Developments in understanding neuronal spike trains and functional specializations in brain regions.
Identifieur interne : 000201 ( Ncbi/Curation ); précédent : 000200; suivant : 000202Developments in understanding neuronal spike trains and functional specializations in brain regions.
Auteurs : Roberto A. Santiago [États-Unis] ; James Mcnames ; Kim Burchiel ; George G. LendarisSource :
- Neural networks : the official journal of the International Neural Network Society [ 0893-6080 ]
Descripteurs français
- KwdFr :
- MESH :
- physiologie : Encéphale, Neurones, Potentiels d'action.
- Humains.
English descriptors
- KwdEn :
- MESH :
- physiology : Action Potentials, Brain, Neurons.
- Humans.
Abstract
Understanding information processing at the neuronal level would provide valuable insights to computational intelligence research and computational neuroscience. In particular, understanding constraints on neuronal spike trains would provide indication about the type of syntactic rules used by neurons when processing information. A recent discovery, reported here, was made through analyzing microelectrode recordings (MER) made during surgical procedure in humans. Analysis of MERs of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulators, done in the treatment of advanced Parkinson's disease. Important to these procedures is the identification of different brain structures such as the globus pallidus internus from the spike train being recorded from the intracranial probe tip during surgery. Spike train data gathered during surgical procedure from multiple patients were processed using a novel feature extraction method reported here. Distinct structures within the spike trains were identified and used to build an effective brain region classifier. The extracted features upon analysis provide some insight into the 'syntactic' constraint on spike trains.
DOI: 10.1016/S0893-6080(03)00123-0
PubMed: 12850013
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pubmed:12850013Le document en format XML
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Santiago</name><affiliation wicri:level="1"><nlm:affiliation>NW Computational Intelligence Laboratory, System Science, Portland State University, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>NW Computational Intelligence Laboratory, System Science, Portland State University</wicri:regionArea><wicri:noRegion>Portland State University</wicri:noRegion></affiliation></author><author><name sortKey="Mcnames, James" sort="Mcnames, James" uniqKey="Mcnames J" first="James" last="Mcnames">James Mcnames</name></author><author><name sortKey="Burchiel, Kim" sort="Burchiel, Kim" uniqKey="Burchiel K" first="Kim" last="Burchiel">Kim Burchiel</name></author><author><name sortKey="Lendaris, George G" sort="Lendaris, George G" uniqKey="Lendaris G" first="George G" last="Lendaris">George G. Lendaris</name></author></analytic><series><title level="j">Neural networks : the official journal of the International Neural Network Society</title><idno type="ISSN">0893-6080</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Action Potentials (physiology)</term><term>Brain (physiology)</term><term>Humans</term><term>Neurons (physiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Encéphale (physiologie)</term><term>Humains</term><term>Neurones (physiologie)</term><term>Potentiels d'action (physiologie)</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Encéphale</term><term>Neurones</term><term>Potentiels d'action</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Action Potentials</term><term>Brain</term><term>Neurons</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Humans</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Humains</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Understanding information processing at the neuronal level would provide valuable insights to computational intelligence research and computational neuroscience. In particular, understanding constraints on neuronal spike trains would provide indication about the type of syntactic rules used by neurons when processing information. A recent discovery, reported here, was made through analyzing microelectrode recordings (MER) made during surgical procedure in humans. Analysis of MERs of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulators, done in the treatment of advanced Parkinson's disease. Important to these procedures is the identification of different brain structures such as the globus pallidus internus from the spike train being recorded from the intracranial probe tip during surgery. Spike train data gathered during surgical procedure from multiple patients were processed using a novel feature extraction method reported here. Distinct structures within the spike trains were identified and used to build an effective brain region classifier. The extracted features upon analysis provide some insight into the 'syntactic' constraint on spike trains.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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