Simulating tactile signals from the whole hand with millisecond precision

Abstract

When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.mechanoreceptor | tactile afferent | somatosensory periphery | skin mechanics | computational model T he human hand is endowed with thousands of mechanoreceptors of different types distributed across the skin, each innervated by one or more large myelinated nerve fibers (1). These fibers convey detailed information about contact events and provide us with an exquisite sensitivity to the form and surface properties of grasped objects (2, 3). During object manipulation and tactile exploration, the glabrous skin of the hand undergoes complex spatiotemporal mechanical deformations, which in turn, drive very precise spiking responses in individual afferents. Coarse object features, such as edges and corners, are reflected in spatial patterns of activation in slowly adapting type I (SA1) and rapidly adapting (RA) fibers, which are densely packed in the fingertip (3, 4). At the same time, interactions with objects and surfaces elicit high-frequency, low-amplitude surface waves that propagate across the skin of the finger and palm and excite vibration-sensitive Pacinian (PC) afferents all over the hand (5-8).Recording the activity of tactile nerve fibers in monkeys or humans is technically difficult, is slow, and generally yields responses from a single unit at a time (9, 10). Although such recordings have yielded powerful insights into the neural basis of touch, they provide a limited window into the information that the hand c...

Similar somatotopy for active and passive digit representation in primary somatosensory cortex

“…As a further rationale that passive tactile stimulation may provide a restricted view of somatosensory activity, the somatosensory cortex receives inputs from a host of sources beyond the simple cutaneous touch activated by passive tasks. This includes activation of deep mechanoreceptors, muscle proprioceptors, and skin stretch receptors (Bensmaia & Tillery, 2014;Chouvardas, Miliou, & Hatalis, 2008;Saal, Delhaye, Rayhaun, & Bensmaia, 2017). Active movement also causes efferent signals into SI from the motor system (Wolpert & Flanagan, 2001;Wolpert, Ghahramani, & Jordan, 1995), as well as top down inputs from high-order cognitive processing, e.g., attention, visual input (Kuehn, Mueller, Turner, & Schütz-Bosbach, 2014;Puckett, Bollmann, Barth, & Cunnington, 2017).…”

Haptic metameric textures

“…We wonder whether the observed insensitivity to the higher-order statistics reflects processing characteristics of the central nervous system or simple information loss in the periphery, i.e., the elasticity of the skin and noisy sparse sampling by mechanoreceptors. To make our best guess of how the difference in spatial texture information is represented in peripheral neural activation, we simulated responses of tactile afferents using the computational model 'TouchSim' (Saal et al, 2017). The beauty of this model is that it can provide the responses of hundreds of distributed afferents on a millisecond scale, though it works under some simplified assumptions (e.g., it does not incorporate lateral sliding/forces).…”

“…We simulated the spike timings and spatial distributions of the afferents while the index finger pad scanned our subband-matched NS stimuli. We calculated the firing similarity across stimuli in terms of a spike distance metric (Victor and Purpura, 1997), following the original 'TouchSim' study (Saal et al, 2017). We found that the firing similarities between pairs of identical texture stimuli with independent neural noise were always higher than those between pairs comprising two different stimuli, regardless of the type of afferent (Fig.…”

“…Due to the difference in the sensing process, however, we can only assume a rough correspondence of the sensor response pattern between the two modalities. To overcome this limitation, we used 'TouchSim' (Saal et al, 2017) to evaluate our hypothesis in this study, but more direct evaluation would be necessary in future.…”

“…We simulated spatio-temporally distributed firing patterns of three different types of tactile afferent by using the computational model "TouchSim", which can reproduce major response characteristics that have been clarified by previous research (Saal et al, 2017). The original parameters of the model were based on measured spiking data obtained with monkeys.…”