用脉冲神经网络实现关节状态稳定估计，适合资源受限的机器人系统。
Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation
- 基于脉冲环吸引子，通过局部兴奋和广域抑制形成稳定活动峰。
- 在关节极限附近仍保持低漂移，精度优于无边界限制模型。
- 硬件友好且稳定持续数秒，速度与突触调制呈近线性关系。
维持连续变量的稳定内部表征对有效机器人控制至关重要。连续吸引子网络提供了一种生物启发机制来编码此类变量，但神经形态实现很少在资源受限条件下解决本体感觉估计问题。本文提出一种脉冲环吸引子网络，通过自持的群体活动表示机器人关节角度。局部兴奋与广域抑制支持稳定的活动峰，速度调制的不对称性驱动其移动，边界条件将运动限制在机械极限内。该网络重现了平滑轨迹跟踪，在关节极限附近保持稳定，相比无界模型表现出更低的漂移和更高的精度。这种紧凑的硬件兼容实现展现了多秒级别的稳定性，活动峰速度与突触调制之间存在近似线性关系。
原文摘要 · Abstract (English)
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.
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