RT Journal Article
SR Electronic
T1 The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.08.03.454908
DO 10.1101/2021.08.03.454908
A1 Bräcklein, Mario
A1 Ibáñez, Jaime
A1 Barsakcioglu, Deren Y
A1 Eden, Jonathan
A1 Burdet, Etienne
A1 Mehring, Carsten
A1 Farina, Dario
YR 2021
UL http://biorxiv.org/content/early/2021/08/04/2021.08.03.454908.abstract
AB Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.Competing Interest StatementDF and DYB are inventors in a patent (Neural 690 Interface. UK Patent application no. GB1813762.0. August 23, 2018) and DF, DYB, JI, and MB are inventors in a patent application (Neural interface. UK Patent application no. GB2014671.8. September 17, 2020) related to the methods and applications of this work.