Co-evolutionary dynamics for two adaptively coupled Theta neurons

TL;DR

This paper investigates how adaptive coupling influences the collective dynamics of two Theta neurons, revealing bifurcation, mode-locking, and chaos, thereby advancing understanding of neuronal synchronization.

Contribution

It provides a bifurcation analysis of adaptive coupling effects in a simple neuronal network, highlighting stability regions, mode-locking, and chaos emergence.

Findings

Adaptive coupling widens Arnol'd tongues, enabling multi-stability.

Increased adaptivity can lead to period-doubling and chaos.

Bifurcation analysis maps stability regions of quiescence and spiking.

Abstract

Natural and technological networks exhibit dynamics that can lead to complex cooperative behaviors, such as synchronization in coupled oscillators and rhythmic activity in neuronal networks. Understanding these collective dynamics is crucial for deciphering a range of phenomena from brain activity to power grid stability. Recent interest in co-evolutionary networks has highlighted the intricate interplay between dynamics on and of the network with mixed time scales. Here, we explore the collective behavior of excitable oscillators in a simple networks of two Theta neurons with adaptive coupling without self-interaction. Through a combination of bifurcation analysis and numerical simulations, we seek to understand how the level of adaptivity in the coupling strength, $a$, influences the dynamics. We first investigate the dynamics possible in the non-adaptive limit; our bifurcation analysis reveals stability regions of quiescence and spiking behaviors, where the spiking frequencies mode-lock in a variety of configurations. Second, as we increase the adaptivity $a$, we observe a widening of the associated Arnol'd tongues, which may overlap and give room for multi-stable configurations. For larger adaptivity, the mode-locked regions may further undergo a period-doubling cascade into chaos. Our findings contribute to the mathematical theory of adaptive networks and offer insights into the potential mechanisms underlying neuronal communication and synchronization.