Embodied sensorimotor control: computational modeling of the neural control of movement

How do distributed neural circuits drive purposeful movements from the complex musculoskeletal system? This characterization is critical towards not just furthering our understanding of the generation of movement, but, importantly, guiding us towards therapeutic targets for diseases affecting motor control. The neural processes leading to movements have been relatively well posited and understood due to the quantitative nature of the behavioral outputs involved. Classic approaches have largely focused on optimization principles, including limb control, to achieve human-like behavioral trajectories. These largely theoretical models of sensorimotor control can recapitulate observed movement trajectories by hypothesizing the presence of a controller guiding the complex movements. However, these models cannot predict how neuronal populations in each brain region affects the resulting movement and vice-versa. On the other hand, breakneck advances in hardware techniques have led to vast improvements in our ability to record large-scale multi-regional neural data. These recordings have enabled dimensionality reduction and modeling techniques to elucidate the structure in high-dimensional neural activity during different conditions, and relate the neural activity directly to kinematic outcomes. However, these data-driven models typically do not consider the biophysical underpinnings of the musculoskeletal system, and thus fail to elucidate the computational role of neural activity in driving the musculoskeletal system such that the body reaches a desired state.