title: On the detection and measurement of synchrony in neural populations by coherence analysis.
abstract: This study considers the possibility of using coherence analysis for detection and measurement of synchrony (correlations) in large neural populations, applied to activities that are relatively easy to record in parallel. Mathematical analysis and computer simulations are used to examine the behavior of the coherence function between both unitary and population-aggregate activity (UTA coherence) and the aggregate activities of two populations (ATA coherence). The results indicate that for a large population showing partial correlations, the UTA coherence function is almost zero at all frequencies for the uncorrelated units. However, unless the synchrony is very restricted, its value is nonzero (i.e., statistically significant by common criteria) at each frequency of synchrony for the units that show correlations to other units. Moreover, this value is indicative of the strength of synchrony for any given unit. These properties enable the identification of the correlated units in a sample of unit/population activities simultaneously recorded in a series of experiments, and hence the detection of synchrony. The extent of synchrony can then be estimated as the fraction of such units in the sample, whereas the values of the UTA coherences in the sample can be used to estimate the strength and its distribution within the population. Similarly, the ATA coherence function is generally nonzero (significant) at the frequencies where there are correlations between members of two large populations. This enables the easy detection of such correlations from simultaneously recorded population activities. However, this function is a very sensitive index of synchrony and even shows saturation effects. It may therefore be used as a general measure of synchrony only under restricted conditions.
authors.affiliation: Department of Basic Sciences, Medical School, University of Crete, 71110 Heraklion, Greece.
authors.forename: C N
authors.initials: CN
authors.lastname: Christakos
mesh_terms: D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors