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arXiv · 2511.02554

Reliability entails input-selective contraction and regulation in excitable networks

Michelangelo Bin·Alessandro Cecconi·Lorenzo Marconi
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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.

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Michelangelo Bin, Alessandro Cecconi, Lorenzo Marconi. 2025-11-04. Reliability entails input-selective contraction and regulation in excitable networks. https://arxiv.org/abs/2511.02554

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