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PhD Defense: Characterization of visual cells using generic models and natural stimuli.
Thu, Mar 04, 2010 @ 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Receptions & Special Events
Title: Characterization of visual cells using generic models and natural stimuli.Committee:
Dr. Norberto M. Grzywacz (co-chair)
Dr. Jerry M. Mendel (co-chair)
Dr. Vassilis Z. Marmarelis
Dr. Judith A. Hirsch
Thesis:http://confucius.usc.edu/~rapela/thesis/thesis.pdfAbstract:Traditionally visual cells have been characterized using their responses to artificial stimuli by simple parametric models. However, recent investigations show that visual cells adapt to the statistical properties of the stimuli used to probe them. Thus, to characterize visual cells in their natural operating conditions, it is important to use naturalistic stimuli. Simple parametric models are designed for specific classes of cells, making assumptions about their response properties. But, if these assumptions do not match the cell response properties, the interpretation of the estimated model parameters isquestionable. An alternative is to use generic non-parametric models that can characterize a broad range of cell classes. This thesis contains technical and scientific contributions. Technically, we develop methods to estimate generic non-parametric models of visual cells from their responses to arbitrary, including natural, stimuli. In the first part of this thesis, we introduce the Volterra Relevant Space Technique (VRST), that allows the estimation of spatial Volterra models of visual cells from their responses to natural stimuli. Disregarding temporal properties of the response generation mechanism for the estimation of spatial Volterra models is a good first approximation. However, in most conditions responses of visual cells are not spatial, but spatio temporal. So, in the second part of this dissertation we build the extended Projection Pursuit Regression (ePPR) algorithm, that estimates a very general model for the characterization of visual cells in
space and time. The generality of the ePPR model reveals differences in response properties of cortical cells to natural and random stimuli that had not been observed with existing models. Thus, scientifically this thesis shows that using natural stimuli for the characterization of visual cells is relevant
to understand natural vision.
Location: Hedco Neurosciences Building (HNB) - 107
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta