Effects of Different Connectivity Topologies in Small World Networks on EEG-Like Activities

在小世界网络基于我们的以前联合脉搏的 integrate-and-Gre 神经原模型，我们在这个模型生产的象 electroencephalographic 一样信号的复杂行为上调查不同连接拓扑学的效果。我们证明若干次，经常是的系列分析方法使用了分析 electroencephalographic-likesignals 的复杂行为，例如在重新可伸缩的范围分析(R/S ) 以内的阶段空间，关联尺寸，分数维的尺寸，和林中小丘代表的重建。我们发现不同连接拓扑学在 integrate-and-fire 神经原的模型导致不同动态行为。

Based on our previously pulse-coupled integrate-and-fire neuron model in small world networks, we investigate the effects of different connectivity topologies on complex behavior of electroencephalographic-like signals produced by this model. We show that several times series analysis methods that are often used for analyzing complex behavior of electroencephalographic-like signals, such as reconstruction of the phase space, correlation dimension, fractal dimension, and the Hurst exponent within the rescaled range analysis （R/S）. We lind that the different connectivity topologies lead to different dynamical behaviors in models of integrate-and-fire neurons.