Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Discovering Excitatory Relationships using Dynamic Bayesian Networks

Debprakash Patnaik, Srivatsan Laxman, and Naren Ramakrishnan

Abstract

Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human-computer interaction modeling. In this paper we introduce the notion of excitatory networks which are essentially temporal models where all connections are stimulative, rather than inhibitive. The emphasis on excitatory connections facilitates learning of network models by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and that such relationships can be summarized into a dynamic Bayesian network (DBN). This leads to an algorithm that is significantly faster than state-of-the-art methods for inferring DBNs, while simultaneously providing theoretical guarantees on network optimality. We demonstrate the advantages of our approach through an application in neuroscience, where we show how strong excitatory networks can be efficiently inferred from both mathematical models of spiking neurons and several real neuroscience datasets.

Details

Publication typeArticle
Published inKnowledge and Information Systems (KAIS)
Pages273-303
Volume29
Number2
PublisherSpringer Verlag
> Publications > Discovering Excitatory Relationships using Dynamic Bayesian Networks