Encoding of tangential torque in responses of tactile afferent fibres innervating the fingerpad of the monkey

Abstract

Torsional loads are ubiquitous during everyday dextrous manipulations. We examined how information about torque is provided to the sensorimotor control system by populations of tactile afferents. Torsional loads of different magnitudes were applied in clockwise and anticlockwise directions to a standard central site on the fingertip. Three different background levels of contact (grip) force were used. The median nerve was exposed in anaesthetized monkeys and single unit responses recorded from 66 slowly adapting type-I (SA-I) and 31 fast adapting type-I (FA-I) afferents innervating the distal segments of the fingertips. Most afferents were excited by torque but some were suppressed. Responses of the majority of both afferent types were scaled by torque magnitude applied in one or other direction, with the majority of FA-I afferent responses and about half of SA-I afferent responses scaled in both directions. Torque direction affected responses in both afferent types, but more so for the SA-I afferents. Latencies of the first spike in FA-I afferent responses depended on the parameters of the torque. We used a Parzen window classifier to assess the capacity of the SA-I and FA-I afferent populations to discriminate, concurrently and in real-time, the three stimulus parameters, namely background normal force, torque magnitude and direction. Despite the potentially confounding interactions between stimulus parameters, both the SA-I and the FA-I populations could extract torque magnitude accurately. The FA-I afferents signalled torque magnitude earlier than did the SA-I afferents, but torque direction was extracted more rapidly and more accurately by the SA-I afferent population.

Touch is a team effort: interplay of submodalities in cutaneous sensibility

“…However, changes in grip forces are triggered even in the absence of slip, for example when information about friction becomes available after initial contact with an object [35]. Furthermore, properties such as object curvature [36], force direction [37], and tangential torque [38] relevant to object grip and manipulation -are all encoded by both SA1 and RA afferents. While differences between the two afferent classes exist in terms of their latency [39] and informativeness [40], these differences are relatively minor.…”

Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

“…These respond predominantly to pressure, stretch, and vibration and recent evidence shows that most afferent classes are excited by most tactile stimuli [27]. Tactile information is signaled to the central nervous system and utilized during object manipulation; although how this information is decoded is still under investigation [5,[27][28][29].…”

“…finding use in unstructured environments where robotic grippers are required to perform increasingly human-like manipulation tasks (e.g., picking up novel, fragile objects). Studies of humans [1] and monkeys [2] have shown that mechanoreceptors in the fingers play a critical role in providing information about an object's shape, weight, and mass distribution [3][4][5], and without feedback from these receptors even simple object manipulation tasks become difficult [6][7][8]. Therefore, it is generally recognized that providing robotic grippers with feedback via tactile sensors is an essential step for improving their dexterity, towards humanlike performance.…”

Generating tactile afferent stimulation patterns for slip and touch feedback in neural prosthetics

“…The remaining free parameters-r, Vieak, () (the standard deviation of Wt), all time points of h, and the nine weights wrw 9 for the force signal inputs-were optimized using a standard maximum likelihood estimation (MLE) procedure for NLIF models [19], in which the spike times predicted by the model are optimized to match those of a real neural spike train. Non-human primate FAl and SAl responses to torque stimuli from study [20] were used as training data for the MLE algorithm. This training data set contained the necessary F x, F y, and F z force inputs.…”