A Novel Measure for Synchrony and its Application to Neural Signals

A novel measure to quantify the synchrony between two sparse binary strings is proposed, referred to as "stochastic event synchrony" (SES). It is computed by performing inference in a probabilistic model. SES can amongst other be used to detect synchrony in neural signals, in particular, spike trains (obtained from electrophysiological recordings) and EEG signals. It is demonstrated how SES can quantify the firing reliability of a neuron. It is also shown how SES can be used as a feature to detect Alzheimer\´s disease based on EEG signals.