Pattern Synchronization in a Two-Layer Neuronal Network

在一个二层的 neuronal 网络的模式同步被学习。为 Rulkov 地图神经原的一个单个层的网络，有三种模式，噪音导致。添加剂噪音能以一个反响的方法在一些中间的噪音紧张导致订的模式；然而，为小、大的噪音，在那里的紧张存在易兴奋的模式和混乱模式分别地。为二个单个层的网络与层之间的噪音紧张差别联合的一个 neuronal 网络，我们发现二层的网络能作为联合力量增加的夹层完成 synchrony。同步状态强烈取决于联合在层之间的力量和噪音紧张差别的夹层。

Pattern synchronization in a two-layer neuronal network is studied. For a single-layer network of Rulkov map neurons, there are three kinds of patterns induced by noise. Additive noise can induce ordered patterns at some intermediate noise intensities in a resonant way; however, for small and large noise intensities there exist excitable patterns and disordered patterns, respectively. For a neuronal network coupled by two single-layer networks with noise intensity differences between layers, we find that the two-layer network can achieve synchrony as the interlayer coupling strength increases. The synchronous states strongly depend on the interlayer coupling strength and the noise intensity difference between layers.