Task-level Models for Image-stabilization Behaviors in Animals

This research addresses a fundamental question in biology and neuroscience: how do animals process sensory information for the control of locomotor behaviors? Behaviors can be described as a sensorimotor loop: sensing (sensori-) governs action (-motor), action changes the environment, and these changes are perceived via sensing. Animal behavior arises from a concert of sensory, computational, and mechanical systems. Often, these mechanisms are studied independently (and often isolated from the context of the behavior) and the behavioral model is constructed from knowledge of the constituents, a bottom-up synthesis. Complementary to this approach, we model behavior at the level of the sensorimotor loop (the task-level) and subsequently generate hypotheses as to the mechanistic constituents. These top-down models serve to constrain permissible mechanisms and identify necessary neural computations. We design an assay of experiments and frequency-domain analyses to identify task-level behavioral models, specifically for image-stabilization behaviors. Imagestabilization describes a broad class of behaviors in which animals modulate movement to fixate a sensory signal. In this dissertation, we study analogous behaviors in two ii