During behavior, rats and other rodents use their facial vibrissae to actively explore surfaces through whisking and head/body movement, resulting in complex sensory inputs that vary over a large range of angular velocities and temporal scales. How these complex sensory inputs manifest in the patterns of cortical firing events that ultimately form the perceptual experience is not well understood. Through single-unit cortical recordings of layer 4 neurons in S1 of the anesthetized rat, we systematically quantified the interactions between instantaneous velocity and timing of vibrissa motion, finding a strong interaction between angular velocity and timing of contacts on the tens of milliseconds time scale. From the quantification of these joint tuning properties, a detailed nonlinear encoding model was formulated that was highly predictive of firing probabilityandtimingcharacteristicsofthesparsecorticalrepresentationofcomplexpatternedtactileinputs.WithinaBayesianframework,the encoding model was then used to decode tactile patterns under simple transformations of the stimulus along dimensions of velocity and timing, as a demonstration of the lower bound of the idealized perceptual capabilities of the animal.
Sensory systems must form stable representations of the external environment in the presence of self-induced variations in sensory signals. It is also possible that the variations themselves may provide useful information about self-motion relative to the external environment. Rats have been shown to be capable of fine texture discrimination and object localization based on palpation by facial vibrissae, or whiskers, alone. During behavior, the facial vibrissae brush against objects and undergo deflection patterns that are influenced both by the surface features of the objects and by the animal's own motion. The extent to which behavioral variability shapes the sensory inputs to this pathway is unknown. Using high-resolution, high-speed videography of unconstrained rats running on a linear track, we measured several behavioral variables including running speed, distance to the track wall, and head angle, as well as the proximal vibrissa deflections while the distal portions of the vibrissae were in contact with periodic gratings. The measured deflections, which serve as the sensory input to this pathway, were strongly modulated both by the properties of the gratings and the trial-to-trial variations in head-motion and locomotion. Using presumed internal knowledge of locomotion and head-rotation, gratings were classified using short-duration trials (<150 ms) from high-frequency vibrissa motion, and the continuous trajectory of the animal's own motion through the track was decoded from the low frequency content. Together, these results suggest that rats have simultaneous access to low- and high-frequency information about their environment, which has been shown to be parsed into different processing streams that are likely important for accurate object localization and texture coding.
In nature, several species use flexible probes to actively explore their environment and acquire important sensory information, such as surface topology, texture, and water or air flow velocity. For example, rats and other rodents have an array of facial vibrissae (or whiskers) with which they gather tactile information about the external world. The complex mechanisms by which mechanical deformations of the probe lead to neuronal activity in the animal's nervous system are still far from being completely understood. This is due to the intricacy of the deformation mechanics of the flexible sensors, the processes responsible for transforming the deformation to electrical activity, and the subsequent representation of the sensory information by the nervous system. Understanding how these mechanosensory signals are transduced and extracted by the nervous system promises great insight into biological function, and has novel technological applications. To understand the mechanical aspect of sensory transduction, here we monitored the deformation of a rat's vibrissa as it strikes rigid objects with different topologies (surface features) during locomotion, using high-speed videography. Motivated by our observations, we developed detailed numerical models to study the mechanics of such flexible probes. Our findings elucidate how active sensation with vibrissae might provide sensory information and in addition have direct implications for several technological areas. To put this in perspective, we propose strategies in which flexible probes can be used to characterize surface topology at high speeds, which is a desirable feature in several technological applications such as memory retrieval.
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