Reliability entails input-selective contraction and regulation in excitable networks
Abstract
The animal nervous system offers a model of computation combining digital reliability and analog efficiency. Understanding how this sweet spot can be realized is a core question of neuromorphic engineering. To this aim, this paper explores the connection between reliability, contraction, and regulation in excitable systems. Using the FitzHugh-Nagumo model of excitable behavior as a proof-of-concept, it is shown that neuronal reliability can be formalized as an average trajectory contraction property induced by the input. In excitable networks, reliability is shown to enable regulation of the network to a robustly stable steady state. It is thus posited that regulation provides a notion of dynamical analog computation, and that stability makes such a computation model robust.
Keywords
Cite
@article{arxiv.2511.02554,
title = {Reliability entails input-selective contraction and regulation in excitable networks},
author = {Michelangelo Bin and Alessandro Cecconi and Lorenzo Marconi},
journal= {arXiv preprint arXiv:2511.02554},
year = {2026}
}