Nervous systems sense, communicate, compute, and actuate movement using distributed components with trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, the resulting control can achieve remarkably fast, accurate, and robust performance due to a highly effective layered control architecture. However, there is no theory explaining the effectiveness of layered control architectures that connects speed-accuracy trade-offs (SATs) in neurophysiology to the resulting SATs in sensorimotor control. In this paper, we introduce a theoretical framework that provides a synthetic perspective to explain why there exists extreme diversity across layers and within levels. This framework characterizes how the sensorimotor control SATs are constrained by the hardware SATs of neurons communicating with spikes and their sensory and muscle endpoints, in both stochastic and deterministic models. The theoretical predictions of the model are experimentally confirmed using driving experiments in which the time delays and accuracy of the control input from the wheel are varied. These results show that the appropriate diversity in the properties of neurons and muscles across layers and within levels help create systems that are both fast and accurate despite being built from components that are individually slow or inaccurate. This novel concept, which we call "diversity-enabled sweet spots" (DESSs), explains the ubiquity of heterogeneity in the sizes of axons within a nerve as well the resulting superior performance of sensorimotor control. [Link to the paper]
