Automatic regularization technique for the estimation of neural receptive fields

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Automatic regularization technique for the estimation of neural receptive fields

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dc.contributor.advisor Vikalo, Haris
dc.contributor.advisor Pillow, Jonathan W.
dc.creator Park, Mijung
dc.date.accessioned 2010-11-02T20:14:32Z
dc.date.accessioned 2010-11-02T20:14:39Z
dc.date.available 2010-11-02T20:14:32Z
dc.date.available 2010-11-02T20:14:39Z
dc.date.created 2010-05
dc.date.issued 2010-11-02
dc.date.submitted May 2010
dc.identifier.uri http://hdl.handle.net/2152/ETD-UT-2010-05-1359
dc.description.abstract A 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.mimetype application/pdf
dc.language.iso eng
dc.subject Neural receptive fields
dc.subject Linear regression
dc.subject Regularization
dc.subject Spatio-temporal restricted prior
dc.subject Frequency restricted prior
dc.title Automatic regularization technique for the estimation of neural receptive fields
dc.date.updated 2010-11-02T20:14:39Z
dc.description.department Electrical and Computer Engineering
dc.type.genre thesis
dc.type.material text
thesis.degree.department Electrical and Computer Engineering
thesis.degree.discipline Electrical and Computer Engineering
thesis.degree.grantor University of Texas at Austin
thesis.degree.level Masters
thesis.degree.name Master of Science in Engineering

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