Computational neurorehabilitation: modeling plasticity and learning to predict recovery
Identifieur interne : 000919 ( Pmc/Corpus ); précédent : 000918; suivant : 000920Computational neurorehabilitation: modeling plasticity and learning to predict recovery
Auteurs : David J. Reinkensmeyer ; Etienne Burdet ; Maura Casadio ; John W. Krakauer ; Gert Kwakkel ; Catherine E. Lang ; Stephan P. Swinnen ; Nick S. Ward ; Nicolas SchweighoferSource :
- Journal of NeuroEngineering and Rehabilitation [ 1743-0003 ] ; 2016.
Abstract
Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss
Url:
DOI: 10.1186/s12984-016-0148-3
PubMed: 27130577
PubMed Central: 4851823
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