Network reconstruction using time-delayed spike-train cross-correlation with limited information and classification according to spectral properties
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Authors
Volosov, Paulina
Issue Date
2020-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Mathematics
Alternative Title
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
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY