Synchronization of two non-identical Chialvo neurons

TL;DR

This study explores how two non-identical neurons synchronize under various conditions, including noise, parameter mismatch, and coupling types, using a stochastic Chialvo neuron model to understand broader neuronal synchronization phenomena.

Contribution

It introduces a simple neuron network model to analyze synchronization between non-identical neurons considering noise, parameter mismatch, and different coupling types, with implications for general neuron models.

Findings

Critical noise and mismatch levels for synchronization identified

Synchronization behavior varies with chaotic or periodic neuron dynamics

Both excitatory and inhibitory couplings affect synchronization thresholds

Abstract

We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, and the coupling strength between the neurons. We propose the simplest neuron network for study, as its analysis is more straightforward and does not compromise generality. Within this network, two nonidentical neuron maps are electrically coupled. In order to understand if specific behaviors affect the global behavior of the system, we consider different cases related to the behavior of the neurons (chaotic or periodic). Furthermore, we study how variations in model parameters affect the firing frequency in all cases. Additionally, we consider that the two neurons have both excitatory and inhibitory couplings. Consequently, we identify critical values of noise and mismatch for achieving satisfactory synchronization between the neurons in both cases. Finally, we conjecture that the results are of a general nature and are applicable to different neuron models.