The volitional control of individual motor units is constrained within low-dimensional neural manifolds by common inputs

Abstract

The implementation of low-dimensional movement control by the central nervous system has been debated for decades. In this study, we investigated the dimensionality of control signals received by spinal motor neurons in controlling one degree of freedom of either the ankle or knee joint. We hypothesized that the central nervous system would mainly adopt a rigid control of motor units; specifically, that the motor units active during these tasks would belong to a small number of synergies, each receiving common descending inputs. This hypothesis was tested using torque-matched isometric contractions, as well as with an operant-conditioning paradigm, where the firing activities of pairs of motor units were provided as visual feedback to the participants. The motor units of the gastrocnemius lateralis could be controlled largely independently from those of the gastrocnemius medialis during ankle plantarflexion. This dissociation of motor unit activity imposed similar behavior to the motor units that were not displayed in the feedback, leading to a two-dimensional control manifold, where each dimension represented a “synergistic” muscle. It was not possible to independently control the motor units within the gastrocnemius medialis muscle. During knee extension tasks, it was not possible to dissociate the activity of the motor units between the vastus lateralis and medialis muscles, which thus belonged to a one-dimensional manifold. Overall, individual motor units were never controlled independently of all others but rather belonged to synergistic groups. These results provide evidence for a synergistic low-dimensional control of motor units constrained by common inputs spanning one or more muscles.

I-Spin live: An open-source software based on blind-source separation for real-time decoding of motor unit activity in humans

“…The activity of motor neurons -in the form of spike trains -represents the neural code of movement. Decoding this firing activity in real-time during various behaviours can thus substantially enhance our understanding of movement control (2)(3)(4)(5). Real-time decoding is also essential for interfacing with external devices (6) or virtual limbs (7) when activity is present at the periphery of the nervous system.…”