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.

中文翻译：

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面向大脑的神经协处理器：在脑机接口中结合解码和编码。

脑机接口领域有望从使用脑信号控制假体设备的传统目标发展到在单个神经假体设备中结合神经解码和编码的传统目标。这种设备可作为大脑的“协处理器”，其应用范围从诱导脑外伤后的Hebbian可塑性恢复到恢复瘫痪的肢体并增强记忆力。我们回顾了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，我们引入了一个统一的框架，用于开发基于人工神经网络和深度学习的脑协处理器。这些“神经协处理器”