Discovering Excitatory Networks from Discrete Event Streams with Applications to Neuronal Spike Train Analysis

—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. We focus in this paper on temporal models representable as excitatory networks where all connections are stimulative, rather than inhibitive. Through this emphasis on excitatory networks, we show how they can be learned by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and which can be summarized into a dynamic Bayesian network (DBN). To demonstrate the practical feasibility of our approach, we show how excitatory networks can be inferred from both mathematical models of spiking neurons as well as real neuroscience datasets. Keywords-Frequent Episodes; Dynamic Bayesian Network; Computational Neuroscience; Spike train analysis; Temporal Data Mining

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Debprakash Patnaik, Srivatsan Laxman, Naren Ramakrishnan
• Keywords: Computational Neuroscience, Data Mining, Dynamic Bayesian Network, Excitatory Networks, ICDM 2009