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

Patnaik, Debprakash and Sastry, PS and Unnikrishnan, KP (2008) Inferring neuronal network connectivity from spike data: A temporal data mining approach. In: Scientific Programming, 16 (1). pp. 49-77.

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Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments 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 different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Item Type: Journal Article
Publication: Scientific Programming
Publisher: IOS Press
Additional Information: Copyright of this article belongs to IOS Press.
Keywords: Data mining;temporal data mining;temporal constraints; frequent episodes;multiple neural spike train data;spike train data analysis;functional connectivity;neuronal connectivity patterns;synfire chains; synchrony.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 11 Jan 2010 08:11
Last Modified: 16 Mar 2012 08:19
URI: http://eprints.iisc.ac.in/id/eprint/25258

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