Center for Neurotechnology, Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, United States.
Curr Opin Neurobiol. 2019 Apr;55:142-151. doi: 10.1016/j.conb.2019.03.008. Epub 2019 Apr 4.
The field of brain-computer interfaces is poised to advance from the traditional goal of controlling prosthetic devices using brain signals to combining neural decoding and encoding within a single neuroprosthetic device. Such a device acts as a 'co-processor' for the brain, with applications ranging from inducing Hebbian plasticity for rehabilitation after brain injury to reanimating paralyzed limbs and enhancing memory. We review recent progress in simultaneous decoding and encoding for closed-loop control and plasticity induction. To address the challenge of multi-channel decoding and encoding, we introduce a unifying framework for developing brain co-processors based on artificial neural networks and deep learning. These 'neural co-processors' can be used to jointly optimize cost functions with the nervous system to achieve desired behaviors ranging from targeted neuro-rehabilitation to augmentation of brain function.
脑机接口领域正准备从使用脑信号控制假肢设备的传统目标推进到在单个神经假肢设备中结合神经解码和编码。这样的设备充当大脑的“协处理器”，其应用范围从诱导脑损伤后的赫布可塑性到重新激活瘫痪的四肢和增强记忆。我们回顾了用于闭环控制和可塑性诱导的同时解码和编码的最新进展。为了解决多通道解码和编码的挑战，我们引入了一个基于人工神经网络和深度学习的开发大脑协处理器的统一框架。这些“神经协处理器”可用于与神经系统联合优化成本函数，以实现从靶向神经康复到大脑功能增强等各种期望行为。
