Communication through coherence in a realistic neuronal model

Abstract

The Communication Through Coherence (CTC) theory establishes that neural communication is much effective if the underlying oscillatory activity of both populations are phase locked, that is, the input from the emitting population arrives at the peak of excitability of the receiving neural network. To study this setting, we consider a novel population rate model, which provides an exact description of the macroscopic activity of a network, and perturb it with a periodic function, modelling the input. We study analytical and numerically the emerging phase-locked states using tools from dynamical systems.