JSM 2004 - Toronto

Abstract #301302

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Activity Number: 27
Type: Contributed
Date/Time: Sunday, August 8, 2004 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #301302
Title: Flexible Modeling of Correlated Binary Data for Estimation of Neuron Firing Rates and Synchrony between Neurons
Author(s): Christel Faes*+ and Helena M. Geys and Marc Aerts and Geert Molenberghs and Carmen Cadarso-Suarez
Companies: Limburgs Universitair Centrum and Limburgs Universitair Centrum and Limburgs Universitair Centrum and Limburgs Universitair Centrum and University of Santiago de Compostela
Address: Center for Statistics, Diepenbeek, International, 3590, Belgium
Keywords: synchrony ; pseudo-likelihood ; correlated binary data ; association measure
Abstract:

A fundamental methodology in neurophysiology is eletrophysiology which records the electrical signals produced by individual neurons within the brain of awake-behaving animal. The main goals are to estimate the temporal evolution and effect of covariates on the neuron firing rates, as well as on the synchrony between neurons. Synchrony refers to the observation that in many cases, action potentials emitted from different neurons are emitted at the same time, or very close in time. While multivariate methods of the analysis of continuous outcomes are well understood, multivariate methods for correlated binary data are less developed. A joint model must allow different time- and covariate-depending firing rates for each neuron, and must account for the association between them. The association between neurons might depend on covariates as well. To describe how "synchronous" two spike trains are, a variety of association measures can be used. Focus is on the specification of a flexible marginal model for multivariate correlated binary data together with a pseudo-likelihood estimation approach, to adequately and directly describe the measures of interest.

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Revised March 2004