Deciphering the neural code for retinal ganglion cells through statistical inference
Name
550571524-MIT.pdf
Description
Full printable version
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20.67 MB
Format
Adobe PDF
Checksum (MD5)
3b5c46038ff498455bb77b33f284108f
Author(s)
Wu, Yi-Chieh, Ph. D. Massachusetts Institute of Technology
Advisor(s)
John L. Wyatt.
Date Issued
2009
Publisher
Massachusetts Institute of Technology
Abstract
This work studies how the visual system encodes information in the firing patterns of retinal ganglion cells. We present a visual scene to a retina, obtain in-vitro recordings from a multi-electrode array, and attempt to identify or reconstruct the scene. Our approach uses the well-known linear-nonlinear Poisson model to characterize neural firing behavior and accounts for stochastic variability by fitting parameters using maximum likelihood. To characterize cells, we use white noise analysis followed by numerical optimization to maximize the likelihood of the experimentally observed neural responses. We then validate our method by keeping these fitted parameters constant and using them to estimate the speed and direction of moving edges, and to identify a natural scene out of a set of possible candidates. Limitations of our approach, including reconstruction fidelity and the validity of various assumption are also examined through simulated cell responses.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 85-87).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
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