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

J Comput Neurosci. 2007 Feb;22(1):5-19. doi: 10.1007/s10827-006-9441-7. Epub 2006 Sep 19.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Cats
  • Cerebral Cortex / cytology
  • Cerebral Cortex / physiology*
  • Cortical Synchronization
  • Data Interpretation, Statistical
  • Electrophysiology
  • Models, Neurological*
  • Models, Statistical
  • Neural Networks, Computer
  • Neurons / physiology*
  • Photic Stimulation
  • Statistics, Nonparametric