{"ID":2841953,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11924","arxiv_id":"2511.11924","title":"A Neuromorphic Architecture for Scalable Event-Based Control","abstract":"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.","short_abstract":"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 di...","url_abs":"https://arxiv.org/abs/2511.11924","url_pdf":"https://arxiv.org/pdf/2511.11924v2","authors":"[\"Yongkang Huo\",\"Fulvio Forni\",\"Rodolphe Sepulchre\"]","published":"2025-11-14T23:08:56Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}