Noise-induce coexisting firing patterns in hybrid-synaptic interacting networks

TL;DR

This paper investigates how synaptic noise influences the coexistence of different firing patterns in hybrid-synaptic neuronal networks, revealing new mechanisms and statistical insights into neural multistability.

Contribution

It demonstrates that synaptic noise intensity and excitatory weights significantly affect firing pattern coexistence, introducing detailed statistical analysis of multistability types in such networks.

Findings

Identification of time-varying multistability as a metastable state

Discovery of parameter-varying multistability involving synchrony and asynchronous states

Validation of statistical methods for analyzing neural multistability

Abstract

Synaptic noise plays a major role in setting up coexistence of various firing patterns, but the precise mechanisms whereby these synaptic noise contributes to coexisting firing activities are subtle and remain elusive. To investigate these mechanisms, neurons with hybrid synaptic interaction in a balanced neuronal networks have been recently put forward. Here we show that both synaptic noise intensity and excitatory weights can make a greater contribution than variance of synaptic noise to the coexistence of firing states with slight modification parameters. The resulting statistical analysis of both voltage trajectories and their spike trains reveals two forms of coexisting firing patterns: time-varying and parameter-varying multistability. The emergence of time-varying multistability as a format of metstable state has been observed under suitable parameters settings of noise intensity and excitatory synaptic weight. While the parameter-varying multistability is accompanied by coexistence of synchrony state and metastable (or asynchronous firing state) with slightly varying noise intensity and excitatory weights. Our results offer a series of precise statistical explanation of the intricate effect of synaptic noise in neural multistability. This reconciles previous theoretical and numerical works, and confirms the suitability of various statistical methods to investigate multistability in a hybrid synaptic interacting neuronal networks.