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In this thesis we investigate statistical modelling of neural activity in the brain. We first develop a framework which is an extension of the state-space Generalized Linear Model (GLM) by Eden and colleagues [20] to include the effects of hidden states. These states, collectively, represent variables which are not observed (or even observable) in the modeling process but nonetheless can have an impact on the neural activity. We then develop a framework that allows us to input apriori target information into the model. We examine both of these modelling frameworks on motor cortex data recorded from monkeys performing different target-driven hand and arm movement tasks. Finally, we perform temporal coding analysis of sensory stimulation using principled statistical models and show the efficacy of our approach.
generalized linear model, Neural coding, state space model
Date of Defense
March 24, 2011.
Submitted Note
A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Bibliography Note
Includes bibliographical references.
Advisory Committee
Wei Wu, Professor Directing Thesis; Robert J. Contreras, University Representative; Anuj Srivastava, Committee Member; Fred Huffer, Committee Member; Xufeng Niu, Committee Member.
Publisher
Florida State University
Identifier
FSU_migr_etd-3251
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