Event-based Neural Decoding for Neuroprosthetic Motor Control

2026-07-13

AI Summary

This work addresses the limitations of neural prosthetic control—namely high latency, excessive power consumption, and communication bandwidth bottlenecks—that hinder device usability and patient mobility. To overcome these challenges, the authors propose an event-driven, efficient neural decoding approach that, for the first time, integrates event-driven gated recurrent units with sparse hierarchical spiking encoding to enable on-chip decoding with minimal communication overhead and low power consumption. By leveraging sparse inference and an efficient training strategy, the method achieves task performance comparable to or better than conventional spiking neural networks while substantially reducing computational and energetic demands. This advancement offers a practical, high-performance deployment solution for wearable neural prosthetics with significantly lower resource requirements.

Abstract

A substantial number of patients experience diminished mobility due to disabilities, diseases, or accidents. Although modern prostheses, powered by deep neural networks, hold the promise of significantly enhancing the quality of life for these individuals, their widespread adoption is hindered by significant latency, energy consumption, and spatial requirements. Wired connections to external high-performance processors restrict patient mobility, while wireless connections limit the volume of information that can be transmitted to these processors. Spiking neural networks offer the potential for compressed communication and low-power inference, yet they often lag behind state-of-the-art deep learning models in various applications. In this study, we propose a high-performance neural decoding method that effectively balances task performance and efficiency. An eventbased gated recurrent unit generates a sparse communication pattern with graded spikes, surpassing classical spiking neural networks in terms of task performance. Utilising an efficient training method and sparse inference, our model presents new opportunities for on-device neural decoding.

Problem

• neuroprosthetic motor control
• latency
• energy consumption
• spiking neural networks
• neural decoding

Innovation

• event-based neural decoding
• spiking neural networks
• graded spikes
• sparse communication
• on-device inference