Neuromorphic Spiking Ring Attractor for Proprioceptive Joint-State Estimation

2026-04-15

AI Summary

This work proposes a spiking ring attractor network–based proprioceptive method for stable, continuous estimation of robotic joint states under resource-constrained conditions. The network maintains a single activity bump through local excitation and global inhibition to represent joint angle, while velocity-modulated synaptic asymmetry drives bump displacement. Boundary constraints are explicitly embedded to respect mechanical joint limits. To the best of our knowledge, this is the first implementation of a compact, low-drift spiking ring attractor on neuromorphic hardware that natively supports boundary conditions, balancing biological plausibility with engineering practicality. Experiments demonstrate smooth trajectory tracking, stability near joint limits, sustained operation over multiple seconds, and an approximately linear relationship between bump velocity and synaptic modulation—collectively yielding significantly reduced estimation drift.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: State Estimation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• neuromorphic
• proprioceptive estimation
• spiking neural network
• ring attractor
• joint-state estimation

Innovation

• spiking ring attractor
• proprioceptive estimation
• neuromorphic computing
• continuous attractor network
• joint-state estimation