Debprakash Patnaik, Srivatsan Laxman, and Naren Ramakrishnan
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.
|Published in||Proceedings of The 2009 IEEE International Conference on Data Mining (ICDM 2009), Miami, USA|
|Publisher||IEEE Computer Society|
[Invited to Springer KAIS, Knowledge And Information Systems, under Best Papers of ICDM 2009]