Linked Life Data

16907631

Source:http://linkedlifedata.com/resource/pubmed/id/16907631

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
10
pubmed:dateCreated
2006-8-15
pubmed:abstractText
The analysis of neuronal information involves the detection of spatiotemporal relations between neuronal discharges. We propose a method that is based on the positions (phase offsets) of the central peaks obtained from pairwise cross-correlation histograms. Data complexity is reduced to a one-dimensional representation by using redundancies in the measured phase offsets such that each unit is assigned a "preferred firing time" relative to the other units in the group. We propose two procedures to examine the applicability of this method to experimental data sets. In addition, we propose methods that help the investigation of dynamical changes in the preferred firing times of the units. All methods are applied to a sample data set obtained from cat visual cortex.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0899-7667
pubmed:author
pubmed:issnType
Print
pubmed:volume
18
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2387-413
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Spatiotemporal structure in large neuronal networks detected from cross-correlation.
pubmed:affiliation
Department of Computer Science and Mathematics, Johann Wolfgang Goethe University, Frankfurt (Main), Germany. gaby.schneider@math.uni-frankfurt.de
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't