Inferring neuronal network connectivity from spike data: A temporal data mining approach

Abstract. Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer ...

• Type: Journal
• Year: 2008
• Where: SP
• Authors: Debprakash Patnaik, P. S. Sastry, K. P. Unnikrishnan
• Keywords: Frequent Episodes, Security Privacy, SP 2008, Temporal Constraints, Temporal Patterns