Non-parametric detection of temporal order across pairwise measurements of time delays

Neuronal synchronization is often associated with small time delays, and these delays can change as a function of stimulus properties. Investigation of time delays can be cumbersome if the activity of a large number of neurons is recorded simultaneously and neuronal synchronization is measured in a...

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Bibliographic Details
Published in:Journal of computational neuroscience 2007-02, Vol.22 (1), p.5-19
Main Author: Nikolic, Danko
Format: Article
Language:English
Subjects:
Algorithms
Animals
Cats
Cerebral Cortex - cytology
Cerebral Cortex - physiology
Consistency
Cortical Synchronization
Cross correlation
Data Interpretation, Statistical
Electrophysiology
Histograms
Models, Neurological
Models, Statistical
Neural Networks, Computer
Neurons - physiology
Nonparametric statistics
Photic Stimulation
Statistical tests
Statistics, Nonparametric
Synchronism
Synchronization
Temporal variations
Time measurement
Time synchronization
Timing
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Summary:Neuronal synchronization is often associated with small time delays, and these delays can change as a function of stimulus properties. Investigation of time delays can be cumbersome if the activity of a large number of neurons is recorded simultaneously and neuronal synchronization is measured in a pairwise manner (such as the cross-correlation histograms) because the number of pairwise measurements increases quadratically. Here, a non-parametric statistical test is proposed with which one can investigate (i) the consistency of the delays across a large number of pairwise measurements and (ii) the consistency of the changes in the time delays as a function of experimental conditions. The test can be classified as non-parametric because it takes into account only the directions of the delays and thus, does not make assumptions about the distributions and the variances of the measurement errors.
ISSN:0929-5313
1573-6873
DOI:10.1007/s10827-006-9441-7