Title: “The Role of Uncertainty in the Vestibular Control of Locomotion”
Supervisor: Dr. Jean-Sébastien Blouin
Committee members: Dr. Romeo Chua, Dr. Calvin Kuo
Abstract: Uncertainty is present in all the inferences we make about the world and our orientation within it. When sensory or motor noise increases, confidence in our belief about our environment and self-motion worsens. All sensory signals we receive and muscle commands we generate contribute to this uncertainty due to the inherent noisiness of the neural pathways, synapses, receptors, and muscles. This includes the vestibular system, which detects head motion in space and is involved in maintaining balance during locomotion. Clinical and experimental studies have shown that as locomotor speed increases, the vestibular contributions to balance decrease. Although uncertainties in vestibular encoding and motor signals driving locomotion likely contribute to this modulation, there are differing theories about the mechanism behind this phenomenon. Based on a recent model of vestibular processing, I propose that the magnitude and recent history of sensed head acceleration variability regulate the vestibular contribution to balance during locomotion. To test this hypothesis, I will characterize how the vestibular control of locomotion changes during transitions between different locomotor cadences. Transitions involve a change in the statistics of head motion, and the rate of change in vestibular control will elucidate the sensorimotor integration mechanisms underlying the vestibular control of locomotion. Under the assumption that vestibular uncertainty is dynamically modulated, I expect that the vestibular control of locomotion will gradually increase over a 20-30 second period following a transition to a slower locomotion speed. The results from this project will be crucial for determining the role of vestibular uncertainty for sensorimotor processing in the central nervous system. In addition to its direct impact for developing computational models and biomimetic robotic controllers, the proposed framework will be transformative for understanding how sensory processing is impaired in clinical conditions.