Research Interests:

Neural Coding and the Neural Basis of Perception

We are primarily interested in neural coding and the neural basis of perception. We combine psychophysics, peripheral and cortical neurophysiology, and computational methods to investigate the tactile processing of form, motion, texture, and vibration. The general approach, pioneered by Vernon Mountcastle, consists in measuring an aspect of perception on human subjects then recording the responses evoked in peripheral afferents and in cortical neurons in macaque monkeys. Importantly, the same stimuli are used in both psychophysical and neurophysiological experiments. The objective is to discover the aspect of the neural response that accounts for the measured behavior at each stage of perceptual processing.

Processing of spatial information
Both vision and touch share the common problem of inferring stimulus form and motion from a spatio-temporal pattern of activation across a two-dimensional sensory sheet (i.e., the retina and the skin). In a series of psychophysical and neurophysiological studies, we have shown that the two systems have evolved analogous neural mechanisms to process both form and motion. First, a large proportion of neurons in the primary somatosensory cortex (S1) exhibit tuned responses to the orientation of stimuli impinging upon their receptive fields (RFs). Second, an overlapping set of neurons in S1 is tuned for direction of motion of bars scanned across their RFs. The tuning properties of these orientation- and motion-sensitive neurons are analogous to those observed in primary visual cortex (V1). Importantly, the orientation and motion signals in S1 can account for psychophysical performance in orientation and motion discrimination tasks. Thirdly, we have shown that the tactile integration of local motion cues is analogous to its visual counterpart using the tactile equivalents of stimuli whose perceptual properties are well established in vision, including superimposed gratings (plaids), barber poles, and moving bar fields. Indeed, a subset of neurons in S1 processes local motion information, whereas other neurons encode global motion. The tuning properties of this latter class of neurons resemble those observed in the medial temporal area (MT), a specialized module for processing visual motion. The responses of neurons that respond to global motion (so-called pattern neurons) can account for the perception of plaids, barber poles and bar-fields, measured in human subjects.

Processing of temporal information
The processing of tactile vibration is in many ways analogous to the auditory processing of acoustic stimuli. Indeed, the auditory and somatosensory systems respond over an overlapping range of stimulus frequencies (from about 100 to 1000Hz) and the underlying stimulus energy is essentially identical. Furthermore, in both modalities, oscillating stimuli yield temporally patterned activity in the peripheral afferents and the evidence suggests that this patterning plays an important role in perception of both auditory and vibrotactile stimuli.

Vibrations have been shown to play a role in the tactile perception of fine textures: when tactually exploring finely textured surfaces, small vibrations are produced in the skin. These vibrations are then converted into neural signals by specialized receptors embedded in the skin and these signals convey information about surface microgeometry. Our perception of fine textures has been shown to depend on the spectral content of the vibrations they elicit in the skin, in a manner analogous to the way in which the spectral content of acoustic stimuli plays a role in the percepts they elicit. Vibrotaction also plays a role in the perception of distal events. Indeed, when we use a tool, vibrations elicited in the tool convey information about the environment impinging upon the distal end of the tool.

In a series of parallel psychophysical and neurophysiological experiments, we investigate the neural mechanisms underlying the tactile perception of vibrations (and, concomitantly, of fine textures). In a published study, we characterize the way in which the intensity of a vibratory stimulus is represented in the pattern of activity it evokes in populations of mechanoreceptors. We show that three types of low-threshold mechanoreceptors in the skin contribute to the perception of stimulus intensity.

Biophysics of transduction
We are also developing biophysical models of how spatio-temporal stimuli are transduced in the three populations of low-threshold mechanoreceptors. Using continuum mechanics, we first developed a model describing how the spatial configuration of a stimulus indented into the skin shapes the response it evokes in SA1 and RA afferents. We are currently developing a model that predicts the timing of spikes evoked by an arbitrary dynamic stimulus. We find that SA1, RA and PC afferents are differentially sensitive to the indentation, and its two derivatives (velocity and acceleration). We propose that models that describe the mechanotransduction will play an important in the development of sensorized prosthetics.

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Selected Publications

1. Kim, S., Sripati, A.P., Vogelstein, R.J., Armiger, R.S., Russel, A.F., & Bensmaia, S.J. (accepted pending minor revisions). Conveying tactile feedback in sensorized hand neuroprostheses using a model of mechanotransduction, IEEE Transactions in Biomedical Circuits and Systems.


2. Pei, Y.C., Denchev P.V., Hsiao S.S., Craig J.C. & Bensmaia S.J. (in press). Convergence of submodality specific input onto neurons in primary somatosensory cortex, Journal of Neurophysiology. (PubMed)


3. Yau J.M., Olenczak J.B., Dammann, J.F. & Bensmaia, S.J. (2009). Temporal frequency channels linked across audition and touch, Current Biology, 19, 561-566. (PubMed)


4. Pei, Y.C., Hsiao S.S., & Bensmaia, S.J. (2008). The tactile integration of local motion cues is analogous to its visual counterpart, Proceedings of the National Academy of Science, 105, 8130-8135. (PubMed)


5. Bensmaia, S.J., Denchev P.V., Dammann J.F., Craig J.C., & Hsiao, S.S. (2008). The representation of stimulus orientation in the early stages of somatosensory processing, Journal of Neuroscience, 28, 776-786. (PubMed)


6. Muniak, M.A., Ray, S., Hsiao, S.S., Dammann, J.F., & Bensmaia, S.J. (2007). The neural coding of stimulus intensity: linking the population response of mechanoreceptive afferents with psychophysical behavior, Journal of Neuroscience, 27, 11687-11699. (PubMed)


7. Bensmaia, S.J., Killebrew, J.H. & Craig, J.C. (2006). Influence of visual motion on tactile motion perception, Journal of Neurophysiology, 96, 1625-1637. (PubMed)


8. Sripati, A.P., Bensmaia, S.J., & Johnson, K.O. (2006). A continuum mechanical model for mechanoreceptive afferent responses to indented spatial patterns, Journal of Neurophysiology, 95, 3852-3864. (PubMed)


9. Bensmaia, S.J., Leung, Y.Y.M., Hsiao, S.S. & Johnson, K.O.  (2005). Vibratory adaptation of cutaneous mechanoreceptive afferents, Journal of Neurophysiology, 94, 3023-3036. (PubMed)


10. Hollins, M., Bensmaia, S., Karlof, K., & Young, F. (2000). Individual Differences in Perceptual Space for Tactile Textures: Evidence from Multidimensional Scaling, Perception & Psychophysics, 62, 1534-1544. (PubMed)

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