Title:
Sequential optimal design of neurophysiology experiments

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dc.contributor.advisor Butera, Robert J.
dc.contributor.advisor Paninski, Liam
dc.contributor.author Lewi, Jeremy en_US
dc.contributor.committeeMember Isbell, Charles
dc.contributor.committeeMember Rozell, Chris
dc.contributor.committeeMember Stanley, Garrett
dc.contributor.committeeMember Vidakovic, Brani
dc.contributor.department Biomedical Engineering en_US
dc.date.accessioned 2009-06-08T19:27:59Z
dc.date.available 2009-06-08T19:27:59Z
dc.date.issued 2009-03-31 en_US
dc.description.abstract For well over 200 years, scientists and doctors have been poking and prodding brains in every which way in an effort to understand how they work. The earliest pokes were quite crude, often involving permanent forms of brain damage. Though neural injury continues to be an active area of research within neuroscience, technology has given neuroscientists a number of tools for stimulating and observing the brain in very subtle ways. Nonetheless, the basic experimental paradigm remains the same; poke the brain and see what happens. For example, neuroscientists studying the visual or auditory system can easily generate any image or sound they can imagine to see how an organism or neuron will respond. Since neuroscientists can now easily design more pokes then they could every deliver, a fundamental question is ``What pokes should they actually use?' The complexity of the brain means that only a small number of the pokes scientists can deliver will produce any information about the brain. One of the fundamental challenges of experimental neuroscience is finding the right stimulus parameters to produce an informative response in the system being studied. This thesis addresses this problem by developing algorithms to sequentially optimize neurophysiology experiments. Every experiment we conduct contains information about how the brain works. Before conducting the next experiment we should use what we have already learned to decide which experiment we should perform next. In particular, we should design an experiment which will reveal the most information about the brain. At a high level, neuroscientists already perform this type of sequential, optimal experimental design; for example crude experiments which knockout entire regions of the brain have given rise to modern experimental techniques which probe the responses of individual neurons using finely tuned stimuli. The goal of this thesis is to develop automated and rigorous methods for optimizing neurophysiology experiments efficiently and at a much finer time scale. In particular, we present methods for near instantaneous optimization of the stimulus being used to drive a neuron. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/28201
dc.publisher Georgia Institute of Technology en_US
dc.subject Generalized linear model (GLM) en_US
dc.subject Active learning en_US
dc.subject Sequential optimal experimental design en_US
dc.subject.lcsh Neurophysiology
dc.subject.lcsh Experimental design
dc.subject.lcsh Combinatorial optimization
dc.title Sequential optimal design of neurophysiology experiments en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication da59be3c-3d0a-41da-91b9-ebe2ecc83b66
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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