耦合小世界神经网络的随机共振

噪声广泛存在于生物神经系统中,对系统功能具有重要作用.采用神经元二维映射模型构建一个复杂神经网络,由多个小世界子网络构成,研究了Gaussian白噪声诱导的随机共振现象.研究发现,只有合适的噪声强度才能使神经网络对输入刺激信号的频率响应达到峰值.另外,网络结构对系统随机共振特性有重要影响.在固定的耦合强度下,存在一个最优的局部小世界子网络结构,使得整个系统的频率响应最佳.

Stochastic resonance in coupled small-world neural networks

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.