Recent studies have suggested that the central nervous system generates movements by controlling groups of motor neurons (synergies) that do not always align with muscle anatomy. In this study, we determined whether these synergies are robust across tasks with different mechanical constraints. We identified motor neuron synergies using principal component analysis (PCA) and cross-correlations between smoothed discharge rates of motor neurons. In Part 1, we used simulations to validate these methods. The results suggested that PCA can accurately identity the number of common inputs and classify motor neurons according to the synaptic weights of the common inputs they receive. Moreover, the results confirmed that cross-correlation can separate pairs of motor neurons that receive common inputs from those that do not receive common inputs. In Part 2, sixteen individuals performed plantarflexion at three ankle angles while recording high-density surface electromyography from the gastrocnemius lateralis (GL) and medialis (GM) and the soleus (SOL) muscles. We identified and tracked the same motor units across angles. PCA revealed two motor neuron synergies, primarily grouping motor neurons innervating GL-SOL and GM-SOL. These motor neuron synergies were relatively stable with 74.0% of motor neurons classified in the same synergy across angles. Cross-correlation demonstrated that only 13.9% of pairs of motor neurons maintained a non-significant level of correlation across angles, confirming the large presence of common inputs. Overall, these results highlighted the modularity of movement control at the motor neuron level, which may ensure a sensible reduction of computational resources for movement control.

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Objective: Previous studies have demonstrated promising results in estimating the neural drive to muscles, the net output of all motoneurons that innervate the muscle, using high-density electromyography (HD-EMG) for the purpose of interfacing with assistive technologies. Despite the high estimation accuracy, current methods based on neural networks need to be trained with specific motor unit action potential (MUAP) shapes updated for each condition (i.e., varying muscle contraction intensities or joint angles). This preliminary step dramatically limits the potential generalization of these algorithms across tasks. We propose a novel approach to estimate the neural drive using a deep convolutional neural network (CNN), which can identify the cumulative spike train (CST) through general features of MUAPs from a pool of motor units. Methods: We recorded HD-EMG signals from the gastrocnemius medialis muscle under three isometric contraction scenarios: 1) trapezoidal contraction tasks with different intensities, 2) contraction tasks with a trapezoidal or sinusoidal torque target, and 3) trapezoidal contraction tasks at different ankle angles. We applied a convolutive blind source separation (BSS) method to decompose HD-EMG signals to CST and segmented both signals into windows to train and validate the deep CNN. Then, we optimized the structure of the deep CNN and validated its generalizability across contraction tasks within each scenario. Results: With the