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The Journal of Neuroscience, October 29, 2008, 28(44):11165-11173; doi:10.1523/JNEUROSCI.3099-08.2008

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Behavioral/Systems/Cognitive
CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

David W. Franklin,^1,2,3 Etienne Burdet,^4,5 Keng Peng Tee,^5,6 Rieko Osu,^1,2 Chee-Meng Chew,⁵ Theodore E. Milner,⁷ and Mitsuo Kawato¹

¹ATR Computational Neuroscience Laboratories, Keihanna Science City, Kyoto 619-0288, Japan, ²National Institute of Information and Communications Technology, Keihanna Science City, Kyoto 619-0288, Japan, ³Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom, ⁴Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom, ⁵Department of Mechanical Engineering, National University of Singapore, 117576, Singapore, ⁶Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore, and ⁷Department of Kinesiology and Physical Education, McGill University, Montreal, Canada H2W 1S4

Correspondence should be addressed to David W. Franklin, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK. Email: dwf25{at}cam.ac.uk

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.

Key words: motor control; motor learning; impedance control; internal model; computational algorithm; muscle cocontraction; stability; stiffness

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Received July 3, 2008; revised Sept. 12, 2008; accepted Sept. 15, 2008.

Correspondence should be addressed to David W. Franklin, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK. Email: dwf25{at}cam.ac.uk

This article has been cited by other articles:

				R. Laboissiere, D. R. Lametti, and D. J. Ostry Impedance Control and Its Relation to Precision in Orofacial Movement J Neurophysiol, July 1, 2009; 102(1): 523 - 531. [Abstract] [Full Text] [PDF]

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