Stochastic resonance in coupled small-world neural networks

Abstract

Noise exists widely in biological neural systems, and plays an important role in system functions. A complex neural network, which contains several small-world subnetworks, is constructed based on a two-dimensional neural map. The phenomenon of stochastic resonance induced by Gaussian white noise is studied. It is found that only with an appropriate noise, can the frequency response of the network to input signal reach a peak value. Moreover, network structure has an important influence on the stochastic resonance of the neural system. With a fixed coupling strength, there exists an optimal local small-world topology, which can offer the best frequency response of the network.