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dc.contributor.advisorVikalo, Harisen
dc.contributor.advisorPillow, Jonathan W.en
dc.creatorPark, Mijungen
dc.date.accessioned2010-11-02T20:14:32Zen
dc.date.accessioned2010-11-02T20:14:39Zen
dc.date.available2010-11-02T20:14:32Zen
dc.date.available2010-11-02T20:14:39Zen
dc.date.created2010-05en
dc.date.issued2010-11-02en
dc.date.submittedMay 2010en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-05-1359en
dc.descriptiontexten
dc.description.abstractA fundamental question on visual system in neuroscience is how the visual stimuli are functionally related to neural responses. This relationship is often explained by the notion of receptive fields, an approximated linear or quasi-linear filter that encodes the high dimensional visual stimuli into neural spikes. Traditional methods for estimating the filter do not efficiently exploit prior information about the structure of neural receptive fields. Here, we propose several approaches to design the prior distribution over the filter, considering the neurophysiological fact that receptive fields tend to be localized both in space-time and spatio-temporal frequency domain. To automatically regularize the estimation of neural receptive fields, we use the evidence optimization technique, a MAP (maximum a posteriori) estimation under a prior distribution whose parameters are set by maximizing the marginal likelihood. Simulation results show that the proposed methods can estimate the receptive field using datasets that are tens to hundreds of times smaller than those required by traditional methods.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectNeural receptive fieldsen
dc.subjectLinear regressionen
dc.subjectRegularizationen
dc.subjectSpatio-temporal restricted prioren
dc.subjectFrequency restricted prioren
dc.titleAutomatic regularization technique for the estimation of neural receptive fieldsen
dc.date.updated2010-11-02T20:14:39Zen
dc.description.departmentElectrical and Computer Engineeringen
dc.type.genrethesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen


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