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CHARACTERIZATION OF VISUAL CELLS USING GENERIC MODELS AND NATURAL STIMULI by Joaqu´ın Rapela A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2010 Copyright 2010 Joaqu´ın Rapela
|Title||Characterization of visual cells using generic models and natural stimuli|
|Author email@example.com; firstname.lastname@example.org|
|Degree||Doctor of Philosophy|
|Degree program||Electrical Engineering|
|School||Viterbi School of Engineering|
|Advisor (committee chair)||
Grzywacz, Norberto M.
Mendel, Jerry M.
|Advisor (committee member)||
Marmarelis, Vasilis Z.
Hirsch, Judith. A.
|Abstract||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 is questionable. 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.|
|Keyword||non-parametric models; natural images; curse of dimensionality|
|Part of collection||University of Southern California dissertations and theses|
|Publisher (of the original version)||University of Southern California|
|Place of publication (of the original version)||Los Angeles, California|
|Publisher (of the digital version)||University of Southern California. Libraries|
|Provenance||Electronically uploaded by the author|
|Legacy record ID||usctheses-m3361|
|Repository name||Libraries, University of Southern California|
|Repository address||Los Angeles, California|
|Full text||CHARACTERIZATION OF VISUAL CELLS USING GENERIC MODELS AND NATURAL STIMULI by Joaqu´ın Rapela A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2010 Copyright 2010 Joaqu´ın Rapela|