Computational models of the basal ganglia.
Identifieur interne : 003F27 ( PubMed/Corpus ); précédent : 003F26; suivant : 003F28Computational models of the basal ganglia.
Auteurs : A. Gillies ; G. ArbuthnottSource :
- Movement disorders : official journal of the Movement Disorder Society [ 0885-3185 ] ; 2000.
English descriptors
- KwdEn :
- Animals, Basal Ganglia (physiology), Basal Ganglia (physiopathology), Brain (physiology), Computer Simulation, Dopamine (metabolism), Dopamine (physiology), Humans, Inhibition (Psychology), Learning (physiology), Models, Neurological, Nerve Net, Parkinson Disease (physiopathology), Reinforcement (Psychology).
- MESH :
- chemical , metabolism : Dopamine.
- physiology : Basal Ganglia, Brain, Dopamine, Learning.
- physiopathology : Basal Ganglia, Parkinson Disease.
- Animals, Computer Simulation, Humans, Inhibition (Psychology), Models, Neurological, Nerve Net, Reinforcement (Psychology).
Abstract
Computer simulation studies and mathematical analysis of models of the basal ganglia are being used increasingly to explore theories of basal ganglia function. We review the implications of these new models for a general understanding of basal ganglia function in normal as well as in diseased brains. The focus is on their functional similarities rather than on the details of mathematical methodologies and simulation techniques. Most of the models suggest a vital role for the basal ganglia in learning. Although this interest in learning is partly driven by experimental results associating the acute firing of dopamine cells with reward prediction in monkeys, some of the models have preceded the electrophysiological results. Another common theme of the models is selection. In this case, the striatum is seen as detecting and selecting cortical contexts for access to basal ganglia output. Although the behavioral consequences of this selection are hard to define, the models provide frameworks within which to explore these ideas empirically. This provides a means of refining our understanding of basal ganglia function and to consider dysfunction within the new logical frameworks.
PubMed: 11009178
Links to Exploration step
pubmed:11009178Le document en format XML
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Gillies</name><affiliation><nlm:affiliation>Institute for Adaptive and Neural Computation, The University of Edinburgh, UK.</nlm:affiliation></affiliation></author><author><name sortKey="Arbuthnott, G" sort="Arbuthnott, G" uniqKey="Arbuthnott G" first="G" last="Arbuthnott">G. Arbuthnott</name></author></analytic><series><title level="j">Movement disorders : official journal of the Movement Disorder Society</title><idno type="ISSN">0885-3185</idno><imprint><date when="2000" type="published">2000</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>Basal Ganglia (physiology)</term><term>Basal Ganglia (physiopathology)</term><term>Brain (physiology)</term><term>Computer Simulation</term><term>Dopamine (metabolism)</term><term>Dopamine (physiology)</term><term>Humans</term><term>Inhibition (Psychology)</term><term>Learning (physiology)</term><term>Models, Neurological</term><term>Nerve Net</term><term>Parkinson Disease (physiopathology)</term><term>Reinforcement (Psychology)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Dopamine</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Basal Ganglia</term><term>Brain</term><term>Dopamine</term><term>Learning</term></keywords><keywords scheme="MESH" qualifier="physiopathology" xml:lang="en"><term>Basal Ganglia</term><term>Parkinson Disease</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Computer Simulation</term><term>Humans</term><term>Inhibition (Psychology)</term><term>Models, Neurological</term><term>Nerve Net</term><term>Reinforcement (Psychology)</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Computer simulation studies and mathematical analysis of models of the basal ganglia are being used increasingly to explore theories of basal ganglia function. We review the implications of these new models for a general understanding of basal ganglia function in normal as well as in diseased brains. The focus is on their functional similarities rather than on the details of mathematical methodologies and simulation techniques. Most of the models suggest a vital role for the basal ganglia in learning. Although this interest in learning is partly driven by experimental results associating the acute firing of dopamine cells with reward prediction in monkeys, some of the models have preceded the electrophysiological results. Another common theme of the models is selection. In this case, the striatum is seen as detecting and selecting cortical contexts for access to basal ganglia output. Although the behavioral consequences of this selection are hard to define, the models provide frameworks within which to explore these ideas empirically. This provides a means of refining our understanding of basal ganglia function and to consider dysfunction within the new logical frameworks.