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Quantifying uncertainty in computational neuroscience with Bayesian statistical inference

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dc.contributor.advisor Mriganka Sur. en_US
dc.contributor.author Cronin, Beau D en_US
dc.contributor.other Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. en_US
dc.date.accessioned 2009-04-29T17:27:24Z
dc.date.available 2009-04-29T17:27:24Z
dc.date.copyright 2008 en_US
dc.date.issued 2008 en_US
dc.identifier.uri http://hdl.handle.net/1721.1/45336
dc.description Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2008. en_US
dc.description Includes bibliographical references (p. 101-106). en_US
dc.description.abstract Two key fields of computational neuroscience involve, respectively, the analysis of experimental recordings to understand the functional properties of neurons, and modeling how neurons and networks process sensory information in order to represent the environment. In both of these endeavors, it is crucial to understand and quantify uncertainty - when describing how the brain itself draws conclusions about the physical world, and when the experimenter interprets neuronal data. Bayesian modeling and inference methods provide many advantages for doing so. Three projects are presented that illustrate the advantages of the Bayesian approach. In the first, Markov chain Monte Carlo (MCMC) sampling methods were used to answer a range of scientific questions that arise in the analysis of physiological data from tuning curve experiments; in addition, a software toolbox is described that makes these methods widely accessible. In the second project, the model developed in the first project was extended to describe the detailed dynamics of orientation tuning in neurons in cat primary visual cortex. Using more sophisticated sampling-based inference methods, this model was applied to answer specific scientific questions about the tuning properties of a recorded population. The final project uses a Bayesian model to provide a normative explanation of sensory adaptation phenomena. The model was able to explain a range of detailed physiological adaptation phenomena. en_US
dc.description.statementofresponsibility by Beau D. Cronin. en_US
dc.format.extent 106 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri http://dspace.mit.edu/handle/1721.1/7582 en_US
dc.subject Brain and Cognitive Sciences. en_US
dc.title Quantifying uncertainty in computational neuroscience with Bayesian statistical inference en_US
dc.type Thesis en_US
dc.description.degree Ph.D. en_US
dc.contributor.department Massachusetts Institute of Technology. Dept. of Brain and Cognitive Sciences. en_US
dc.identifier.oclc 315901857 en_US


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