Please use this identifier to cite or link to this item:
|Title:||CNS learns stable, accurate, and efficient movements using a simple algorithm|
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 17, 2018
WEB OF SCIENCETM
checked on Oct 10, 2018
checked on May 18, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.