CNS learns stable, accurate, and efficient movements using a simple algorithm

David W. Franklin, Etienne Burdet, Peng Tee Keng, Rieko Osu, Chee Meng Chew, Theodore E. Milner, Mitsuo Kawato

Research output: Contribution to journalArticlepeer-review

254 Scopus citations


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.

Original languageEnglish
Pages (from-to)11165-11173
Number of pages9
JournalJournal of Neuroscience
Issue number44
StatePublished - 29 Oct 2008
Externally publishedYes


  • Computational algorithm
  • Impedance control
  • Internal model
  • Motor control
  • Motor learning
  • Muscle cocontraction
  • Stability
  • Stiffness


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