Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance decoding performance of neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to neural decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for decoding other (e.g., motor) neural signals.

中文翻译：

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背柱核神经信号特征允许对自然触觉和本体感觉主导的刺激进行稳健的机器学习

神经假体通过将大脑信号转换为运动控制信号，使用户能够通过各种执行器实现运动。然而，为了通过这些设备实现更自然的肢体运动，需要恢复体感反馈。我们使用特征可学习性（一种机器学习方法）来评估信号特征的能力，以增强由自然触觉和本体感觉体感刺激诱发的神经信号的解码性能，这些刺激是从背柱核 (DCN) 表面记录的氨基甲酸乙酯 -麻醉的大鼠。性能最高的单个特征、尖峰幅度、分类的体感 DCN 信号，准确度为 70%。使用从 DCN 信号的高频和低频 (LF) 频带中提取的 13 个特征，实现的最高精度为 87%。一般来说，高频 (HF) 特征包含有关外围体感事件的最多信息，但是当从短时间窗口获取特征时，通过将 LF 特征添加到特征集中，分类精度显着提高。我们发现，本体感觉主导的刺激比触觉主导的刺激在动物中的泛化效果更好，并且我们展示了信号特征的信息如何在动态体感事件的时间过程中对神经解码的变化做出贡献。这些发现可能会为人工刺激的仿生设计提供信息，这些刺激可以激活 DCN 以替代体感反馈。虽然，我们研究了体感结构，但我们研究的特征集也可能证明对解码其他（例如，运动）神经信号有用。