Encoding and decoding cortical representations of tactile features in the vibrissa system.

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 probability and timing characteristics of the sparse cortical representation of complex patterned tactile inputs. Within a Bayesian framework, 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.

Self-motion and the shaping of sensory signals.

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.

The dynamics of spatiotemporal response integration in the somatosensory cortex of the vibrissa system.

Spatiotemporal response integration across the neural receptive field (RF) is a general feature of sensory coding and has an important role in shaping responses to naturalistic stimuli. In the primary somatosensory cortex of the rat vibrissa pathway, such integration across the vibrissa array strongly shapes the coding of spatiotemporally distributed deflections. Using a spatiotemporal paired-pulse paradigm, this study revealed that fundamentally different types of pairwise interactions have similar qualitative behavior but that the magnitude, latency, and precision of the neural responses depend on the specific RF components being engaged. In all cases, however, increase in the suppression of response magnitude accompanied a lengthening of latency and a decrease in response precision. Furthermore, nonlinear interactions evoked by stimulation of multiple RF subregions strongly influence both response magnitude and timing to more complex sequences. Despite their complexity, such response interactions are highly predictable from elementary pairwise interactions. To understand the functional role of spatiotemporal interactions in coding, we developed a response model that incorporated the experimentally measured modulations in response magnitude, latency, and precision induced by cross-vibrissa interactions. Simulations of a simplified textural discrimination task indicate that spatiotemporal interactions enhance discrimination under certain stimulus time scales. This improvement follows from a nonlinear response property that acts to restore the neural response in the face of suppression. Together, the present findings highlight the role of response integration in shaping single-cell responses and provide predictions about how changes in response parameters influence coding accuracy.