.,Osu, R.,Chew, C.-M.,Milner, T.E.,Kawato, M. (2008-10-29). CNS learns stable, accurate, and efficient movements using a simple algorithm. Journal of Neuroscience 28 (44) : 11165-11173. ScholarBank@NUS Repository. https://doi.org/10.1523/JNEUROSCI.3099-08.2008
Abstract: 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. Copyright © 2008 Society for Neuroscience.
Source Title: Journal of Neuroscience
URI: http://scholarbank.nus.edu.sg/handle/10635/59716
ISSN: 02706474
DOI: 10.1523/JNEUROSCI.3099-08.2008
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