Topology identification of dynamic point process networks

Abstract

Recently, there has been a growing interest in dynamic networks for understanding interactions and information flows. A fundamental problem is the identification of the links or the network topology. In comparison with its time series counterpart, the problem has received little attention in the point process literature. But with high-dimensional point process data becoming available in a number of application areas such as communication networks and neural coding, topology identification has become crucial for understanding the information flows. Here we discuss for the first time topology identification of a dynamic network of interacting Hawkes processes. Cortical recordings from cats are used to identify the interaction of neurons in the primary visual cortex.