Ontology type: schema:ScholarlyArticle
2009-06-18
AUTHORSMatthieu Gilson, Anthony N. Burkitt, David B. Grayden, Doreen A. Thomas, J. Leo van Hemmen
ABSTRACTSpike-timing-dependent plasticity (STDP) is believed to structure neuronal networks by slowly changing the strengths (or weights) of the synaptic connections between neurons depending upon their spiking activity, which in turn modifies the neuronal firing dynamics. In this paper, we investigate the change in synaptic weights induced by STDP in a recurrently connected network in which the input weights are plastic but the recurrent weights are fixed. The inputs are divided into two pools with identical constant firing rates and equal within-pool spike-time correlations, but with no between-pool correlations. Our analysis uses the Poisson neuron model in order to predict the evolution of the input synaptic weights and focuses on the asymptotic weight distribution that emerges due to STDP. The learning dynamics induces a symmetry breaking for the individual neurons, namely for sufficiently strong within-pool spike-time correlation each neuron specializes to one of the input pools. We show that the presence of fixed excitatory recurrent connections between neurons induces a group symmetry-breaking effect, in which neurons tend to specialize to the same input pool. Consequently STDP generates a functional structure on the input connections of the network. More... »
PAGES103-114
http://scigraph.springernature.com/pub.10.1007/s00422-009-0320-y
DOIhttp://dx.doi.org/10.1007/s00422-009-0320-y
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1037020006
PUBMEDhttps://www.ncbi.nlm.nih.gov/pubmed/19536559
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/02",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Physical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Information and Computing Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Psychology and Cognitive Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0299",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Other Physical Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Artificial Intelligence and Image Processing",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1702",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Cognitive Sciences",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Action Potentials",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Animals",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Computer Simulation",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Learning",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Models, Neurological",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Nerve Net",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Neural Networks, Computer",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Neuronal Plasticity",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Neurons",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Poisson Distribution",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Synapses",
"type": "DefinedTerm"
},
{
"inDefinedTermSet": "https://www.nlm.nih.gov/mesh/",
"name": "Synaptic Transmission",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia",
"id": "http://www.grid.ac/institutes/grid.425461.0",
"name": [
"Department of Electrical and Electronic Engineering, University of Melbourne, 3010, Melbourne, VIC, Australia",
"The Bionic Ear Institute, 384-388 Albert St, 3002, East Melbourne, VIC, Australia",
"NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia"
],
"type": "Organization"
},
"familyName": "Gilson",
"givenName": "Matthieu",
"id": "sg:person.01362642406.55",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01362642406.55"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia",
"id": "http://www.grid.ac/institutes/grid.425461.0",
"name": [
"Department of Electrical and Electronic Engineering, University of Melbourne, 3010, Melbourne, VIC, Australia",
"The Bionic Ear Institute, 384-388 Albert St, 3002, East Melbourne, VIC, Australia",
"NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia"
],
"type": "Organization"
},
"familyName": "Burkitt",
"givenName": "Anthony N.",
"id": "sg:person.0704026214.23",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0704026214.23"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia",
"id": "http://www.grid.ac/institutes/grid.425461.0",
"name": [
"Department of Electrical and Electronic Engineering, University of Melbourne, 3010, Melbourne, VIC, Australia",
"The Bionic Ear Institute, 384-388 Albert St, 3002, East Melbourne, VIC, Australia",
"NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia"
],
"type": "Organization"
},
"familyName": "Grayden",
"givenName": "David B.",
"id": "sg:person.01361317114.20",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01361317114.20"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia",
"id": "http://www.grid.ac/institutes/grid.425461.0",
"name": [
"Department of Electrical and Electronic Engineering, University of Melbourne, 3010, Melbourne, VIC, Australia",
"NICTA, Victoria Research Lab, 3010, Melbourne, VIC, Australia"
],
"type": "Organization"
},
"familyName": "Thomas",
"givenName": "Doreen A.",
"id": "sg:person.01220740562.10",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01220740562.10"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Physik Department (T35), BCCN Munich, Technische Universit\u00e4t M\u00fcnchen, 85747, Garching bei M\u00fcnchen, Germany",
