Inferring a network from dynamical signals at its nodes.
- Author(s): Weistuch, Corey
- Agozzino, Luca
- Mujica-Parodi, Lilianne R
- Dill, Ken A
- et al.
Published Web Locationhttps://doi.org/10.1371/journal.pcbi.1008435
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.