title: Neural spike train synchronization indices: definitions, interpretations, and applications.
abstract: A comparison of previously defined spike train syncrhonization indices is undertaken within a stochastic point process framework. The second-order cumulant density (covariance density) is shown to be common to all the indices. Simulation studies were used to investigate the sampling variability of a single index based on the second-order cumulant. The simulations used a paired motoneurone model and a paired regular spiking cortical neurone model. The sampling variability of spike trains generated under identical conditions from the paired motoneurone model varied from 50% to 160% of the estimated value. On theoretical grounds, and on the basis of simulated data a rate dependence is present in all synchronization indices. The application of coherence and pooled coherence estimates to the issue of synchronization indices is considered. This alternative frequency domain approach allows an arbitrary number of spike train pairs to be evaluated for statistically significant differences, and combined into a single population measure. The pooled coherence framework allows pooled time domain measures to be derived, application of this to the simulated data is illustrated. Data from the cortical neurone model is generated over a wide range of firing rates (1-250 spikes/s). The pooled coherence framework correctly characterizes the sampling variability as not significant over this wide operating range. The broader applicability of this approach to multielectrode array data is briefly discussed.
authors.affiliation: Department of Electronics, University of York, UK. dh20@ohm.york.ac.uk
authors.forename: David M
authors.initials: DM
authors.lastname: Halliday
authors.forename: J R
authors.initials: JR
authors.lastname: Rosenberg
authors.forename: P
authors.initials: P
authors.lastname: Breeze
authors.forename: B A
authors.initials: BA
authors.lastname: Conway
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D003344:Cortical Synchronization / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology*; D009046:Motor Neurons / Q000502:physiology*; D013223:Statistics as Topic; D009435:Synaptic Transmission / Q000502:physiology*