Neural Computations Underlying Cancellation of the Vestibular Consequences of Voluntary Movement

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Project Summary: This research program is motivated by three goals. First, we will establish the neural mechanisms that underlie the brain's ability to estimate and cancel self-generated vestibular (inner ear balance) input during active movement. Second, we will determine how the vestibular cerebellum learns to adapt to changes in the relationship between expected and actual sensory input to maintain stabile perception and accurate behavior. Third, we will assess how reward-motivation signals influence circuit performance. The brain's ability to distinguish sensory stimuli that are the result of self-generated (i.e., active) versus unexpected or externally generated (i.e., passive) stimulation is vital to ensuring perceptual stability and accurate motor control. Notably, in the vestibular system, the same central neurons that receive afferent input also send direct projections to motor centers to control balance and posture via the vestibular-spinal reflex. This reflex is essential for providing robust postural responses to unexpected vestibular stimuli, yet is counter- productive when the goal is to make active head movements. Accordingly, it is advantageous to suppress this pathway during active self-motion. Over the past two decades, we have made excellent progress toward identifying where brain makes the distinction between reafferent (i.e., active) and exafferent (i.e., passive) vestibular signals. Specifically, while the responses of vestibular afferents remain robust (and equivalent) regardless of whether stimulation is active or passive, neurons at the next stage of processing in the vestibular nuclei are significantly less responsive to active self-motion. In addition, we have shown that this suppression only occurs when sensory feedback matches that expected based on the motor command (e.g., during normal active movements). In the proposed research, we will address several fundamental questions that remain open regarding the computations that the brain performs to ensure stable perception and accurate motor control during self-motion. First, experiments in Aim 1 will investigate how the brain computes the vestibular cancellation signal that eliminates actively generated signals from early sensory processing. We predict that the cerebellar cortex plays an essential role in computing the mismatch between expected and actual vestibular input to compute a cancellation signal. Aim 2 will determine how the cerebellum learns to interpret active motion as self-generated when the relationship between the actual and expected sensory feedback is altered. These experiments will provide insight into the error-based mechanisms that ensure calibration of the vestibular reafference suppression mechanism is maintained. Finally, in Aim 3 we will determine whether and how motivation modulates cerebellum-mediated vestibular reafference suppression. Combined, these studies will (1) determine the source of the vestibular reafference cancellation signal, (2) advance our understanding of the cerebellum adapts to changes in vestibular input, and (3) clarify how neuronal mechanisms underlying reafference suppression can be leveraged by motivational influences to optimize performance.