Noise-induced network bursts and coherence in a calcium-mediated neural network

Noise-induced population bursting has been widely identified to play important roles in information processes. We construct a mathematical model for a random and sparse heterogeneous neural network where bursting can be induced from a resting state by a global stochastic stimulus. Importantly, the noise-induced bursting dynamics of this network are mediated by calcium conductance. We use two spectral measures to evaluate network coherence in the context of the network bursts, the spike trains of all neurons, and the individual bursts of all neurons. Our results show that the coherence of the network is optimized by an optimal level of the stochastic stimulus, which is known as coherence resonance (CR). We also demonstrate that the interplay of the calcium conductance and noise intensity can modify the degree of CR.

Automated summary

This study constructed a mathematical model to investigate noise-induced population bursting in neural networks, where calcium conductance played a crucial role in the bursting dynamics. Optimal coherence of the network was achieved through an optimal level of stochastic stimulus, known as coherence resonance (CR), and the interplay of calcium conductance and noise intensity could modify the degree of CR.