Transient termination of synaptically sustained spiking by stochastic inputs in a pair of coupled Type 1 neurons

TL;DR

This paper investigates how stochastic inputs can transiently interrupt and restore sustained oscillations in coupled Type 1 neurons, revealing asymmetric transition times and analyzing the system's stability through basin of attraction and Markov processes.

Contribution

It introduces a detailed analysis of noise-induced transient termination of oscillations in coupled neurons, combining numerical basin analysis with Markov theory.

Findings

Gaussian noise can transiently stop neuron oscillations

Transition times between states are strongly asymmetric

The system's behavior is explained via escape probabilities from the basin of attraction

Abstract

We examine the effects of stochastic input currents on the firing behavior of two excitable neurons coupled with fast excitatory synapses. In such cells (models), typified by the quadratic integrate and fire model, mutual synaptic coupling can cause sustained firing or oscillatory behavior which is necessarily antiphase. Additive Gaussian white noise can transiently terminate the oscillations, hence destroying the stable limit cycle. Further application of the noise may return the system to spiking activity. In a particular noise range, the transition times between the oscillating and the resting state are strongly asymmetric. We numerically investigate an approximate basin of attraction, A, of the periodic orbit and use Markov process theory to explain the firing behavior in terms of the probability of escape of trajectories from A