The neurosciences provide rich, diverse details on how humans sense/communicate/compute/actuate movement using efficient, distributed hardware with tradeoffs in sparsity, quantization, noise, delays, and saturation throughout. These processes are implemented in highly-layered architectures involving high-level goals/plans/decisions and low-level sensing/reflex/action to facilitate robust control. Missing is an integrative view that connects component-level tradeoffs/constraints with sensorimotor performance and effective architectures. In this talk, we briefly review essential neuroscience motivation, emphasizing speed/accuracy tradeoffs (SATs). SATs are among the most extensively studied and ubiquitous tradeoffs in both neurophysiology and sensorimotor control literature. We model the component SATs in spiking neuron communication and their sensory and muscle endpoints. We then provide both stochastic and deterministic frameworks that yield tight analytic bounds on how component SATs impose sensorimotor control SATs. From the resulting optimal control policies, we clarify the benefit of layering and heterogeneities in neurons, muscles, and sensorimotor control loops. We also briefly sketch our new experimental platforms and experiments that illustrate the theory and highlight tradeoffs and layering. Finally, we will discuss how the successful architectures extend downward into the cellular level as well as outward in our most advanced technologies: to cleverly combine diverse components to create efficient systems that are both fast and accurate, despite being built from parts that are not.
Yorie Nakahira is a Ph.D. student in John Doyle's group, California Institute of Technology. Her primary research interests are control and information theory with applications to neuroscience and biology.