Linked Life Data

19191596

Source:http://linkedlifedata.com/resource/pubmed/id/19191596

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2009-5-21
pubmed:abstractText
Emerging evidence indicates that information processing, as well as learning and memory processes, in both the network and single-neuron levels are highly dependent on the correlation structure of multiple spike trains. Contemporary experimental as well as theoretical studies that involve quasi-realistic neuronal stimulation thus require a method for controlling spike train correlations. This letter introduces a general new strategy for generating multiple spike trains with exactly controlled mean firing rates and correlation structure (defined in terms of auto- and cross-correlation functions). Our approach nonlinearly transforms random gaussian-distributed processes with a predistorted correlation structure into nonnegative rate processes, which are then used to generate doubly stochastic Poisson point processes with the required correlation structure. We show how this approach can be used to generate stationary or nonstationary spike trains from small or large groups of neurons with diverse auto- and cross-correlation structures. We analyze and derive analytical formulas for the high-order correlation structure of generated spike trains and discuss the limitations of this approach.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0899-7667
pubmed:author
pubmed:issnType
Print
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1642-64
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Generation of spike trains with controlled auto- and cross-correlation functions.
pubmed:affiliation
Faculty of Bio-Medical Engineering, Technion IIT, 32000 Haifa, Israel. miha@bm.technion.ac.il
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't