Interactions of Whisking and Touch Signals in the Rat Brainstem

Neural mechanisms for the localization of unexpected external motion

To localize objects during active sensing, animals must differentiate stimuli caused by volitional movement from real-world object motion. To determine a neural basis for this ability, we examined the mouse superior colliculus (SC), which contains multiple egocentric maps of sensorimotor space. By placing mice in a whisker-guided virtual reality, we discovered a rapidly adapting tactile response that transiently emerged during externally generated gains in whisker contact. Responses to self-generated touch that matched self-generated history were significantly attenuated, revealing that transient response magnitude is controlled by sensorimotor predictions. The magnitude of the transient response gradually decreased with repetitions in external motion, revealing a slow habituation based on external history. The direction of external motion was accurately encoded in the firing rates of transiently responsive neurons. These data reveal that whisker-specific adaptation and sensorimotor predictions in SC neurons enhance the localization of unexpected, externally generated changes in tactile space.

Sensory computations in the cuneate nucleus of macaques

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures-the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in the CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, the CN plays a key role in processing tactile information.