Positivity sets of hinge functions

In this paper we investigate which subsets of the real plane are realisable as the set of points on which a one-layer ReLU neural network takes a positive value. In the case of cones we give a full characterisation of such sets. Furthermore, we give a necessary condition for any subset of ${\mathbb{R}}^d$. We give various examples of such one-layer neural networks.