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 preci...

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Vydáno v:The Journal of neuroscience Ročník 28; číslo 44; s. 11165
Hlavní autoři: Franklin, David W, Burdet, Etienne, Tee, Keng Peng, Osu, Rieko, Chew, Chee-Meng, Milner, Theodore E, Kawato, Mitsuo
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 29.10.2008
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ISSN:1529-2401, 1529-2401
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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.
AbstractList 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.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.
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.
Author Tee, Keng Peng
Franklin, David W
Chew, Chee-Meng
Kawato, Mitsuo
Milner, Theodore E
Osu, Rieko
Burdet, Etienne
Author_xml – sequence: 1
  givenname: David W
  surname: Franklin
  fullname: Franklin, David W
  email: dwf25@cam.ac.uk
  organization: ATR Computational Neuroscience Laboratories, Keihanna Science City, Kyoto 619-0288, Japan. dwf25@cam.ac.uk
– sequence: 2
  givenname: Etienne
  surname: Burdet
  fullname: Burdet, Etienne
– sequence: 3
  givenname: Keng Peng
  surname: Tee
  fullname: Tee, Keng Peng
– sequence: 4
  givenname: Rieko
  surname: Osu
  fullname: Osu, Rieko
– sequence: 5
  givenname: Chee-Meng
  surname: Chew
  fullname: Chew, Chee-Meng
– sequence: 6
  givenname: Theodore E
  surname: Milner
  fullname: Milner, Theodore E
– sequence: 7
  givenname: Mitsuo
  surname: Kawato
  fullname: Kawato, Mitsuo
BackLink https://www.ncbi.nlm.nih.gov/pubmed/18971459$$D View this record in MEDLINE/PubMed
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Snippet 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...
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SubjectTerms Adaptation, Physiological - physiology
Adult
Algorithms
Central Nervous System - physiology
Female
Humans
Learning - physiology
Male
Movement - physiology
Postural Balance - physiology
Psychomotor Performance - physiology
Title CNS learns stable, accurate, and efficient movements using a simple algorithm
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