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dc.creatorPark, Mijung
dc.date.accessioned2010-11-02T20:14:32Z
dc.date.accessioned2010-11-02T20:14:39Z
dc.date.available2010-11-02T20:14:32Z
dc.date.available2010-11-02T20:14:39Z
dc.date.created2010-05
dc.date.issued2010-11-02
dc.date.submittedMay 2010
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2010-05-1359
dc.descriptiontext
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.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectNeural receptive fields
dc.subjectLinear regression
dc.subjectRegularization
dc.subjectSpatio-temporal restricted prior
dc.subjectFrequency restricted prior
dc.titleAutomatic regularization technique for the estimation of neural receptive fields
dc.date.updated2010-11-02T20:14:39Z
dc.description.departmentElectrical and Computer Engineering
dc.type.genrethesis*
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering


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