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    Automatic regularization technique for the estimation of neural receptive fields

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    PARK-THESIS.pdf (2.771Mb)
    Date
    2010-05
    Author
    Park, Mijung
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    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.
    Department
    Electrical and Computer Engineering
    Description
    text
    Subject
    Neural receptive fields
    Linear regression
    Regularization
    Spatio-temporal restricted prior
    Frequency restricted prior
    URI
    http://hdl.handle.net/2152/ETD-UT-2010-05-1359
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    © The University of Texas at Austin