"id": "http://www.grid.ac/institutes/grid.6936.a",
"name": [
"Physik Department (T35), BCCN Munich, Technische Universit\u00e4t M\u00fcnchen, 85747, Garching bei M\u00fcnchen, Germany"
],
"type": "Organization"
},
"familyName": "van Hemmen",
"givenName": "J. Leo",
"id": "sg:person.01066247102.81",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01066247102.81"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/s00422-005-0006-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1041719925",
"https://doi.org/10.1007/s00422-005-0006-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00422-007-0148-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1010529867",
"https://doi.org/10.1007/s00422-007-0148-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00422-008-0233-1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045495873",
"https://doi.org/10.1007/s00422-008-0233-1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/383076a0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013183215",
"https://doi.org/10.1038/383076a0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00337288",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009635558",
"https://doi.org/10.1007/bf00337288"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00422-009-0319-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034695944",
"https://doi.org/10.1007/s00422-009-0319-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/bf00288907",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020503948",
"https://doi.org/10.1007/bf00288907"
],
"type": "CreativeWork"
}
],
"datePublished": "2009-06-18",
"datePublishedReg": "2009-06-18",
"description": "Spike-timing-dependent plasticity (STDP) is believed to structure neuronal networks by slowly changing the strengths (or weights) of the synaptic connections between neurons depending upon their spiking activity, which in turn modifies the neuronal firing dynamics. In this paper, we investigate the change in synaptic weights induced by STDP in a recurrently connected network in which the input weights are plastic but the recurrent weights are fixed. The inputs are divided into two pools with identical constant firing rates and equal within-pool spike-time correlations, but with no between-pool correlations. Our analysis uses the Poisson neuron model in order to predict the evolution of the input synaptic weights and focuses on the asymptotic weight distribution that emerges due to STDP. The learning dynamics induces a symmetry breaking for the individual neurons, namely for sufficiently strong within-pool spike-time correlation each neuron specializes to one of the input pools. We show that the presence of fixed excitatory recurrent connections between neurons induces a group symmetry-breaking effect, in which neurons tend to specialize to the same input pool. Consequently STDP generates a functional structure on the input connections of the network.",
"genre": "article",
"id": "sg:pub.10.1007/s00422-009-0320-y",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1081741",
"issn": [
"0340-1200",
"1432-0770"
],
"name": "Biological Cybernetics",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "101"
}
],
"keywords": [
"neuronal networks",
"spike-time correlations",
"constant firing rate",
"synaptic connections",
"excitatory recurrent connections",
"firing rate",
"neurons",
"individual neurons",
"recurrent neuronal networks",
"dependent plasticity",
"firing dynamics",
"neuronal firing dynamics",
"asymptotic weight distribution",
"input pool",
"weight",
"Poisson neuron model",
"STDP",
"synaptic weights",
"correlation",
"plasticity",
"recurrent connections",
"pool",
"neuron model",
"activity",
"rate",
"effect",
"presence",
"changes",
"input connections",
"analysis",
"recurrent weights",
"connection",
"emergence",
"turn",
"model",
"distribution",
"functional structure",
"order",
"strength",
"input",
"weight distribution",
"network",
"connected network",
"structure",
"dynamics",
"evolution",
"paper",
"input weights",
"network structure",
"symmetry",
"breaking",
"symmetry-breaking effects"
],
"name": "Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. II. Input selectivity\u2014symmetry breaking",
"pagination": "103-114",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1037020006"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00422-009-0320-y"
]
},
{
"name": "pubmed_id",
"type": "PropertyValue",
"value": [
"19536559"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00422-009-0320-y",
"https://app.dimensions.ai/details/publication/pub.1037020006"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:25",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_477.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00422-009-0320-y"
}
]Download the RDF metadata as: json-ld nt turtle xml License info
JSON-LD is a popular format for linked data which is fully compatible with JSON.
curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s00422-009-0320-y'
N-Triples is a line-based linked data format ideal for batch operations.
curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s00422-009-0320-y'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00422-009-0320-y'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00422-009-0320-y'
This table displays all metadata directly associated to this object as RDF triples.
239 TRIPLES
22 PREDICATES
101 URIs
82 LITERALS
19 BLANK NODES