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Title: Inferring a network from dynamical signals at its nodes
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network.  more » « less
Award ID(s):
1926781
PAR ID:
10359596
Author(s) / Creator(s):
; ; ;
Editor(s):
You, Lingchong
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
16
Issue:
11
ISSN:
1553-7358
Page Range / eLocation ID:
e1008435
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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