The Utility of Phase Models in Studying Neural Synchronization

TL;DR

This paper reviews how phase models, especially phase response curves, help predict neural synchronization outcomes in coupled oscillators, providing insights into the dynamics of neural networks.

Contribution

It demonstrates the utility of phase models in predicting synchronization and compares convergence rates for different classes of coupled neural oscillators.

Findings

Phase response curves predict synchronization outcomes.

Class I and Class II oscillators show different convergence rates.

Synchronization predictions apply to inhibitory and excitatory coupling.

Abstract

Synchronized neural spiking is associated with many cognitive functions and thus, merits study for its own sake. The analysis of neural synchronization naturally leads to the study of repetitive spiking and consequently to the analysis of coupled neural oscillators. Coupled oscillator theory thus informs the synchronization of spiking neuronal networks. A crucial aspect of coupled oscillator theory is the phase response curve (PRC), which describes the impact of a perturbation to the phase of an oscillator. In neural terms, the perturbation represents an incoming synaptic potential which may either advance or retard the timing of the next spike. The phase response curves and the form of coupling between reciprocally coupled oscillators defines the phase interaction function, which in turn predicts the synchronization outcome (in-phase versus anti-phase) and the rate of convergence. We review the two classes of PRC and demonstrate the utility of the phase model in predicting synchronization in reciprocally coupled neural models. In addition, we compare the rate of convergence for all combinations of reciprocally coupled Class I and Class II oscillators. These findings predict the general synchronization outcomes of broad classes of neurons under both inhibitory and excitatory reciprocal coupling.