Neuroscience at The University of Chicago

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Ph.D., University of North Carolina at Chapel Hill




Lab Website

Sliman Bensmaia, Ph.D.

Assistant Professor

Research Interests
  • Computational & Theoretical
  • Systems / Behavior / Cognitive
When we interact with an object, much information about the object is conveyed through signals from the hand. Information about the shape of the object, its texture, its compliance, and its thermal properties is carried in the pattern of activity evoked in a variety of receptors embedded in the skin, the joints, and the muscles. We can often recognize an object simply on the basis of sensory signals emanating from our hands. Without this information, manipulating objects would be slow, clumsy, and effortful. Our goal is to characterize the sensory information originating from the hand and understand how this information is transformed in successive stages of processing. Our approach involves combining psychophysics, peripheral and cortical neurophysiology, and computational modeling. The goal is to discover the aspect of the neural response that accounts qualitatively and quantitatively for behavior at each stage of perceptual processing. This lab is currently following four lines of inquiry: 1. The neural basis of tactile texture perception Texture is the sensory correlate of surface material and microgeometry. Though textural information can be obtained both visually and auditorily, touch yields much finer and more complex textural information than do the other sensory modalities. When we run our fingers across a surface, we may perceive the surface as being rough, like sandpaper, or smooth, like glass; the surface may also vary along other sensory continua, such as hardness (e.g., stone) vs. softness (e.g. moist sponge), stickiness (e.g., tape) vs. slipperiness (e.g., soap). Also, whether a texture is thermally isolating (e.g., leather) or thermally conductive (like metal) contributes to the textural percept. In a series of psychophysical experiments, we have shown that the vibrations elicited in the skin during the scanning of surfaces play an important role in how the textures of these surfaces are perceived. Responses elicited in mechanoreceptive afferents by these complex vibrations are highly repeatable and temporally patterned; a patterning that has yet to be ascribed a function in everyday sensory experience. We hypothesize that an important function of this temporal patterning is to convey information about surface texture. The objective of this line of inquiry is to elucidate the neuronal mechanisms underlying the tactile perception of texture. We focus on how the multiple dimensions of texture are encoded in the nervous system, and the extent to which information about surface texture is conveyed through texture-elicited vibrations. 2. The neural basis of proprioception Interacting with our environment requires us to resolve spatial relationships. Accordingly, the brain maintains multiple representations of space, each at different scales and in different coordinate systems, all of which must interact intimately to guide action. Information about our body in space, proprioception, is key because all other neural representations of space must ultimately interface with proprioceptive representations for us to act efficiently upon objects. The movements of patients who have lost proprioceptive feedback, and thus must rely solely on vision, are consequently very slow, poorly coordinated, and require great concentration. In addition to its function in motor control, the awareness of our body and its position in space is an essential component of our sense of self. The goal of this line of inquiry is to characterize proprioceptive representations of the hand, our most important organ for grasping and tool use, focusing on areas of the brain known to receive proprioceptive signals (i.e., somatosensory cortex). To this end, we measure the neuronal responses evoked in the brain during natural grasping movements. In collaboration with Dr. Nicholas Hatsopoulos, we also analyze the functional connectivity between ensembles of SI and MI neurons during grasping behavior. 3. Sensory feedback for upper limb neuroprostheses Tactile sensation is critical for effective object manipulation, but current prosthetic upper limbs make no provision for delivering haptic feedback to the user. For individuals who require use of prosthetic limbs, this lack of feedback transforms a mundane task into one that requires herculean concentration and effort. Although vibrotactile motors and sensory substitution devices can be used to convey gross sensations, a direct neural interface is required to provide detailed and intuitive sensory feedback. . In view of this, the new generation of neuroprostheses will enable electrical stimulation of somatosensory neurons in the peripheral or central nervous system. Although there have been a few reports illustrating how electrical stimulation in the peripheral (PNS) or central nervous system (CNS) can be used to convey tactile information, there have been no systematic studies elucidating the advantages or disadvantages of stimulating afferent peripheral nerves versus neurons in primary somatosensory cortex (S1). In an attempt to fill this gap, we aim to quantify and compare the efficacy of electrical stimulation of three neural populations—peripheral afferents and neurons in areas 3b and 1 of S1—as a means to convey the tactile information required for basic object manipulation. 4. Integration of tactile and auditory signals We are constantly bombarded by myriad sensory signals and are tasked with sorting these for useful information about our environment. Signals conveyed by our sensory systems interact in time and space, affecting not only when and where we perceive meaningful events, but even the identity and content of these occurrences. Familiar examples of such perceptual interactions include the ventriloquism illusion (in which viewing an object biases the perceived location of a separate sound source) and the McGurk effect (in which seeing lip movements biases the perception of simultaneously heard speech sounds). During haptic exploration of surfaces, complex mechanical oscillations—of surface displacement and air pressure—are generated, which are then transduced by receptors in the skin and in the inner ear. Tactile and auditory signals thus convey redundant information about texture, partially carried in the spectral content of these signals. In ongoing psychophysical experiments, we investigate how tactile and auditory signals are integrated to form a coherent percept.

