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Validating a Population Model of Tactile Mechanotransduction of Slowly Adapting Type I Afferents at Levels of Skin Mechanics, Single-unit Response and Psychophysics
PrePrint
ISSN: 1939-1412
Gregory J. Gerling, University of Virginia, Charlottesville
Isabelle I. Rivest, University of Virginia, Charlottesville
Daine R. Lesniak, University of Virginia, Charlottesville
Jacob R. Scanlon, University of Virginia, Charlottesville
Lingtian Wan, University of Virginia, Charlottesville
Previous models of touch have linked skin mechanics to neural firing rate, neural dynamics to action potential elicitation, and mechanoreceptor populations to psychophysical discrimination. However, no one model spans all levels. The objective of work herein is to build a multi-level, computational model of tactile neurons embedded in cutaneous skin, and then validate its predictions of skin surface deflection, single-afferent firing to indenter shift, and population response for sphere discrimination. The model includes a 3D finite element representation of the distal phalange with hyper- and visco-elastic mechanics. Distributed over its surface, a population of receptor models is comprised of bi-phasic functions to represent Merkel cells' transformation of stress/strain to membrane current and a leaky integrate-and-fire neuronal models to generate the timing of action potentials. After including neuronal noise, the predictions of two population encoding strategies (Gradient Sum and Euclidean Distance) are compared to psychophysical discrimination of spheres. Results indicate that predicted skin surface deflection matches Srinivasan's observations for 50 micron and 3.17 mm diameter cylinders [1] and single-afferent responses achieve R2=0.81 when compared to Johnson's recordings [2]. Discrimination results correlate with Goodwin's experiments [3], whereby 287 and 365 m-1 spheres are more discriminable than 287 and 296 m-1.
Index Terms:
Perception and psychophysics,Information Technology and Systems,Models and Principles,User/Machine Systems,Human information processing,Computing Methodologies,Artificial Intelligence,Miscellaneous,Computational neuroscience,Haptics,Human Haptics,Touch-based properties and capabilities of the human user,Biomechanics,Human factors and ergonomics,Neuroscience
Citation:
Gregory J. Gerling, Isabelle I. Rivest, Daine R. Lesniak, Jacob R. Scanlon, Lingtian Wan, "Validating a Population Model of Tactile Mechanotransduction of Slowly Adapting Type I Afferents at Levels of Skin Mechanics, Single-unit Response and Psychophysics," IEEE Transactions on Haptics, 04 June 2013. IEEE computer Society Digital Library. IEEE Computer Society, <http://doi.ieeecomputersociety.org/10.1109/TOH.2013.36>
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