Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

Abstract

<jats:p>We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.</jats:p>

Task space exploration improves adaptation after incompatible virtual surgeries

“…After compatible virtual surgeries (like in visuomotor rotations as they can be seen as a type of compatible perturbation), cursor trajectories that can be generated within the know repertoire of motor commands -- by recombining existing muscle synergies -- provide information about the novel mapping between motor commands and task space and the inaccurate cursor trajectories can thereby be used to improve performance in the following trials by calculating the error between the estimated trajectory and the actual trajectory. More specifically, in a synergy-based controller (Hagio and Kouzaki 2018), a compatible virtual surgery can be overcome by using existing muscle synergies and associating new synergy combinations to the movement targets. The hypothesis is that a new mapping of visual targets to synergy coefficients is learned and that such adaptive process occurs faster than the learning of new synergies.…”

The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis

“…This result is consistent with the experiments by Berger et al [4] that indicate that it is more difficult (i.e., requires more trials and time) to learn movements that are incompatible with existing experimentallydetermined muscle synergies than to learn movements that are consistent with measured synergies. Hagio et al [24] previously showed that modularity speeds up motor adaptation to rotational perturbations in an isometric force production task. We extend their result by studying reaching movements and by studying the effects of modularity on motor learning, not just adaptation.…”

The Effects of Motor Modularity on Performance, Learning, and Generalizability in Upper-Extremity Reaching: a Computational Analysis

“…Experiments by Berger et al [5] indicate that it is more difficult (i.e., requires more trials and time) to learn movements that are incompatible with existing experimentally-determined muscle synergies than to learn movements that are consistent with measured synergies. In a computational study, Hagio et al [24] showed that modularity speeds up motor adaptation to rotational perturbations in an isometric force production task. We extend these results by studying reaching movements and by studying the effects of modularity on motor learning, not just adaptation.…”

“…In a computational study, Hagio et al [24] showed that modularity speeds up motor adaptation to rotational perturbations in an isometric force production task. In Berger et al [5], after a virtual tendon rearrangement surgery, subjects had difficulty adapting to perturbations that were incompatible with synergies identified pre-virtual-surgery, whereas they quickly adapted to perturbations compatible with the synergies.…”