</div></front></TEI><pubmed><MedlineCitation Owner="NLM" Status="MEDLINE"><PMID Version="1">11009178</PMID><DateCreated><Year>2001</Year><Month>01</Month><Day>09</Day></DateCreated><DateCompleted><Year>2001</Year><Month>01</Month><Day>09</Day></DateCompleted><DateRevised><Year>2013</Year><Month>11</Month><Day>21</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0885-3185</ISSN><JournalIssue CitedMedium="Print"><Volume>15</Volume><Issue>5</Issue><PubDate><Year>2000</Year><Month>Sep</Month></PubDate></JournalIssue><Title>Movement disorders : official journal of the Movement Disorder Society</Title><ISOAbbreviation>Mov. Disord.</ISOAbbreviation></Journal><ArticleTitle>Computational models of the basal ganglia.</ArticleTitle><Pagination><MedlinePgn>762-70</MedlinePgn></Pagination><Abstract><AbstractText>Computer simulation studies and mathematical analysis of models of the basal ganglia are being used increasingly to explore theories of basal ganglia function. We review the implications of these new models for a general understanding of basal ganglia function in normal as well as in diseased brains. The focus is on their functional similarities rather than on the details of mathematical methodologies and simulation techniques. Most of the models suggest a vital role for the basal ganglia in learning. Although this interest in learning is partly driven by experimental results associating the acute firing of dopamine cells with reward prediction in monkeys, some of the models have preceded the electrophysiological results. Another common theme of the models is selection. In this case, the striatum is seen as detecting and selecting cortical contexts for access to basal ganglia output. Although the behavioral consequences of this selection are hard to define, the models provide frameworks within which to explore these ideas empirically. This provides a means of refining our understanding of basal ganglia function and to consider dysfunction within the new logical frameworks.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gillies</LastName><ForeName>A</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Institute for Adaptive and Neural Computation, The University of Edinburgh, UK.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Arbuthnott</LastName><ForeName>G</ForeName><Initials>G</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D016454">Review</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>UNITED STATES</Country><MedlineTA>Mov Disord</MedlineTA><NlmUniqueID>8610688</NlmUniqueID><ISSNLinking>0885-3185</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>VTD58H1Z2X</RegistryNumber><NameOfSubstance UI="D004298">Dopamine</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName MajorTopicYN="N" UI="D000818">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001479">Basal Ganglia</DescriptorName><QualifierName MajorTopicYN="Y" UI="Q000502">physiology</QualifierName><QualifierName MajorTopicYN="N" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D001921">Brain</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D003198">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D004298">Dopamine</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000378">metabolism</QualifierName><QualifierName MajorTopicYN="Y" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D006801">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D007266">Inhibition (Psychology)</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D007858">Learning</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000502">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D008959">Models, Neurological</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D009415">Nerve Net</DescriptorName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="N" UI="D010300">Parkinson Disease</DescriptorName><QualifierName MajorTopicYN="N" UI="Q000503">physiopathology</QualifierName></MeshHeading><MeshHeading><DescriptorName MajorTopicYN="Y" UI="D012054">Reinforcement (Psychology)</DescriptorName></MeshHeading></MeshHeadingList><NumberOfReferences>43</NumberOfReferences></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2000</Year><Month>9</Month><Day>29</Day><Hour>11</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2001</Year><Month>2</Month><Day>28</Day><Hour>10</Hour><Minute>1</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2000</Year><Month>9</Month><Day>29</Day><Hour>11</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">11009178</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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