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Understanding flexibility in the neural control of movement requires identifying the distribution of common inputs to the motor units. In this study, we identified large samples of motor units from two lower limb muscles: the vastus lateralis (VL; up to 60 motor units/participant) and the gastrocnemius medialis (GM; up to 67 motor units/participant). First, we applied a linear dimensionality reduction method to assess the dimensionality of the manifolds underlying the motor unit activity. We subsequently investigated the flexibility in motor unit control under two conditions: sinusoidal contractions with torque feedback, and online control with visual feedback on motor unit firing rates. Overall, we found that the activity of GM motor units was effectively captured by a single latent factor defining a unidimensional manifold, whereas the VL motor units were better represented by three latent factors defining a multidimensional manifold. Despite this difference in dimensionality, the recruitment of motor units in the two muscles exhibited similarly low levels of flexibility. Using a spiking network model, we tested the hypothesis that dimensionality derived from factorization does not solely represent descending cortical commands but is also influenced by spinal circuitry. We demonstrated that a heterogeneous distribution of inputs to motor units, or specific configurations of recurrent inhibitory circuits, could produce a multidimensional manifold. This study clarifies an important debated issue, demonstrating that while motor unit firings of a non-compartmentalised muscle can lie in a multidimensional manifold, the central nervous system may still have limited capacity for flexible control of these units.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
* We expanded the scope of the modelling to include alternative organisations of excitatory and inhibitory inputs. To achieve this, we updated the modelling approach to allow independent adjustment of inhibitory and excitatory synaptic inputs, each with distinct conductance properties. While being more complex, this revised model is more appropriate for running new simulation scenarios. For transparency and reproducibility, the full code is publicly available.
* https://github.com/FrancoisDernoncourt/Motor_unit_flexibility
* https://figshare.com/s/45cecab12ec45658ef83