论文标题
用复杂的动态计算大脑网络
Computing brain networks with complex dynamics
论文作者
论文摘要
神经科学中的一个重要问题是，大脑网络中的全球行为是如何从网络连接性与单个节点的神经动力学之间的相互作用中出现的。为了更好地理解这种理论关系，我们一直在探索一种简化的建模方法，在该方法中，我们在复杂平面中为每个节点配备了离散的二次动力学，并研究了所得复杂的二次网络（CQN）的新兴行为。 CQN的长期行为可以由渐近分形组表示，其特定拓扑特征远远超出了传统的单个地图迭代中所描述的特定特征。 在这项研究中，我们说明了这些渐近集的拓扑度量如何有效地用作人类受试者的拖拉学衍生连接组中动力学的综合描述符和分类器。我们研究这些集合的复杂几何形状在多大程度上与网络体系结构（一方面）和网络行为（另一方面）相关。这有助于我们了解受试者的大脑功能，生理和行为及其潜在的连接架构之间关系的机制。
One important question in neuroscience is how global behavior in a brain network emerges from the interplay between network connectivity and the neural dynamics of individual nodes. To better understand this theoretical relationship, we have been exploring a simplified modeling approach in which we equip each node with discrete quadratic dynamics in the complex plane, and we study the emerging behavior of the resulting complex quadratic network (CQN). The long-term behavior of CQNs can be represented by asymptotic fractal sets with specific topological signatures going far beyond those described in traditional single map iterations. In this study, we illustrate how topological measures of these asymptotic sets can be used efficiently as comprehensive descriptors and classifiers of dynamics in tractography-derived connectomes for human subjects. We investigate to what extent the complex geometry of these sets is tied to network architecture (on one hand) and to the network behavior (on the other). This helps us understand the mechanics of the relationship between the subject's brain function, physiology and behavior and their underlying connectivity architecture.
