复杂网络在临界点的成功
The success of complex networks at criticality
摘要：
在脉冲神经网络中，动作电位原则上可以在初始神经元附近触发后续的脉冲。一个成功的尖峰是触发随后的尖峰，从而在系统内产生级联行为。在这项研究中，我们引入了一个指标来评估的成功整合和消防神经元排列在复杂的拓扑结构，其集体行为正在经历一个相变，这是确定的神经元雪崩，成为集群的激活，其大小的分布可以近似的幂律发射的尖峰。在数值模拟中，我们报告说，具有小世界性质的无标度网络是神经元拥有更成功的尖峰的结构。同样，我们在分析和数值模拟中得出结论，全连接网络是神经元表现较差的结构。此外，我们研究了小世界属性如何影响尖峰行为及其在无标度网络中的成功。
英文摘要：
In spiking neural networks an action potential could in principle trigger subsequent spikes in the neighbourhood of the initial neuron. A successful spike is that which trigger subsequent spikes giving rise to cascading behaviour within the system. In this study we introduce a metric to assess the success of spikes emitted by integrate-and-fire neurons arranged in complex topologies and whose collective behaviour is undergoing a phase transition that is identified by neuronal avalanches that become clusters of activation whose distribution of sizes can be approximated by a power-law. In numerical simulations we report that scale-free networks with the small-world property is the structure in which neurons possess more successful spikes. As well, we conclude both analytically and in numerical simulations that fully-connected networks are structures in which neurons perform worse. Additionally, we study how the small-world property affects spiking behaviour and its success in scale-free networks.
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