Suppression of synchronous spiking in two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons

TL;DR

This paper investigates how external stimulation can suppress collective oscillations in a neural network with excitatory and inhibitory neurons, using mean-field analysis and simulations to reveal different dynamic modes and control strategies.

Contribution

It provides an exact low-dimensional mean-field model for large neural networks and demonstrates how external stimulation can switch network states and suppress oscillations.

Findings

High-frequency stimulation stabilizes resting state and suppresses oscillations.

Bistability allows switching between oscillatory and resting states with inhibitory pulses.

Mean-field results are validated by microscopic simulations.

Abstract

Collective oscillations and their suppression by external stimulation are analyzed in a large-scale neural network consisting of two interacting populations of excitatory and inhibitory quadratic integrate-and-fire neurons. In the limit of an infinite number of neurons, the microscopic model of this network can be reduced to an exact low-dimensional system of mean-field equations. Bifurcation analysis of these equations reveals three different dynamic modes in a free network: a stable resting state, a stable limit cycle, and bistability with a coexisting resting state and a limit cycle. We show that in the limit cycle mode, high-frequency stimulation of an inhibitory population can stabilize an unstable resting state and effectively suppress collective oscillations. We also show that in the bistable mode, the dynamics of the network can be switched from a stable limit cycle to a stable resting state by applying an inhibitory pulse to the excitatory population. The results obtained from the mean-field equations are confirmed by numerical simulation of the microscopic model.