This paper introduces the ``rebound Winner-Take-All (RWTA)" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winner-take-all state machines and the continuous tuning capabilities of excitable biophysical circuits. The proposed event-based framework addresses continuous rhythmic generation and discrete decision-making in a unified physical modeling language. We illustrate the versatility, robustness, and modularity of the architecture through the nervous system design of a snake robot.
@article{arxiv.2511.11924,
title = {A Neuromorphic Architecture for Scalable Event-Based Control},
author = {Yongkang Huo and Fulvio Forni and Rodolphe Sepulchre},
journal= {arXiv preprint arXiv:2511.11924},
year = {2026}
}