Conditional entropies, phase synchronization and changes in the directionality of information flow in neural systems

Abstract

We devised a novel measure that dynamically evaluates temporal interdependences between two coupled units based on the properties of the distributions of their relative interevent intervals. We investigate its properties on the system of two coupled non-identical Rössler oscillators and a system of non-identical Hindmarsh–Rose models of thalamocortical neurons and show that the measure highlights the properties of phase synchronization observed in those two systems. We postulate that the observed properties of the phase lag, in conjunction with the experimentally observed activity-dependent synaptic modification in the neural systems, may drive the changes of the direction of information flow in a neural network, and thus the measure can play an important role in assessing those changes.