论文标题
用于植入式脑机界面的手指速度解码的节能尖峰神经网络
An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface
论文作者
论文摘要
脑机界面（BMI）对于运动康复和迁移率增强是有希望的。要实现可植入的BMI系统，需要高准确性和低功率算法。在本文中，我们提出了一个新型的尖峰神经网络（SNN）解码器，用于植入BMI回归任务。 SNN经过增强的时空反向传播训练，以充分利用其处理时间问题的能力。所提出的SNN解码器达到的相关系数与离线手指速度解码任务中最新的ANN解码器相同，而仅需要6.8％的计算操作和9.4％的内存访问。
Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation. High-accuracy and low-power algorithms are required to achieve implantable BMI systems. In this paper, we propose a novel spiking neural network (SNN) decoder for implantable BMI regression tasks. The SNN is trained with enhanced spatio-temporal backpropagation to fully leverage its ability in handling temporal problems. The proposed SNN decoder achieves the same level of correlation coefficient as the state-of-the-art ANN decoder in offline finger velocity decoding tasks, while it requires only 6.8% of the computation operations and 9.4% of the memory access.
