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Understanding the functioning of a neural system in terms of its underlyingcircuitry is an important problem in neuroscience. Recent developments inelectrophysiology and imaging allow one to simultaneously record activities ofhundreds of neurons. Inferring the underlying neuronal connectivity patternsfrom such multi-neuronal spike train data streams is a challenging statisticaland computational problem. This task involves finding significant temporalpatterns from vast amounts of symbolic time series data. In this paper we showthat the frequent episode mining methods from the field of temporal data miningcan be very useful in this context. In the frequent episode discoveryframework, the data is viewed as a sequence of events, each of which ischaracterized by an event type and its time of occurrence and episodes arecertain types of temporal patterns in such data. Here we show that, using theset of discovered frequent episodes from multi-neuronal data, one can inferdifferent types of connectivity patterns in the neural system that generatedit. For this purpose, we introduce the notion of mining for frequent episodesunder certain temporal constraints; the structure of these temporal constraintsis motivated by the application. We present algorithms for discovering serialand parallel episodes under these temporal constraints. Through extensivesimulation studies we demonstrate that these methods are useful for unearthingpatterns of neuronal network connectivity.

Inferring Neuronal Network Connectivity from Spike Data: A Temporal Datamining Approach