Ph.D. Thesis Defense
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, we have not paid much attention to this architecture because of the lack of theory that connects speed-accuracy trade-offs (SATs) in neurophysiology and sensorimotor control. In my talk, we present a theoretical framework that provides a holistic perspective of both levels. We then use this framework to clarify the properties of effective layered architectures and explain why there exists extreme diversity across layers (planning versus reaction layers) and within levels (sensorimotor versus neural hardware levels). The framework characterizes how the sensorimotor SATs are constrained by the component SATs of neurons communicating with spikes and their sensory and muscle endpoints, in both stochastic and deterministic models. The theoretical predictions are also verified using driving experiments. Our results lead to a novel concept, termed ``diversity sweet spots (DSSs)'': the appropriate diversity in the properties of neurons and muscles across levels and within levels help create systems that are both fast and accurate despite being built from components that are individually slow or inaccurate. DSSs explain the necessity of the heterogeneity in the sizes of axons observed in neurophysiology as well as the resulting performance heterogeneity in sensorimotor control.