# Inferring a network from dynamical signals at its nodes

arXiv: https://arxiv.org/abs/2004.02318 · HTML: https://mlsanity.com/paper/2004.02318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Backpropagation on Dynamical Networks](https://mlsanity.com/paper/2207.03093/) (2022)

## Abstract (arXiv)

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.