19670479

Source:http://linkedlifedata.com/resource/pubmed/id/19670479

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0038215, umls-concept:C0470166, umls-concept:C1522240, umls-concept:C1552961
pubmed:issue	8
pubmed:dateCreated	2009-8-10
pubmed:abstractText	We present a novel approach to quantify the statistical interdependence of two time series, referred to as stochastic event synchrony (SES). The first step is to extract the two given time series. The next step is to try to align events from one time series with events from the other. The better the alignment the more similar the two series are considered to be. More precisely, the similarity is quantified by the following parameters: time delay, variance of the time jitter, fraction of noncoincident events, and average similarity of the aligned events. The pairwise alignment and SES parameters are determined by statistical inference. In particular, the SES parameters are computed by maximum a posteriori (MAP) estimation, and the pairwise alignment is obtained by applying the max product algorithm. This letter deals with one-dimensional point processes; the extension to multidimensional point processes is considered in a companion letter in this issue. By analyzing surrogate data, we demonstrate that SES is able to quantify both timing precision and event reliability more robustly than classical measures can. As an illustration, neuronal spike data generated by Morris-Lecar neuron model are considered.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/9426182
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:month	Aug
pubmed:issn	0899-7667
pubmed:author	pubmed-author:CichockiAA, pubmed-author:DauwelsJJ, pubmed-author:VialatteFF, pubmed-author:WeberTT
pubmed:issnType	Print
pubmed:volume	21
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	2152-202
pubmed:meshHeading	pubmed-meshheading:19670479-Action Potentials, pubmed-meshheading:19670479-Algorithms, pubmed-meshheading:19670479-Models, Neurological, pubmed-meshheading:19670479-Models, Statistical, pubmed-meshheading:19670479-Neurons, pubmed-meshheading:19670479-Time Factors
pubmed:year	2009
pubmed:articleTitle	Quantifying statistical interdependence by message passing on graphs-part I: one-dimensional point processes.
pubmed:affiliation	Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. jdauwels@mit.edu
pubmed:publicationType	Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't