Department of Neurobiology and Duke University Center for Neuroengineering, Duke University, Durham, NC 27710, U.S.A.
Departments of Information and Communication Technologies and Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, 08018, Spain; and Department of Mechanical and Process Engineering, ETH Zurich, 8092 Zurich, Switzerland
Neural Comput. 2019 Jun;31(6):1085-1113. doi: 10.1162/neco_a_01189. Epub 2019 Apr 12.
Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large ( = 134-402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.
尽管多年来已经提出了许多用于脑机接口 (BMI) 应用的实时神经解码算法，但仍然难以找到一种最优的共识方法。深度学习算法的最新进展为改进 BMI 解码器的设计提供了新的机会，包括使用递归人工神经网络实时解码神经元集合活动。在这里，我们开发了一个长短时记忆 (LSTM) 解码器，用于从猕猴执行运动任务时同时从多个皮质区域中采样的大 ( = 134-402) 个神经元群体的活动中提取运动运动学。记录的区域包括初级运动、背侧运动前区、辅助运动区和初级躯体感觉皮层区。LSTM 保留信息的能力可以持续很长时间，因此可以准确解码需要运动和静止期的任务。我们的 LSTM 算法在三个任务中显著优于最新的无迹卡尔曼滤波器：中心到臂的伸展、双手伸展和在跑步机上的双足行走。值得注意的是，LSTM 单元表现出皮质神经元活动的多种众所周知的生理特征，例如方向调谐和跨任务时段的神经元动态。LSTM 模拟了涉及运动任务的皮质回路的几个关键生理属性。这些发现表明，基于 LSTM 的方法可能为使用 BMI 恢复严重残疾患者运动的神经假体提供更好的算法策略。
