Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks
Herzog, Erik D.
Kiss, Istvan Z.
Schwartz, William J.
UMass Chan AffiliationsDepartment of Neurology
Document TypeJournal Article
Neuroscience and Neurobiology
MetadataShow full item record
AbstractExtracting complex interactions (i.e., dynamic topologies) has been an essential, but difficult, step toward understanding large, complex, and diverse systems including biological, financial, and electrical networks. However, reliable and efficient methods for the recovery or estimation of network topology remain a challenge due to the tremendous scale of emerging systems (e.g., brain and social networks) and the inherent nonlinearity within and between individual units. We develop a unified, data-driven approach to efficiently infer connections of networks (ICON). We apply ICON to determine topology of networks of oscillators with different periodicities, degree nodes, coupling functions, and time scales, arising in silico, and in electrochemistry, neuronal networks, and groups of mice. This method enables the formulation of these large-scale, nonlinear estimation problems as a linear inverse problem that can be solved using parallel computing. Working with data from networks, ICON is robust and versatile enough to reliably reveal full and partial resonance among fast chemical oscillators, coherent circadian rhythms among hundreds of cells, and functional connectivity mediating social synchronization of circadian rhythmicity among mice over weeks.
Proc Natl Acad Sci U S A. 2018 Sep 11;115(37):9300-9305. doi: 10.1073/pnas.1721286115. Epub 2018 Aug 27. Link to article on publisher's site
Permanent Link to this Itemhttp://hdl.handle.net/20.500.14038/40761
RightsCopyright © 2018 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Except where otherwise noted, this item's license is described as Copyright © 2018 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).