Abstract
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.
Highlights
IntroductionNeural prostheses enable users to control robotic limbs, computer cursors, or even effect movement of the users own limbs, by translating brain signals into movement control signals (Ethier et al, 2012;Hochberg et al, 2012;Collinger et al, 2013;Gilja et al, 2015;Jarosiewicz et al, 2015;Bouton et al, 2016;Capogrosso et al, 2016;Flesher et al, 2016;Ajiboye et al, 2017)
Motor control performance can be significantly improved by restoring somatosensory feedback that rapidly updates limb status, and by improved neural decoding of motor control-related brain signals
Feature-learnability enables us to assess the information contained in dorsal column nuclei (DCN) surface potentials for decoding natural tactile- and proprioceptive-dominated somatosensory events
Summary
Neural prostheses enable users to control robotic limbs, computer cursors, or even effect movement of the users own limbs, by translating brain signals into movement control signals (Ethier et al, 2012;Hochberg et al, 2012;Collinger et al, 2013;Gilja et al, 2015;Jarosiewicz et al, 2015;Bouton et al, 2016;Capogrosso et al, 2016;Flesher et al, 2016;Ajiboye et al, 2017). We previously devised a metric for quantifying the relevance of neural signal features to the stimulus from which they were evoked, which we termed feature-learnability (Loutit et al, 2019). This approach uses a simple feed-forward back-propagation supervised artificial neural network (ANN), which has several advantages over the use of state-of-the-art deep neural network (DNN) architectures (Pandarinath et al, 2018): An ANN enables the quantification of information content from signal features of interest, that, for example, may be selected on the basis of: i) their neurophysiological relevance, ii) their capacity to mimic or inform electrical stimulation in sensory applications, and/or iii) are commonly used for decoding neural signals. ANNs can perform excellent classification accuracy with significantly smaller data sets compared to DNNs
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