Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation

TL;DR

This paper presents a neuromorphic spiking ring-attractor network for stable proprioceptive joint-state estimation, demonstrating improved accuracy and stability in robotic control under resource constraints.

Contribution

It introduces a biologically inspired, hardware-compatible ring-attractor model that effectively encodes joint angles with stable, drift-reduced activity.

Findings

Reproduces smooth joint trajectory tracking

Remains stable near joint limits

Shows near-linear relationship between bump velocity and synaptic modulation

Abstract

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.