Control of noise-induced coherent oscillations in three-neuron motifs

TL;DR

This paper investigates how to control and enhance noise-induced coherent oscillations in neuronal motifs using autapses and multiplexing, revealing strategies to improve stochastic resonance in neural networks.

Contribution

It introduces novel methods for enhancing stochastic resonance in neuronal motifs through autapses and multiplexing, considering topology and synaptic delays.

Findings

Autapses can significantly enhance SISR coherence in single neurons.

Multiplexing with high-SISR motifs boosts coherence in low-SISR motifs.

The effectiveness of enhancement strategies depends on network topology and synaptic delays.

Abstract

The phenomenon of self-induced stochastic resonance (SISR) requires a nontrivial scaling limit between the deterministic and the stochastic timescales of an excitable system, leading to the emergence of coherent oscillations which are absent without noise. In this paper, we numerically investigate SISR and its control in single neurons and three-neuron motifs made up of the Morris-Lecar model. In single neurons, we compare the effects of electrical and chemical autapses on the degree of coherence of the oscillations due to SISR. In the motifs, we compare the effects of altering the synaptic time-delayed couplings and the topologies on the degree of SISR. Finally, we provide two enhancement strategies for a particularly poor degree of SISR in motifs with chemical synapses: (i) we show that a poor SISR can be significantly enhanced by attaching an electrical or an excitatory chemical autapse on one of the neurons, and (ii) we show that by multiplexing the motif with a poor SISR to another motif (with a high SISR in isolation), the degree of SISR in the former motif can be significantly enhanced. We show that the efficiency of these enhancement strategies depends on the topology of the motifs and the nature of synaptic time-delayed couplings mediating the multiplexing connections.