Encoding and Decoding of Shape in Tactile Sensing

This thesis is concerned with the biomechanical bases of tactile sensing and their implica-tions to neural response of Slowly Adapting Type-I (SA-I) mechanoreceptors in the skin. In addition, possible non-linear mechanisms used by the central nervous system (CNS) for decoding tactile information from the afferent nerve fibers were also investigated. To investigate the mechanics of touch, a high resolution three dimensional multilay-ered finite element model of a primate fingerpad was developed. Predictions of the model matched empirically obtained surface displacements very well, thereby validating its biomechanical behavior. The model was used to simulate static indentation of the fingertip by rigid objects of different shapes such as rectangular bars, cylinders, and si-nusoidal step shapes. The corresponding surface pressure distribution was found to be highly dependent on the curvature of the object that indented the finger. A simple model for surface pressure as a function of the indenting object's curvature and the local depth of indentation was developed. To study the mechanism of transduction by the mechanore-ceptors (transformation of the mechanical stress state into neural signals), 18 mechanica