Network reconstruction using time-delayed spike-train cross-correlation with limited information and classification according to spectral properties

Authors
Volosov, Paulina
ORCID
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Other Contributors
Kovacic, Gregor
Holmes, Mark H.
Kramer, Peter Roland, 1971-
Zhou, Douglas
Issue Date
2020-08
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
We begin by reconstructing the entire network using time-delayed spike-train correlation, and we determine the time required before an adequate reconstruction becomes possible and compare this to time spans employed by experimentalists. We then sample the reconstruction matrix randomly and use the tool of matrix completion to fill in the rest of the network. To more closely mimic experimental settings, we next examine a small subnetwork of the network and determine how much information we can deduce about the whole network from this small piece. An examination of the spectral properties of connectivity matrices forms a major part of this analysis, and we formulate a metric which classifies the complex architectural network structure. Our results have practical implications for network science and computational neuroscience.
Description
August 2020
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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