论文标题
带有嵌入式机器学习的闭环神经界面
Closed-Loop Neural Interfaces with Embedded Machine Learning
论文作者
论文摘要
能够多部电气记录，现场信号分类和闭环治疗的神经界面对于诊断和治疗神经系统疾病至关重要。但是，鉴于此类设备对计算和内存资源的严格限制，在低功耗神经设备上部署机器学习算法具有挑战性。在本文中，我们回顾了在神经界面中嵌入机器学习的最新发展，重点是设计权衡和硬件效率。我们还提出了优化的基于树的模型，用于对脑植入物中神经信号的低功率和记忆有效分类。使用能量感知的学习和模型压缩，我们表明所提出的倾斜树可以在诸如癫痫发作或震颤检测和运动解码等应用中的常规机器学习模型。
Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders. However, deploying machine learning algorithms on low-power neural devices is challenging, given the tight constraints on computational and memory resources for such devices. In this paper, we review the recent developments in embedding machine learning in neural interfaces, with a focus on design trade-offs and hardware efficiency. We also present our optimized tree-based model for low-power and memory-efficient classification of neural signal in brain implants. Using energy-aware learning and model compression, we show that the proposed oblique trees can outperform conventional machine learning models in applications such as seizure or tremor detection and motor decoding.
