Currently there are many methods to detect synchroniza- tion, each of them trying to extract some specific aspects or oriented to specific number or type of signals. In this paper, we present a new method to detect synchronization for multivariate signals, computation- ally light and not requiring a combinatorial number of operations on signals differences.The method is based on the Hilbert transform of the signals, which provides their instantaneous phases. The distribution of phases for all signals at a specific time is assimilated to a probability dis- tribution. In this way, we obtain a sequence of probability distributions (one per time unit). Computing the entropy of the probability distribu- tions we get finally a function of entropies along time. The average value of this final function provides a good estimate of the synchronization level of the multivariate signals ensemble, and the function itself can be used as a signature (descriptive function) of the whole multidimensional ensemble dynamics.
['Manuel Recuero López', 'Francisco de Borja Rodríguez']
Detection Method for Phase Synchronization in a Population of Spiking Neurons