Time series analysis of coupled slow-fast neuron models: From Hurst exponent to Granger causality

TL;DR

This paper analyzes small networks of coupled slow-fast neuron models using time series techniques to understand how coupling strength influences dynamics like chaos, synchronization, and causality.

Contribution

It provides a detailed analysis of how different coupling strategies affect the complex dynamics of neuron networks, including chaos, quasi-periodicity, and synchronization.

Findings

Chaos occurs at inhibitory coupling and high temperature.

Quasi-periodicity appears at weak coupling.

Synchronization of bursting occurs at strong excitatory coupling.

Abstract

We perform time series analysis of small networks where every node is the slow-fast version of the denatured Morris--Lecar neuron proposed by Schaeffer and Cain. We choose popular coupling strategies from the literature and provide a detailed account of how varying their strength drives the dynamics of the small networks. Algorithms for time series analysis range from measuring their persistence (ability to remember past values), irregularity, chaos and quasiperiodicity, to synchronization between time series from every node within a network. Chaos is observed for inhibitory coupling strengths and for temperature higher than a reference temperature when the coupling is thermally sensitive. We observe quasi-periodicity when the coupling is very weak and synchronized bursting for highly excitatory coupling strength. In certain cases we also observe decay oscillations. Finally, a causality test is performed to detect whether the dynamics of one neuron is influencing the dynamics of the other in the coupled system.