TY - JOUR
T1 - Inferring a network from dynamical signals at its nodes
AU - Weistuch, Corey
AU - Agozzino, Luca
AU - Dill, Ken A.
AU - Mujica-Parodi, Lilianne R.
N1 - Publisher Copyright:
© 2020 Weistuch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2020/11/30
Y1 - 2020/11/30
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85097167984
U2 - 10.1371/journal.pcbi.1008435
DO - 10.1371/journal.pcbi.1008435
M3 - Article
C2 - 33253160
AN - SCOPUS:85097167984
SN - 1553-734X
VL - 16
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 11
M1 - e1008435
ER -