Compression of dynamic tactile information in the human hand

Abstract

A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.

Unsupervised learning of haptic material properties

“…PC and RA). This does not imply that the different auto encoders we tested only mimic the function of the Pacini and Meissner corpuscles innervated by the PC and RA afferents, because, before the receptors, the signal might already be compressed by the biomechanic properties of the hand (Shao, Hayward & Visell, 2020).…”

Malleability of the cortical hand map following a finger nerve block

“…Secondly, Manfredi et al (2012) have demonstrated that even passive stimulation of a part of the hand triggers extensive ripple effects across the skin. Because these vibrations activate mechanoreceptors on other fingers, localised tactile stimulation elicits widely distributed peripheral responses, with distinct spatiotemporal patterns (Shao et al 2016, Shao et al 2020. Both of these results suggest that shared somatosensory processing of inputs from multiple skin surfaces across the hand might be more prevalent than previously anticipated.…”

“…Although the peripheral signals leading to this pattern of activity mainly come from mechanoreceptors on the blocked finger (in natural contexts), some peripheral receptive fields are very large, integrating information from across most of the hand (Johansson 1978, Tommerdahl et al 2010, even before reaching cortex: Pruszynski and Johansson 2014). These effects likely arise from the mechanical attributes of the hand, which induces a ripple of vibration following even very localised touch, which will in turn cause a specific spatiotemporal pattern of mechanoreceptor activity across the entire hand (Manfredi et al 2012, Shao et al 2020; see also Figure 4A). Receptive fields on the cortical level can be complex and integrate information across a variety of receptor types (Tommerdahl et al 2010, Saal et al 2015.…”

Harnessing tactile waves to measure skin-to-skin interactions

“…Skin-to-skin touch is challenging to measure objectively, yet it presents a number of intriguing problems that span neuroscience, psychology and philosophy. Here, we tested the efficacy of a new measure of skin-to-skin tactile behaviour that took advantage of the frictional fluctuations propagating in soft tissues (Shao et al, 2016(Shao et al, , 2020. Participants were instructed to stroke skin surfaces while an accelerometer was fixed to their touching finger.…”

“…It is adapted from previous work highlighting the propagation of mechanical energy in soft tissues far from a region of contact. The effect of digital tactile interactions can be measured in the whole hand (Tanaka et al, 2012;Manfredi et al, 2012;Shao et al, 2016Shao et al, , 2020, at least as far as in the forearm (Delhaye et al, 2012). These long-range effects are likely to result from the propagation of elastic S-waves (Vexler et al, 1999) and surface Rayleigh waves (Kirkpatrick et al, 2004) in soft tissues, with a relatively low rate of attenuation over distance.…”