Statistical Inference of Functional Connectivity in Neuronal Networks using Frequent Episodes

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dc.creatorDiekman, Casey
dc.creatorDasgupta, Kohinoor
dc.creatorNair, Vijay
dc.creatorSastry, P. S.
dc.creatorUnnikrishnan, K. P.
dc.date2009-02-22
dc.date.accessioned2026-07-07T12:47:53Z
dc.date.available2026-07-07T12:47:53Z
dc.descriptionIdentifying the spatio-temporal network structure of brain activity from multi-neuronal data streams is one of the biggest challenges in neuroscience. Repeating patterns of precisely timed activity across a group of neurons is potentially indicative of a microcircuit in the underlying neural tissue. Frequent episode discovery, a temporal data mining framework, has recently been shown to be a computationally efficient method of counting the occurrences of such patterns. In this paper, we propose a framework to determine when the counts are statistically significant by modeling the counting process. Our model allows direct estimation of the strengths of functional connections between neurons with improved resolution over previously published methods. It can also be used to rank the patterns discovered in a network of neurons according to their strengths and begin to reconstruct the graph structure of the network that produced the spike data. We validate our methods on simulated data and present analysis of patterns discovered in data from cultures of cortical neurons.
dc.description15 pages
dc.identifierhttps://arxiv.org/abs/0902.3725
dc.identifierhttp://arxiv.org/abs/0902.3725
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/221869
dc.subjectNeurons and Cognition
dc.subjectDisordered Systems and Neural Networks
dc.subjectDatabases
dc.subjectQuantitative Methods
dc.subjectMethodology
dc.titleStatistical Inference of Functional Connectivity in Neuronal Networks using Frequent Episodes
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