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.

翻译：本文提出“回弹赢者通吃（RWTA）”基元作为可扩展神经形态控制架构的基本单元。从细胞层面到系统层面，该架构融合了离散计算的可靠性与连续调节的可调性：既继承了赢者通吃状态机的离散计算能力，又具备可兴奋生物物理电路的连续调节特性。所提出的事件驱动框架采用统一的物理建模语言，同时处理连续节律生成与离散决策问题。我们通过蛇形机器人神经系统的设计，展示了该架构的多功能性、鲁棒性与模块化特性。