# Control of coherence resonance by self-induced stochastic resonance in a multiplex neural network

## TL;DR

This paper demonstrates how self-induced stochastic resonance (SISR) can be used to control and enhance coherence resonance (CR) in a multiplex neural network of FitzHugh-Nagumo neurons, with implications for neural dynamics and engineering.

## Contribution

It introduces a method to control CR in a neural network using SISR in a multiplex structure, highlighting the effects of coupling strength and delays.

## Key findings

- SISR can significantly improve poor CR in the second layer.
- Strong inter-layer coupling enhances CR, while long delays deteriorate it.
- SISR outperforms CR in certain coupling regimes for controlling CR.

## Abstract

We consider a two-layer multiplex network of diffusively coupled FitzHugh-Nagumo (FHN) neurons in the excitable regime. It is shown, in contrast to SISR in a single isolated FHN neuron, that the maximum noise amplitude at which SISR occurs in the network of coupled FHN neurons is controllable, especially in the regime of strong coupling forces and long time delays. In order to use SISR in the first layer of the multiplex network to control CR in the second layer, we first choose the control parameters of the second layer in isolation such that in one case CR is poor and in another case, non-existent. It is then shown that a pronounced SISR cannot only significantly improve a poor CR, but can also induce a pronounced CR, which was non-existent in the isolated second layer. In contrast to strong intra-layer coupling forces, strong inter-layer coupling forces are found to enhance CR. While long inter-layer time delays just as long intra-layer time delays, deteriorates CR. Most importantly, we find that in a strong inter-layer coupling regime, SISR in the first layer performs better than CR in enhancing CR in the second layer. But in a weak inter-layer coupling regime, CR in the first layer performs better than SISR in enhancing CR in the second layer. Our results could find novel applications in noisy neural network dynamics and engineering.