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[2511.11924] A Neuromorphic Architecture for Scalable Event-Based Control
Summary
This paper presents a neuromorphic architecture for scalable event-based control, leveraging the rebound Winner-Take-All motif to integrate discrete computation and continuous regulation, illustrated through a snake robot design.
Why It Matters
The proposed architecture offers a novel approach to event-based control systems, enhancing the reliability and tunability of robotic applications. By bridging discrete and continuous processes, it paves the way for more sophisticated AI and robotics solutions, which are crucial for advancing automation and intelligent systems.
Key Takeaways
- Introduces the rebound Winner-Take-All motif for neuromorphic control.
- Combines discrete computation with continuous regulation for enhanced performance.
- Demonstrates versatility through the design of a snake robot.
Computer Science > Artificial Intelligence arXiv:2511.11924 (cs) [Submitted on 14 Nov 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:A Neuromorphic Architecture for Scalable Event-Based Control Authors:Yongkang Huo, Fulvio Forni, Rodolphe Sepulchre View a PDF of the paper titled A Neuromorphic Architecture for Scalable Event-Based Control, by Yongkang Huo and 2 other authors View PDF HTML (experimental) 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2511.11924 [cs.AI] (or arXiv:2511.11924v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2511.11924 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yongkang Huo [view email] [v1] Fri, 14 Nov 2025 23:08:56 UTC (12,826 KB) [v2] Fri, 2...
