Linked Life Data

21763517

Source:http://linkedlifedata.com/resource/pubmed/id/21763517

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2011-7-18
pubmed:abstractText
Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1875-7855
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 Elsevier B.V. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
192
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
33-58
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
A dynamical systems view of motor preparation: implications for neural prosthetic system design.
pubmed:affiliation
Department of Electrical Engineering, Stanford University, Stanford, California, USA. shenoy@stanford.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Review, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural