Dorsal Column Nuclei Neural Signal Features Permit Robust Machine-Learning of Natural Tactile- and Proprioception-Dominated Stimuli - Loutit, Alastair J.; Potas, Jason R. (Frontiers in Systems Neuroscience, 2020)
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, 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 in urethane-anaesthetised rats. The highest performing individual feature, spike amplitude, classified somatosensory dorsal column nuclei signals with 70% accuracy. The highest accuracy achieved was 87% using thirteen features that were extracted from both high and low-frequency bands of dorsal column nuclei signals. In general, high-frequency 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 low-frequency features to the feature set. We found that proprioception-dominated stimuli generalise 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 dorsal column nuclei 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.