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  • 标题:Inferring dynamic topology for decoding spatiotemporal structures in complex heterogeneous networks
  • 本地全文:下载
  • 作者:Shuo Wang ; Erik D. Herzog ; István Z. Kiss
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2018
  • 卷号:115
  • 期号:37
  • 页码:9300-9305
  • DOI:10.1073/pnas.1721286115
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要: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.
  • 关键词:network inference ; complex networks ; dynamic topology ; circadian rhythms ; social synchronization
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