A fundamental question in neuroscience is: How does the brain compute accurate estimates of our self-motion relative to the world in everyday life. In this talk, I will describe recent findings from my laboratory that have addressed this question and provided new insights into the processing of vestibular self-motion information to ensure stable perception and accurate motor control.
First, we have recently examined the statistics of the natural self-motion signals experienced by mice, monkeys, and humans, and then explored the neural coding strategies used by early vestibular pathways. We then found that vestibular afferents identically encode this self-motion across passive and active conditions. In contrast, the central vestibular neurons directly targeted by afferents are markedly less sensitive to active motion. This ability to distinguish between active and passive motion is not a general feature of early vestibular processing, but rather is a characteristic of a distinct group of central vestibular neurons known to contribute to postural control and perception. To make the required distinction between passive and active stimuli, we have further shown that the cerebellum builds a dynamic prediction (e.g., internal model) of the sensory consequences of self-motion during active behaviors. Notably, when unexpected vestibular inputs become persistent during active motion, this cerebellum-based mechanism is rapidly updated to re-enable the vital distinction between active and passive vestibular inputs. Finally, we have demonstrated that ascending thalamocortical vestibular pathway even more selectively encode unexpected motion, thereby providing a neural correlate for ensuring perceptual stability during active versus externally generated motion.
Taken together, these findings have important implications for our understanding of the brain mechanisms that ensure stable perception and accurate behaviour as we move through and explore our world.