Hysteresis bifurcation and application to delayed Fitzhugh-Nagumo neural systems

TL;DR

This paper investigates hysteresis phenomena in dynamical systems, especially in delayed FitzHugh-Nagumo neural models, using bifurcation theory and multiple scales analysis to classify behaviors and derive analytical normal forms.

Contribution

It provides a mathematical framework for understanding hysteresis via bifurcation analysis and extends the approach to neural systems with delays, linking hysteresis to bursting activity.

Findings

Hysteresis arises from subcritical Hopf and saddle-node bifurcations.

The normal form up to fifth order accurately predicts hysteretic dynamics.

The framework applies broadly to neural systems and bursting oscillations.

Abstract

Hysteresis dynamics has been described in a vast number of biological experimental studies. Many such studies are phenomenological and a mathematical appreciation has not attracted enough attention. In the paper, we explore the nature of hysteresis and study it from the dynamical system point of view by using the bifurcation and perturbation theories. We firstly make a classification of hysteresis according to the system behaviours transiting between different types of attractors. Then, we focus on a mathematically amenable situation where hysteretic movements between the equilibrium point and the limit cycle are initiated by a subcritical Hopf bifurcation and a saddle-node bifurcation of limit cycles. We present a analytical framework by using the method of multiple scales to obtain the normal form up to the fifth order. Theoretical results are compared with time domain simulations and numerical continuation, showing good agreement. Although we consider the time-delayed FitzHugh-Nagumo neural system in the paper, the generalization should be clear to other systems or parameters. The general framework we present in the paper can be naturally extended to the notion of bursting activity in neuroscience where hysteresis is a dominant mechanism to generate bursting oscillations.