Cebulla Christof
Institute for Applied Mathematics, Universität Bonn, Bonn, Germany.
Neural Comput. 2007 Sep;19(9):2492-514. doi: 10.1162/neco.2007.19.9.2492.
We propose an approach to the analysis of the influence of the topology of a neural network on its synchronizability in the sense of equal output activity rates given by a particular neural network model. The model we introduce is a variation of the Zhang model. We investigate the time-asymptotic behavior of the corresponding dynamical system (in particular, the conditions for the existence of an invariant compact asymptotic set) and apply the results of the synchronizability analysis on a class of random scale free networks and to the classical random networks with Poisson connectivity distribution.
我们提出一种方法，用于分析特定神经网络模型给出的相等输出活动率意义下神经网络拓扑结构对其同步性的影响。我们引入的模型是张模型的一个变体。我们研究相应动力系统的时间渐近行为（特别是不变紧渐近集存在的条件），并将同步性分析结果应用于一类随机无标度网络以及具有泊松连接分布的经典随机网络。
