Can modular strategies simplify neural control of multidirectional human locomotion?

Each human lower-limb contains over 50 muscles that are coordinated during locomotion. It has been hypothesized that the nervous system simplifies muscle control through modularity, using neural patterns to activate muscles in groups called synergies. Here we investigate how simple modular controllers based on invariant neural primitives (synergies or patterns) might generate muscle activity observed during multidirectional locomotion. We extracted neural primitives from unilateral electromyographic recordings of 25 lower-limb muscles during five locomotor tasks, walking forwards, backwards, leftwards, rightwards and stepping in place. A subset of subjects also performed five variations of forward (unidirectional) walking: self-selected cadence, fast cadence, slow cadence, tiptoe and uphill (20% incline). We assessed the results in the context of dimensionality reduction, defined here as the number of neural signals needing to be controlled. For individual tasks we found that modular architectures could theoretically reduce dimensionality compared to independent muscle control, but we also observed trade-offs for each strategy. Specifically, we found that modular strategies relying on neural primitives shared across different tasks were limited in their ability to account for muscle activations during multi- and uni-directional locomotion. The utility of shared primitives may thus depend on if they can be adapted for specific task demands, for instance, by means of sensory feedback or by being embedded within a more complex sensorimotor controller. Our findings indicate the need for more sophisticated formulations of modular control or alternative motor control hypotheses in order to understand muscle coordination during locomotion.