Hierarchical networks, power laws, and neuronal avalanches
Hierarchical networks, power laws, and neuronal avalanches
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DOI：
10.1063/1.4793782
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发表时间：
2013-03-01
期刊：
影响因子：
2.9
通讯作者：
Landsberg, Adam S.
中科院分区：
文献类型：
--
作者：
Friedman, Eric J.;Landsberg, Adam S.
我们表明，在网络的层次结构，关键的动力学行为可以出现，即使底层的动力学过程并不关键。这一发现提供了明确的洞察到目前的研究大脑的神经元网络显示幂律雪崩的神经记录，并提供了一个理论的理由，最近的数值结果。我们的分析表明，层次组织的网络本身可以导致幂律分布的雪崩大小和持续时间，标度律之间的异常指数，和通用功能，即使在没有自组织临界或临界点。这种层次引起的现象是独立的，虽然可以潜在地与标准的动力学机制产生的幂律一起操作。(C)2013年美国物理学会。[http：//dx.doi.org/10.1063/1.4793782]
We show that in networks with a hierarchical architecture, critical dynamical behaviors can emerge even when the underlying dynamical processes are not critical. This finding provides explicit insight into current studies of the brain's neuronal network showing power-law avalanches in neural recordings, and provides a theoretical justification of recent numerical findings. Our analysis shows how the hierarchical organization of a network can itself lead to power-law distributions of avalanche sizes and durations, scaling laws between anomalous exponents, and universal functions-even in the absence of self-organized criticality or critical points. This hierarchy-induced phenomenon is independent of, though can potentially operate in conjunction with, standard dynamical mechanisms for generating power laws. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4793782]