论文标题
自适应网络中的异步转变
Desynchronization transitions in adaptive networks
论文作者
论文摘要
自适应网络会随着时间的流逝而改变其连接性，具体取决于其动态状态。尽管已经对结构静态网络中的同步进行了广泛的研究，但对于自适应网络而言，此问题更具挑战性。在这封信中，我们为大量的自适应网络开发了主稳定性方法。这种方法允许通过解耦拓扑和动力学属性来减少自适应网络的同步问题。我们展示了适应性和网络结构之间的相互作用如何产生稳定岛的形成。此外，我们报告了异步转变以及由增强的总体耦合强度引起的复杂部分同步模式的出现。我们使用具有突触可塑性的耦合相振荡器和Fitzhugh-Nagumo神经元的自适应网络来说明我们的发现。
Adaptive networks change their connectivity with time, depending on their dynamical state. While synchronization in structurally static networks has been studied extensively, this problem is much more challenging for adaptive networks. In this Letter, we develop the master stability approach for a large class of adaptive networks. This approach allows for reducing the synchronization problem for adaptive networks to a low-dimensional system, by decoupling topological and dynamical properties. We show how the interplay between adaptivity and network structure gives rise to the formation of stability islands. Moreover, we report a desynchronization transition and the emergence of complex partial synchronization patterns induced by an increasing overall coupling strength. We illustrate our findings using adaptive networks of coupled phase oscillators and FitzHugh-Nagumo neurons with synaptic plasticity.
