用神经形态架构实现可扩展的事件驱动控制，兼具离散与连续计算优势。
A Neuromorphic Architecture for Scalable Event-Based Control
- 提出基于'反弹竞争淘汰'的神经形态核心单元，融合离散与连续计算能力
- 在蛇形机器人神经系统中验证了架构的多功能性、鲁棒性和模块化特性
- 适合需要实时响应和低功耗的智能控制系统设计者参考
本文提出一种名为'反弹竞争淘汰（RWTA）'的神经形态基本单元，构成可扩展的神经形态控制架构。从细胞级到系统级，该架构结合了离散计算的可靠性与连续调节的可调性：既继承了竞争淘汰状态机的离散计算能力，又具备兴奋性生物物理电路的连续调制能力。所提出的事件驱动框架以统一的物理建模语言处理连续节律生成与离散决策。通过蛇形机器人的神经系统设计，展示了该架构的多功能性、鲁棒性和模块化优势。
原文摘要 · Abstract (English)
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.
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