{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1363670319733428480.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1162/089976603321891783"}},{"identifier":{"@type":"URI","@value":"https://www.mitpressjournals.org/doi/pdf/10.1162/089976603321891783"}}],"dc:title":[{"@value":"Relating STDP to BCM"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p> We demonstrate that the BCM learning rule follows directly from STDP when pre- and postsynaptic neurons fire uncorrelated or weakly correlated Poisson spike trains, and only nearest-neighbor spike interactions are taken into account. </jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1383670319733428481","@type":"Researcher","foaf:name":[{"@value":"Eugene M. Izhikevich"}],"jpcoar:affiliationName":[{"@value":"The Neurosciences Institute, San Diego, CA, 92121, U.S.A., ,"}]},{"@id":"https://cir.nii.ac.jp/crid/1383670319733428480","@type":"Researcher","foaf:name":[{"@value":"Niraj S. Desai"}],"jpcoar:affiliationName":[{"@value":"The Neurosciences Institute, San Diego, CA, 92121, U.S.A.,"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"08997667"},{"@type":"EISSN","@value":"1530888X"}],"prism:publicationName":[{"@value":"Neural Computation"}],"dc:publisher":[{"@value":"MIT Press - Journals"}],"prism:publicationDate":"2003-07-01","prism:volume":"15","prism:number":"7","prism:startingPage":"1511","prism:endingPage":"1523"},"reviewed":"false","url":[{"@id":"https://www.mitpressjournals.org/doi/pdf/10.1162/089976603321891783"}],"createdAt":"2003-06-03","modifiedAt":"2021-03-12","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050853334077969536","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Multicoding in neural information transfer suggested by mathematical analysis of the frequency‑dependent synaptic plasticity in vivo"}]},{"@id":"https://cir.nii.ac.jp/crid/1360004237451252480","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Spectral Analysis of Input Spike Trains by Spike-Timing-Dependent Plasticity"}]},{"@id":"https://cir.nii.ac.jp/crid/1360004240545578880","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Recurrent network model for learning goal-directed sequences through reverse replay"}]},{"@id":"https://cir.nii.ac.jp/crid/1360290617563522944","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"A review on neural network models of schizophrenia and autism spectrum disorder"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1162/089976603321891783"},{"@type":"CROSSREF","@value":"10.1371/journal.pcbi.1002584_references_DOI_j63l4jS7A2WkL9nBaRFEl2yrgj"},{"@type":"CROSSREF","@value":"10.7554/elife.34171_references_DOI_j63l4jS7A2WkL9nBaRFEl2yrgj"},{"@type":"CROSSREF","@value":"10.1016/j.neunet.2019.10.014_references_DOI_j63l4jS7A2WkL9nBaRFEl2yrgj"},{"@type":"CROSSREF","@value":"10.1038/s41598-020-70876-4_references_DOI_j63l4jS7A2WkL9nBaRFEl2yrgj"}]}