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 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.
Nikolić
Danko
D
Max-Planck-Institute for Brain Research, Deutschordenstr. 46, D-60528, Frankfurt am Main, Germany. danko@mpih-frankfurt.mpg.de
D000465:Algorithms; D000818:Animals; D002415:Cats; D002540:Cerebral Cortex / Q000166:cytology / Q000502:physiology*; D003344:Cortical Synchronization; D003627:Data Interpretation, Statistical; D004594:Electrophysiology; D008959:Models, Neurological; D015233:Models, Statistical; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D018709:Statistics, Nonparametric