A Flexible Method to Measure Synchrony in Neuronal Firing

Neurons can transmit information about the characteristics of a stimulus through the spike rate of neurons and synchronization of the neurons. Various association measure can be used to describe how “synchronous” two spike trains are. We propose a new measure of synchrony, the conditional synchrony measure, which is the probability of firing together given that at least one of the two neurons is active. Focus is on the specification of a flexible marginal model for multivariate correlated binary data together with a pseudolikelihood estimation approach, to adequately and directly describe the measures of interest. A joint model must allow different time- and covariate-dependent firing rates for each neuron, and also must account for the association between them. The association between neurons might depend on covariates as well.