用事件驱动的神经网络实现低延迟高精度假肢控制
Event-based Neural Decoding for Neuroprosthetic Motor Control
- 采用事件触发的门控循环单元，生成稀疏且分级的脉冲信号
- 在保持低功耗的同时，性能超越传统脉冲神经网络
- 适合嵌入式设备实时解码，助力可穿戴神经假肢
许多患者因残疾、疾病或事故导致运动能力下降。尽管基于深度神经网络的现代假肢有望显著改善生活质量，但其广泛应用受限于显著的延迟、能耗和空间占用。有线连接至外部高性能处理器限制了患者移动性，无线连接则制约了传输信息量。脉冲神经网络具备压缩通信和低功耗推理的潜力，但在多项应用中仍落后于前沿深度学习模型。本研究提出一种高性能神经解码方法，在任务性能与效率间取得良好平衡。所提事件驱动的门控循环单元生成稀疏的分级脉冲模式，任务性能优于传统脉冲神经网络。结合高效训练与稀疏推理，该模型为设备端神经解码开辟了新路径。
原文摘要 · Abstract (English)
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.
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