Linked Life Data

17271543

Source:http://linkedlifedata.com/resource/pubmed/id/17271543

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-2-2
pubmed:abstractText
Implementation of brain-machine interface neural-to-motor mapping algorithms in low-power, portable digital signal processors (DSPs) requires efficient use of model resources especially when predicting signals that show interdependencies. We show here that a single recurrent neural network can simultaneously predict hand position and velocity from the same ensemble of cells using a minimalist topology. Analysis of the trained topology showed that the model learns to concurrently represent multiple kinematic parameters in a single state variable. We further assess the expressive power of the state variables for both large and small topologies.
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
5321-4
pubmed:year
2004
pubmed:articleTitle
Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network.
pubmed:affiliation
Department of Biomedical Engineering, Florida University, Gainesville, FL, USA.
pubmed:publicationType
Journal Article