  Noise-induced population bursting has been widely identified to play
important roles in the information process. We constructed a mathematical model
for a random and sparse neural network where bursting can be induced from the
resting state by the global stochastic stimulus. Importantly, the noise-induced
bursting dynamics of this network are mediated by calcium conductance. We use
two spectral measures to evaluate the network coherence in the context of
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 stochastic stimulus, which is known as coherence resonance
(CR). We also demonstrate that the interplay of calcium conductance and noise
intensity can modify the degree of CR.
