Inferring a network from dynamical signals at its nodes
Biological Physics
2021-01-27 v3 Data Analysis, Statistics and Probability
Neurons and Cognition
Quantitative Methods
Abstract
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.
Cite
@article{arxiv.2004.02318,
title = {Inferring a network from dynamical signals at its nodes},
author = {Corey Weistuch and Luca Agozzino and Lilianne R. Mujica-Parodi and Ken A. Dill},
journal= {arXiv preprint arXiv:2004.02318},
year = {2021}
}