15963534

Source:http://linkedlifedata.com/resource/pubmed/id/15963534

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0025664, umls-concept:C0205246, umls-concept:C0599851, umls-concept:C0870071, umls-concept:C1708517
pubmed:issue	1
pubmed:dateCreated	2005-7-19
pubmed:abstractText	This paper presents a general methodological framework for the practical modeling of neural systems with point-process inputs (sequences of action potentials or, more broadly, identical events) based on the Volterra and Wiener theories of functional expansions and system identification. The paper clarifies the distinctions between Volterra and Wiener kernels obtained from Poisson point-process inputs. It shows that only the Wiener kernels can be estimated via cross-correlation, but must be defined as zero along the diagonals. The Volterra kernels can be estimated far more accurately (and from shorter data-records) by use of the Laguerre expansion technique adapted to point-process inputs, and they are independent of the mean rate of stimulation (unlike their P-W counterparts that depend on it). The Volterra kernels can also be estimated for broadband point-process inputs that are not Poisson. Useful applications of this modeling approach include cases where we seek to determine (model) the transfer characteristics between one neuronal axon (a point-process 'input') and another axon (a point-process 'output') or some other measure of neuronal activity (a continuous 'output', such as population activity) with which a causal link exists.
pubmed:grant	http://linkedlifedata.com/resource/pubmed/grant/01861
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/0103146
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Jul
pubmed:issn	0025-5564
pubmed:author	pubmed-author:BergerT WTW, pubmed-author:MarmarelisV ZVZ
pubmed:issnType	Print
pubmed:volume	196
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	1-13
pubmed:dateRevised	2009-11-11
pubmed:meshHeading	pubmed-meshheading:15963534-Action Potentials, pubmed-meshheading:15963534-Mathematics, pubmed-meshheading:15963534-Models, Neurological, pubmed-meshheading:15963534-Nonlinear Dynamics, pubmed-meshheading:15963534-Poisson Distribution
pubmed:year	2005
pubmed:articleTitle	General methodology for nonlinear modeling of neural systems with Poisson point-process inputs.
pubmed:affiliation	University of Southern California, Biomedical Engineering, Olin Hall 500, Los Angeles, CA 90089-1415, USA. marmarelis@hotmail.com
pubmed:publicationType	Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, N.I.H., Extramural