Insects use an estimate of the angular speed of the visual image across the eye (termed optic flow) for a wide variety of behaviors including flight speed control, visual navigation, depth estimation, grazing landings, and visual odometry. Despite the behavioral importance of visual speed estimation, the neuronal mechanisms by which the brain extracts optic flow information from the retinal image remain unknown. This dissertation investigates the underlying neuronal mechanisms of visual speed estimation via three complementary strategies: the development of neuronally-based computational models, testing of the models in a behavioral simulation framework, and behavioral experiments using bumblebees. Using these methods I demonstrate the sufficiency of two non-directional models of motion detection for reproducing real-world, speed dependent behaviors, propose potential neuronal circuits by which these models may be physiologically implemented, and predict the expected responses of these neurons to a range of visual stimuli.
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