Internal models of self-motion: computations that suppress vestibular reafference in early vestibular processing

Abstract

In everyday life, vestibular sensors are activated by both self-generated and externally applied head movements. The ability to distinguish inputs that are a consequence of our own actions (i.e., active motion) from those that result from changes in the external world (i.e., passive or unexpected motion) is essential for perceptual stability and accurate motor control. Recent work has made progress toward understanding how the brain distinguishes between these two kinds of sensory inputs. We have performed a series of experiments in which single-unit recordings were made from vestibular afferents and central neurons in alert macaque monkeys during rotation and translation. Vestibular afferents showed no differences in firing variability or sensitivity during active movements when compared to passive movements. In contrast, the analyses of neuronal firing rates revealed that neurons at the first central stage of vestibular processing (i.e., in the vestibular nuclei) were effectively less sensitive to active motion. Notably, however, this ability to distinguish between active and passive motion was not a general feature of early central processing, but rather was a characteristic of a distinct group of neurons known to contribute to postural control and spatial orientation. Our most recent studies have addressed how vestibular and proprioceptive inputs are integrated in the vestibular cerebellum, a region likely to be involved in generating an internal model of self-motion. We propose that this multimodal integration within the vestibular cerebellum is required for eliminating self-generated vestibular information from the subsequent computation of orientation and posture control at the first central stage of processing.