Data compression in sensory processing
Authors
Barranca, Victor
ORCID
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Other Contributors
Kovacic, Gregor
Cai, David
Kramer, Peter Roland, 1971-
Roytburd, Victor
Cai, David
Kramer, Peter Roland, 1971-
Roytburd, Victor
Issue Date
2013-05
Keywords
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.
Full Citation
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
School of Science
Department
Dept. of Mathematical Sciences
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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