21231357

Source:http://linkedlifedata.com/resource/pubmed/id/21231357

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0599851, umls-concept:C0871935
pubmed:issue	21
pubmed:dateCreated	2011-1-14
pubmed:abstractText	We study the computational capacity of a model neuron, the tempotron, which classifies sequences of spikes by linear-threshold operations. We use statistical mechanics and extreme value theory to derive the capacity of the system in random classification tasks. In contrast with its static analog, the perceptron, the tempotron's solutions space consists of a large number of small clusters of weight vectors. The capacity of the system per synapse is finite in the large size limit and weakly diverges with the stimulus duration relative to the membrane and synaptic time constants.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0401141
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Nov
pubmed:issn	1079-7114
pubmed:author	pubmed-author:MonassonRémiR, pubmed-author:RubinRanR, pubmed-author:SompolinskyHaimH
pubmed:issnType	Electronic
pubmed:day	19
pubmed:volume	105
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	218102
pubmed:meshHeading	pubmed-meshheading:21231357-Action Potentials, pubmed-meshheading:21231357-Electricity, pubmed-meshheading:21231357-Models, Neurological, pubmed-meshheading:21231357-Neurons, pubmed-meshheading:21231357-Time Factors
pubmed:year	2010
pubmed:articleTitle	Theory of spike timing-based neural classifiers.
pubmed:affiliation	Racah Institute of Physics, Hebrew University, 91904 Jerusalem, Israel.
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't