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Kim S.S., Sripati, A.P. & Bensmaia S.J. (in press). Predicting individual spikes evoked by tactile stimulation of the hand, Journal of Neurophysiology. Pei Y.C., Hsiao S.S., Craig J.C. & Bensmaia S.J. (in review). Neural mechanisms of tactile motion integration in somatosensory cortex. Pei, Y.C., Hsiao, S.S., Craig, J.C. & Bensmaia S.J. (2010). Shape invariant coding of motion direction in somatosensory cortex, Public Library of Science Biology, 8, e1000305. Kim, S., Sripati, A.P., Vogelstein, R.J., Armiger, R.S., Russel, A.F., & Bensmaia, S.J. (2009). Conveying tactile feedback in sensorized hand neuroprostheses using a model of mechanotransduction, IEEE Transactions in Biomedical Circuits and Systems, 3, 398-404. Pei, Y.C., Denchev P.V., Hsiao S.S., Craig J.C. & Bensmaia S.J. (2009). Convergence of submodality specific input onto neurons in primary somatosensory cortex, Journal of Neurophysiology, 102, 1843-1853. Yau J.M., Hollins M., & Bensmaia, S.J. (2009). Textural timbre: the perception of surface microtexture depends in part on multimodal spectral cues, Communicative and Integrative Biology, 2, 1-3. 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. 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. Craig J.C., Rhodes R.P., Gibson G.O. & Bensmaia S.J. (2008). Discriminating smooth from grooved surfaces: Effects of random variations in skin penetration, Experimental Brain Research, 188, 331-340. 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. Bensmaia, S.J., Hsiao, S.S., Denchev, P.V., Killebrew, J.H., & Craig J.C. (2008). The tactile perception of stimulus orientation, Somatosensory and Motor Research, 25, 49-59. 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. Yoshioka, T., Bensmaia, S.J., Craig, J.C., & Hsiao, S.S. (2007). Texture perception through direct and indirect touch: An analysis of perceptual space for tactile textures in two modes of exploration, Somatosensory and Motor Research, 24, 53-70. Killebrew, J.H., Bensmaia, S.J., Dammann, J.F., Denchev, P., Hsiao, S.S., Craig, J.C. & Johnson, K.O. (2007). A dense array stimulator to generate arbitrary spatio-temporal tactile stimuli, Journal of Neuroscience Methods, 161, 62-74. Bensmaia, S.J., Killebrew, J.H. & Craig, J.C. (2006). Influence of visual motion on tactile motion perception, Journal of Neurophysiology, 96, 1625-1637. 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. Bensmaia, S.J., Craig, J.C., & Johnson, K.O. (2006). Temporal factors in tactile spatial acuity: Evidence for RA interference in fine spatial processing, Journal of Neurophysiology, 95, 1783-1791. Bensmaia, S.J., Craig, J.C., Yoshioka, T., & Johnson, K.O. (2006). SA1 and RA responses to static and vibrating gratings, Journal of Neurophysiology, 95, 1771-1782. Leung, Y.Y.M., Bensmaia, S.J., Hsiao, S.S. & Johnson, K.O. (2005). Time course of vibratory adaptation and recovery in cutaneous mechanoreceptive afferents, Journal of Neurophysiology, 94, 3037-3045. 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. Bensmaia, S.J. & Hollins, M. (2005). Pacinian representation of fine surface texture, Perception & Psychophysics, 67, 842-854. Bensmaia, S.J., Hollins, M., & Yau. J. (2005). Vibrotactile frequency and intensity information in the Pacinian system: a psychophysical model, Perception & Psychophysics, 67, 828-841. Bensmaia, S.J. & Hollins, M (2003). The vibrations of texture, Somatosensory and motor research, 20, 33-43. Bensmaia, S. (2002). A transduction model of the Meissner corpuscle, Mathematical Biosciences, 176, 203-217. Hollins, M., Bensmaia, S., & Washburn, S. (2001). Vibrotactile adaptation impairs discrimination of fine, but not coarse, textures, Somatosensory and Motor Research, 18, 253-262. 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. Bensmaia, S. & Hollins, M. (2000). Complex tactile waveform discrimination, Journal of the Acoustical Society of America, 108, 1236-1245.