Revealing networks from dynamics: an introduction

被引:156
作者
Timme, Marc [1 ,2 ,3 ]
Casadiego, Jose [1 ]
机构
[1] Max Planck Inst Dynam & Self Org MPIDS, Network Dynam, D-37077 Gottingen, Germany
[2] Bernstein Ctr Computat Neurosci BCCN Gottingen, D-37077 Gottingen, Germany
[3] Univ Gottingen, Inst Nonlinear Dynam, Fac Phys, D-37077 Gottingen, Germany
关键词
network reconstruction; network dynamics; network inference; structural connectivity; functional connectivity; effective connectivity; network topology; complex networks; synchrony; ENGINEERING GENE NETWORKS; NEURAL-NETWORKS; PERTURBATIONS; CONNECTIVITY; PREDICTION; TOPOLOGY; DESIGN; MODEL;
D O I
10.1088/1751-8113/47/34/343001
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.
引用
收藏
页数:36
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