Bifurcation analysis of a two-neuron central pattern generator model for both oscillatory and convergent neuronal activities

TL;DR

This paper analyzes the bifurcation behavior of a two-neuron Matsuoka oscillator model, revealing mechanisms behind oscillations, fixed points, and bifurcations, with implications for understanding neuronal activity patterns.

Contribution

It provides a detailed fixed point and bifurcation analysis of a two-neuron oscillator model, explaining oscillation mechanisms and predicting scaling laws and noise effects.

Findings

Identified bifurcation types including Hopf-like and homoclinic bifurcations.

Predicted logarithmic oscillation-period scaling law.

Observed noise-induced oscillations near bifurcations.

Abstract

The neural oscillator model proposed by Matsuoka is a piecewise affine system, which exhibits distinctive periodic solutions. Although such typical oscillation patterns have been widely studied, little is understood about the dynamics of convergence to certain fixed points and bifurcations between the periodic orbits and fixed points in this model. We performed fixed point analysis on a two-neuron version of the Matsuoka oscillator model, the result of which explains the mechanism of oscillation and the discontinuity-induced bifurcations such as subcritical/supercritical Hopf-like, homoclinic-like, and grazing bifurcations. Furthermore, it provided theoretical predictions concerning a logarithmic oscillation-period scaling law and noise-induced oscillations, which are both observed around those bifurcations. These results are expected to underpin further investigations into both oscillatory and transient neuronal activities with respect to central pattern generators.