论文标题
构建边缘订购的多向图形，用于比较空间颞神经网络的动力学
Construction of edge-ordered multidirected graphlets for comparing dynamics of spatial temporal neural networks
论文作者
论文摘要
大脑中信息的整合和传输取决于结构和动力学特性之间的相互作用。隐含的任何旨在了解适当的数学界限条件集的神经动态的追求是由结构网络的几何形状以及与信息传递有关的产生潜伏期所施加的基本基本结构 - 函数函数约束的概念。我们最近描述了一个框架的构建和理论分析，该框架模拟了局部结构功能规则如何引起神经网络上新兴的全球动态。该研究计划的一个重要部分是对一组数学方法的要求，这些数学方法使我们能够分类，理论分析并数值研究所产生的丰富动力学模式。我们正在探索的一个方向是图形理论的扩展。在本文中，我们介绍了图形和相关度量标准的扩展，该图形将网络从一个时刻到另一个时刻的拓扑过渡，同时保留了因果关系。
The integration and transmission of information in the brain are dependent on the interplay between structural and dynamical properties. Implicit in any pursuit aimed at understanding neural dynamics from appropriate sets of mathematically bounded conditions is the notion of an underlying fundamental structure-function constraint imposed by the geometry of the structural networks and the resultant latencies involved with transfer of information. We recently described the construction and theoretical analysis of a framework that models how local structure-function rules give rise to emergent global dynamics on a neural network. An important part of this research program is the requirement for a set of mathematical methods that allow us to catalog, theoretically analyze, and numerically study the rich dynamical patterns that result. One direction we are exploring is an extension of the theory of graphlets. In this paper we introduce an extension of graphlets and associated metric that maps the topological transition of a network from one moment in time to another at the same time that causal relationships are preserved.
