Cerebellar circuits for locomotor learning in space and time
Every movement we make requires us to coordinate our actions precisely in space and time. This proposal aims to understand how that remarkable coordination is achieved by neural circuits controlling movement. The cerebellum plays a critical role in keeping movements calibrated and coordinated, and it is thought to do this in part through a motor learning process in which predictable perturbations of movement are gradually compensated. Cerebellum-dependent forms of motor learning have been identified for a variety of behaviors, including locomotion, and locomotor learning is used as a rehabilitative therapy in human patients. We recently established locomotor learning in mice, using a custom-built, transparent split-belt treadmill that controls the speeds of the two sides of the body independently and allows for high-resolution behavioral readouts. Here, we will combine quantitative analysis of locomotor behavior with genetic circuit dissection to answer two fundamental questions: How are cerebellar outputs read out by downstream circuits, to calibrate spatial and temporal components of movement? and How are instructive signals for spatial and temporal learning encoded by cerebellar inputs? Specifically, we will: 1) Use circuit tracing combined with manipulation of specific cerebellar outputs to identify downstream pathways for spatial and temporal locomotor learning, 2) Investigate the role of error signals for cerebellar learning via optogenetic perturbation of climbing fiber inputs to the cerebellum, and 3) Image complex spike activity from populations of Purkinje cells during locomotion and learning, to ask how spatial and temporal error signals are encoded within the cerebellum. These studies will allow us to bridge levels of analysis to understand how cerebellar learning mechanisms convert behaviorally-relevant sensorimotor error signals into calibration signals that ensure accurate and coordinated movements in space and time for a wide range of behaviors.