JSM 2011 Online Program

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Abstract Details

Activity Number: 405
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #302071
Title: Assessing Functional Connectivity in Neuronal Networks from Spike-Train Data: A New Class of Models and Likelihood-Based Inference
Author(s): Kohinoor Dasgupta*+ and Jijay Nair and Stilian Stoev and Xuanlong Nguyen
Companies: University of Michigan and University of Michigan and University of Michigan and University of Michigan
Address: Department of Statistics, Ann Arbor, MI, 48109,
Keywords: Likelihood based methods ; Generalized linear models ; Spike-Train data
Abstract:

Recent advances in technology have allowed neuroscientists to collect large amounts of electrophysiological data at fine time scales. An important class of such data is multi-neuronal spike trains - time sequences of firings of a group of neurons. Identifying the functional connectivity of the neurons from spike-train data has been a problem of considerable interest in recent years. This paper proposes a new class of models for characterizing the dependence and connectivity among the neurons over time. Likelihood-based methods for estimating the underlying parameters including the connectivity matrix and base firing rates have also been developed. Asymptotic theory for the estimators is currently under investigation. Our inference methods are shown to be considerably less complex than other comparable methods in the literature and are illustrated on simulation and real data.


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