发表机构
TU Dresden（德累斯顿工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对因残疾等行动不便患者，提出基于事件的神经解码方法，用基于事件的门控循环单元生成稀疏通信模式，经有效训练方法和稀疏推理，在任务性能上超越经典脉冲神经网络，为设备上神经解码带来新机会。
AI 中文摘要
大量患者因残疾、疾病或事故而行动不便。尽管由深度神经网络驱动的现代假肢有望显著提高这些人的生活质量，但其广泛应用受到显著延迟、能量消耗和空间要求的阻碍。有线连接限制患者移动性，无线连接限制信息传输量。脉冲神经网络虽有潜力，但在各种应用中常落后于深度学习模型。本研究提出一种高效神经解码方法，基于事件的门控循环单元生成稀疏通信模式，通过有效训练方法和稀疏推理，在任务性能上超越经典脉冲神经网络，为设备上的神经解码带来新机遇。
英文摘要
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.
Journal refK. K. Nazeer, S. Arfa, M. Jobst, R. George and C. Mayr, "Event-based Neural Decoding for Neuroprosthetic Motor Control," 2025 IEEE Biomedical Circuits and Systems Conference (BioCAS), Abu Dhabi, United Arab Emirates, 2025, pp. 334-338
DOI：10.1109/BioCAS67066.2025.00079
