CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm

By David W. Franklin, Etienne Burdet, Keng Peng Tee, Rieko Osu, Chee-Meng Chew, Theodore E. Milner, Mitsuo Kawato. Journal of Neuroscience, 2008

340 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Social Psychology

View Paper: https://www.semanticscholar.org/paper/be96ba0a036b815a21fed6d8fdee1bfbbebb3b9e

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Citations in the corpus

Human-Like Adaptation of Force and Impedance in Stable and Unstable Interactions (2011, background): “We found principles of motor learning [8], [9] that yield the adaptation of both force and impedance by the concurrent minimization of motion error and effort while maintaining a fixed stability margin.”
Principles of sensorimotor learning (2011, background): “In terms of the interaction between stiffness and predictive control, it has been proposed that when errors are large, stiffness increases but as the predictive component begins to learn, and errors reduce, stiffness decreases 38 .”
Computational mechanisms of sensorimotor control. (2011, neutral): “activation, the learning algorithm tunes the time varying feedforward activation to the nonlinear nonstationary changes in the environment (Franklin et al., 2008).”
On the analysis of movement smoothness (2015, background): “Smooth movements are believed to be the result of effort minimisation [8, 9] – an important characteristic of motor learning [10].”
Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control. (2011, background): “In motor learning of arm reaching under novel force fields, changes in motor commands are huge for the first few trials, much more than the level of trajectory errors [78].”
Motor learning. (2010, background): “In terms of the interaction between stiffness and predictive control it has been proposed that when errors are large, stiffness increases but as the predictive component begins to learn, and errors reduce, stiffness decreases [14].”