论文标题
多重神经网络中的孤立状态：发作和脆弱性
Solitary states in multiplex neural networks: onset and vulnerability
论文作者
论文摘要
我们研究了振荡性方案中Fitzhugh-Nagumo神经元的两层多路复用网络中的孤立状态。我们证明了如何在由两个非相同层组成的多重网络中诱导孤立状态。更具体地说，我们表明这些模式可以通过弱的多路复用引入到完全同步的网络中。我们表明，在层间耦合强度的变化下，这种结果是可靠的，并且在很大程度上独立于初始条件的选择。此外，我们研究了孤立状态在层间拓扑变化方面的脆弱性。更详细地说，我们删除了连接每个层的两个孤立节点的链接，并评估所得模式。我们发现孤立状态对拓扑（网络中的位置）和动力学（振荡阶段）特征的生存能力的高度非平地依赖性。
We investigate solitary states in a two-layer multiplex network of FitzHugh-Nagumo neurons in the oscillatory regime. We demonstrate how solitary states can be induced in a multiplex network consisting of two non-identical layers. More specifically, we show that these patterns can be introduced via weak multiplexing into a network that is fully synchronized in isolation. We show that this result is robust under variations of the inter-layer coupling strength and largely independent of the choice of initial conditions. Moreover, we study the vulnerability of solitary states with respect to changes in the inter-layer topology. In more detail, we remove links that connect two solitary nodes of each layer and evaluate the resulting pattern. We find a highly non-trivial dependence of the survivability of the solitary states on topological (position in the network) and dynamical (phase of the oscillation) characteristics.
