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Abstract Details
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Activity Number:
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119
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #304156 |
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Title:
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Bayesian Nonparametric Analysis of Neuronal Intensity Rates
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Author(s):
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Sam Behseta*+ and Athanasios Kottas and Valerie Poynor and David Moorman
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Companies:
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California State University/CHANCE and University of California at Santa Cruz and University of California at Santa Cruz and Medical University of South Carolina
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Address:
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Department of Mathematics, CSUF, Fullerton, CA, , USA
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Keywords:
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Bayesian Nonparametrics ;
Hierarchical modeling ;
Dependent Dirichlet process ;
Dirichlet process mixture models ;
Spike trains ;
Supplementary eye field
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Abstract:
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We propose a flexible hierarchical Bayesian nonparametric modeling approach to compare the spike trains of neurons recorded under multiple experimental conditions. We use our statistical methodology to analyze neuronal firing rates obtained from the supplementary eye field of the brains of two macaque monkeys trained to make delayed eye movements to three different types of targets. The proposed Bayesian methodology can be used to perform either a global analysis, allowing for the construction of posterior comparative intervals over the entire experimental time window, or a pointwise analysis for comparing the spiking patterns locally, in a predetermined portion of the experimental time window.
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