Type of Document	Dissertation
Author	Lawhern, Vernon
URN	etd-04132011-151042
Title	Statistical Modeling and Applications of Neural Spike Trains
Degree	Doctor of Philosophy
Department	Statistics, Department of
Advisory Committee	Advisor Name Title Wei Wu Committee Chair Anuj Srivastava Committee Member Fred Huffer Committee Member Xufeng Niu Committee Member Robert Contreras University Representative
Keywords	generalized linear model Neural coding state space model
Date of Defense	2011-03-24
Availability	unrestricted
Abstract In this thesis we investigate statistical modelling of neural activity in the brain. We rst develop a framework which is an extension of the state-space Generalized Linear Model (GLM) by Eden and colleagues [20] to include the eects of hidden states. These states, collectively, represent variables which are not observed (or even observable) in the modelling 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 dierent target-driven hand and arm movement tasks. Finally, we perform temporal coding analysis of sensory stimulation using principled statistical models and show the ecacy of our approach.
Files	Filename       Size       Approximate Download Time (Hours:Minutes:Seconds)     28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access    Lawhern_V_Dissertation_2011.pdf 4.67 Mb 00:21:37 00:11:07 00:09:43 00:04:51 00:00:24