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    Data compression in sensory processing

    Author
    Barranca, Victor
    View/Open
    167197_Barranca_rpi_0185E_10048.pdf (17.59Mb)
    Other Contributors
    Kovacic, Gregor; Cai, David; Kramer, Peter Roland, 1971-; Roytburd, Victor;
    Date Issued
    2013-05
    Subject
    Mathematics
    Degree
    PhD;
    Terms of Use
    This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.;
    Metadata
    Show full item record
    URI
    https://hdl.handle.net/20.500.13015/897
    Abstract
    Along the early stages of many sensory pathways, significant downstream reductions occur in the numbers of neurons transmitting stimuli. To understand how much information is lost due to such a reduction, we investigate an idealized mathematical model of the retina using an integrate-and-fire type modeling structure. Our model features a large network of receptor cells randomly and sparsely coupled to a relatively small network of downstream neurons. Using numerical simulations of our model dynamical system and a static mean-field analytical reduction, we demonstrate firing patterns in the downstream neurons can in fact be used to reconstruct stimuli and confirm that mechanisms of data-preservation similar to compressive sensing may be at work in receptive fields. To address the underlying structure of our model and assess its biological realism, we study how the quality of the reconstruction depends on our choice of physiological features, reflected by the model parameters. Moreover, we extend our idealized model to address a number of more realistic scenarios, including clumped receptive fields and temporally varying stimuli. Our methods are expected to provide guidance for studying information loss in more realistic neuronal network models as well as experiments investigating stimuli reconstruction in sensory pathways.;
    Description
    May 2013; School of Science
    Department
    Dept. of Mathematical Sciences;
    Publisher
    Rensselaer Polytechnic Institute, Troy, NY
    Relationships
    Rensselaer Theses and Dissertations Online Collection;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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