English

On graphical domination for threshold-linear networks with recurrent excitation and global inhibition

Neurons and Cognition 2025-10-07 v1

Abstract

Graphical domination was first introduced in [1] in the context of combinatorial threshold-linear networks (CTLNs). There it was shown that when a domination relationship exists between a pair of vertices in a graph, certain fixed points in the corresponding CTLN can be ruled out. Here we prove two new theorems about graphical domination, and show that they apply to a significantly more general class of recurrent networks called generalized CTLNs (gCTLNs). Theorem 1 establishes that if a dominated node is removed from a network, the reduced network has exactly the same fixed points. Theorem 2 tells us that by iteratively removing dominated nodes from an initial graph GG, the final (irreducible) graph G~\widetilde{G} is unique. We also introduce another new family of TLNs, called E-I TLNs, consisting of nn excitatory nodes and a single inhibitory node providing global inhibition. We provide a concrete mapping between the parameters of gCTLNs and E-I TLNs built from the same graph such that corresponding networks have the same fixed points. We also show that Theorems 1 and 2 apply equally well to E-I TLNs, and that the dynamics of gCTLNs and E-I TLNs with the same underlying graph GG exhibit similar behavior that is well predicted by the fixed points of the reduced graph G~\widetilde{G}.

Keywords

Cite

@article{arxiv.2510.05098,
  title  = {On graphical domination for threshold-linear networks with recurrent excitation and global inhibition},
  author = {Carina Curto},
  journal= {arXiv preprint arXiv:2510.05098},
  year   = {2025}
}

Comments

25 pages, 8 figures