Muscle synergies have been proposed as a solution to control redundant systems by coordinating a number of muscles with a smaller number of control modules, which are called muscle synergies ( Tresch et al., 1999 Bizzi et al., 2002 d’Avella et al., 2003). Understanding how the central nervous system (CNS) coordinates the redundant musculoskeletal system is a central question of motor control. Nevertheless, we show a highly stereotyped movement trajectory and agonist-antagonist muscle activity patterns ( Morasso, 1981). For example, to reach for a coffee cup on a desk, there are an infinite number of patterns of muscle activity involved in extending the arm because multiple muscles span the same joint. Our body is remarkably complex, yet we display a highly stable motor performance. These results suggest that a spinal population with moderate variation is a biologically plausible model for the neural implementation of muscle synergies. Furthermore, the size of the spinal variation of the population synergy matched well with the variation in spinal PreM-INs recorded in monkeys. We found that the simple and population synergy models emulate the robustness of muscle synergies against cortical stroke observed in human stroke patients. We examined three neural network models: one with random connections (non-synergy model), one with a small number of spinal synergies (simple synergy model), and one with a large number of spinal neurons representing muscle synergies with a certain variation (population synergy model). Here we compared neural network models for muscle synergies to seek a biologically plausible model that reconciles previous clinical and electrophysiological findings. Converging evidence suggests that output projections of the spinal premotor interneurons (PreM-INs) underlie the formation of muscle synergies, but they exhibit a substantial variation across neurons and exclude standard models assuming a small number of unitary “modules” in the spinal cord. Muscle synergies have been proposed as functional modules to simplify the complexity of body motor control however, their neural implementation is still unclear.
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