Department of Neurology
Biology | Neuroscience and Neurobiology
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.
circadian rhythms, complex networks, dynamic topology, network inference, social synchronization
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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
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
Proceedings of the National Academy of Sciences of the United States of America
Wang S, Herzog ED, Kiss IZ, Schwartz WJ, Bloch G, Sebek M, Granados-Fuentes D, Wang L, Li J. (2018). Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks. Open Access Articles. https://doi.org/10.1073/pnas.1721286115. Retrieved from 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.