The Vestibular System Implements a Linear–Nonlinear Transformation In Order to Encode Self-Motion

Early vestibular processing in macaque monkeys is inherently nonlinear and is optimized to detect specific features of self-motion. Although it is well established that the neural code representing the world changes at each stage of a sensory pathway, the transformations that mediate these changes are not well understood. Here we show that self-motion (i.e. vestibular) sensory information encoded by VIIIth nerve afferents is integrated nonlinearly by post-synaptic central vestibular neurons. This response nonlinearity was characterized by a strong (∼50%) attenuation in neuronal sensitivity to low frequency stimuli when presented concurrently with high frequency stimuli. Using computational methods, we further demonstrate that a static boosting nonlinearity in the input-output relationship of central vestibular neurons accounts for this unexpected result. Specifically, when low and high frequency stimuli are presented concurrently, this boosting nonlinearity causes an intensity-dependent bias in the output firing rate, thereby attenuating neuronal sensitivities. We suggest that nonlinear integration of afferent input extends the coding range of central vestibular neurons and enables them to better extract the high frequency features of self-motion when embedded with low frequency motion during natural movements. These findings challenge the traditional notion that the vestibular system uses a linear rate code to transmit information and have important consequences for understanding how the representation of sensory information changes across sensory pathways. Author Summary: Understanding how the coding of sensory information changes at different stages of sensory processing remains a fundamental challenge in systems neuroscience. Here we address this question by studying early sensory processing in vestibular pathways of monkeys, a system for which sensory stimuli are relatively easy to describe. Peripheral vestibular afferents detect and encode head motion in space to ensure posture and gaze is accurate and stable during everyday life. In this study, we show that central vestibular neurons nonlinearly integrate their afferent inputs, which helps explain the mechanisms that generate enhanced feature detection in sensory pathways. In addition, our results overturn conventional wisdom that early vestibular processing is linear, revealing a striking boosting nonlinearity that is a hallmark of the first central stage of vestibular processing. Studies from other sensory systems have shown that higher-order neurons can more efficiently detect specific features of sensory input, and that nonlinear transformations can increase this efficiency. We suggest that nonlinear integration of afferent input by central vestibular neurons extends their coding range and facilitates the detection of natural vestibular stimuli.