The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm
Open accessAbstract
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.
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Cites
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Self-citationThe central nervous system stabilizes unstable dynamics by learning optimal impedance
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001) · Nature · doi:10.1038/35106566 · Cited by 1,097
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Cited by
Principles of sensorimotor learning
Wolpert, D. M., Diedrichsen, J., & Flanagan, J. R. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3112 · Cited by 1,391
Progress and prospects of the human–robot collaboration
Ajoudani, A., Zanchettin, A. M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., & Khatib, O. (2018) · Autonomous Robots · doi:10.1007/s10514-017-9677-2 · Cited by 832
Model-Based and Model-Free Mechanisms of Human Motor Learning
Haith, A. M., & Krakauer, J. W. (2013) · Advances in Experimental Medicine and Biology · doi:10.1007/978-1-4614-5465-6_1 · Cited by 263
Internal models in biological control
McNamee, D., & Wolpert, D. M. (2019) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105206 · Cited by 225
Milot, M.-H., Marchal-Crespo, L., Green, C. S., Cramer, S. C., & Reinkensmeyer, D. J. (2010) · Experimental Brain Research · doi:10.1007/s00221-009-2014-z · Cited by 139
Metadata
- Authors
- Franklin, D. W., Burdet, E., Peng Tee, K., Osu, R., Chew, C.-M., Milner, T. E., & Kawato, M.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2008-10-29
- Volume
- 28
- Issue
- 44
- Pages
- 11165-11173
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2030268430
- Cited by
- 324
- References
- 54
- FWCI
- 8.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Franklin, D. W., Burdet, E., Peng Tee, K., Osu, R., Chew, C.-M., Milner, T. E., & Kawato, M. (2008). CNS Learns Stable, Accurate, and Efficient Movements Using a Simple Algorithm. The Journal of Neuroscience. https://doi.org/10.1523/jneurosci.3099-08.2008
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
