Title
Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks
UMMS Affiliation
Department of Neurology
Publication Date
2018-09-11
Document Type
Article
Disciplines
Biology | Neuroscience and Neurobiology
Abstract
Extracting 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.
Keywords
circadian rhythms, complex networks, dynamic topology, network inference, social synchronization
Rights and Permissions
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).
DOI of Published Version
10.1073/pnas.1721286115
Source
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
Journal/Book/Conference Title
Proceedings of the National Academy of Sciences of the United States of America
Related Resources
PubMed ID
30150403
Repository Citation
Wang, Shuo; Herzog, Erik D.; Kiss, Istvan Z.; Schwartz, William J.; Bloch, Guy; Sebek, Michael; Granados-Fuentes, Daniel; Wang, Liang; and Li, Jr-Shin, "Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks" (2018). Open Access Articles. 3565.
https://escholarship.umassmed.edu/oapubs/3565
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.