Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide a biologically inspired mechanism for encoding such variables, yet neuromorphic realizations have rarely addressed proprioceptive estimation under resource constraints. This work introduces a spiking ring-attractor network representing a robot joint angle through self-sustaining population activity. Local excitation and broad inhibition support a stable activity bump, while velocity-modulated asymmetries drive its translation and boundary conditions confine motion within mechanical limits. The network reproduces smooth trajectory tracking and remains stable near joint limits, showing reduced drift and improved accuracy compared to unbounded models. Such compact hardware-compatible implementation preserves multi-second stability demonstrating a near-linear relationship between bump velocity and synaptic modulation.

翻译：保持连续变量的稳定内部表征是实现有效机器人控制的基础。连续吸引子网络为编码此类变量提供了一种生物启发机制，然而基于神经形态的实现鲜少涉及资源约束下的本体感估计问题。本文提出一种脉冲环吸引子网络，通过自持的群体活动表征机器人关节角度。局部兴奋与广泛抑制机制支撑稳定的活动波包，而速度调制的非对称性驱动波包平移，边界条件则将其运动限制在机械限位内。该网络可复现平滑轨迹追踪，在关节限位附近仍保持稳定，与无边界模型相比，漂移减少且精度提升。这种紧凑型硬件兼容实现可维持多秒稳定性，并展现出波包速度与突触调制之间的近线性关系。