Discovering excitatory relationships using dynamic Bayesian networks

作者：Patnaik Debprakash^*; Laxman Srivatsan; Ramakrishnan Naren

来源：Knowledge and Information Systems, 2011, 29(2): 273-303.

DOI：10.1007/s10115-010-0344-6

• Frequent episodes
• Dynamic Bayesian networks
• Computational neuroscience
• Spike train analysis
• Temporal data mining

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.

• 出版日期2011-11
• 单位Microsoft; Virginia Tech; 美国弗吉尼亚理工大学(Virginia Tech)