Edge orientation signals in tactile afferents of macaques

Skin properties and afferent density in the deterioration of tactile spatial acuity with age

Tactile sensitivity is affected by age, as shown by the deterioration of spatial acuity assessed with the two-point discrimination task. This is assumed to be partly a result of age-related changes of the peripheral somatosensory system. In particular, in the elderly, the density of mechanoreceptive afferents decreases with age and the skin tends to become drier, less elastic and less stiff. To assess to what degree mechanoreceptor density, skin hydration, elasticity and stiffness can account for the deterioration of tactile spatial sensitivity observed in the elderly, several approaches were combined, including psychophysics, measurements of finger properties, modelling and simulation of the response of first-order tactile neurons. Psychophysics confirmed that the Elderly group has lower tactile acuity than the Young group. Correlation and commonality analysis showed that age was the most important factor in explaining decreases in behavioural performance. Biological elasticity, hydration and finger pad area were also involved. These results were consistent with the outcome of simulations showing that lower afferent density and lower Young's modulus (i.e. lower stiffness) negatively affected the tactile encoding of stimulus information. Simulations revealed that these changes resulted in a lower build-up of task-relevant stimulus information. Importantly, the reduction in discrimination performance with age in the simulation was less than that observed in the psychophysical testing, indicating that there are additional peripheral as well as central factors responsible for age-related changes in tactile discrimination. KEY POINTS: Ageing effects on tactile perception involve the deterioration of spatial sensitivity, although the contribution of central and peripheral factors is not clear. We combined psychophysics, measurements of finger properties, modelling and simulation of the response of first-order tactile neurons to investigate to what extent skin elasticity, stiffness, hydration, finger pad area and afferent density can account for the lower spatial sensitivity observed in the elderly. Correlation and commonality analysis revealed that age was the most important factor to predict behavioural performance. Skin biological elasticity, hydration and finger pad area contributed to a lesser extent. The simulation of first-order tactile neuron responses indicated that reduction in afferent density plays a major role in the deterioration of tactile spatial acuity. Simulations also showed that lower skin stiffness and lower afferent density affect the build-up of stimulus information and the response of SA1 (i.e. type 1 slowly adapting fibres) and RA1 (i.e. type 1 rapidly adapting fibres) afferent fibres.

Temporal coherency of mechanical stimuli modulates tactile form perception

The human hand can detect both form and texture information of a contact surface. The detection of skin displacement (sustained stimulus) and changes in skin displacement (transient stimulus) are thought to be mediated in different tactile channels; however, tactile form perception may use both types of information. Here, we studied whether both the temporal frequency and the temporal coherency information of tactile stimuli encoded in sensory neurons could be used to recognize the form of contact surfaces. We used the fishbone tactile illusion (FTI), a known tactile phenomenon, as a probe for tactile form perception in humans. This illusion typically occurs with a surface geometry that has a smooth bar and coarse textures in its adjacent areas. When stroking the central bar back and forth with a fingertip, a human observer perceives a hollow surface geometry even though the bar is physically flat. We used a passive high-density pin matrix to extract only the vertical information of the contact surface, suppressing tangential displacement from surface rubbing. Participants in the psychological experiment reported indented surface geometry by tracing over the FTI textures with pin matrices of the different spatial densities (1.0 and 2.0 mm pin intervals). Human participants reported that the relative magnitude of perceived surface indentation steeply decreased when pins in the adjacent areas vibrated in synchrony. To address possible mechanisms for tactile form perception in the FTI, we developed a computational model of sensory neurons to estimate temporal patterns of action potentials from tactile receptive fields. Our computational data suggest that (1) the temporal asynchrony of sensory neuron responses is correlated with the relative magnitude of perceived surface indentation and (2) the spatiotemporal change of displacements in tactile stimuli are correlated with the asynchrony of simulated sensory neuron responses for the fishbone surface patterns. Based on these results, we propose that both the frequency and the asynchrony of temporal activity in sensory neurons could produce tactile form perception.

Bio-inspired Haptic Feedback for Artificial Palpation in Robotic Surgery

Adding haptic feedback has been reported to improve the outcome of minimally invasive robotic surgery. In this study, we seek to determine whether an algorithm based on simulating responses of a cutaneous afferent population can be implemented to improve the performance of presenting haptic feedback for robot-assisted surgery. We propose a bio-inspired controlling model to present vibration and force feedback to help surgeons localize underlying structures in phantom tissue. A single pair of actuators was controlled by outputs of a model of a population of cutaneous afferents based on the pressure signal from a single sensor embedded in surgical forceps. We recruited 25 subjects including 10 expert surgeons to evaluate the performance of the bio-inspired controlling model in an artificial palpation task using the da Vinci surgical robot. Among the control methods tested, the bio-inspired system was unique in allowing both novices and experts to easily identify the locations of all classes of tumors and did so with reduced contact force and tumor contact time. This work demonstrates the utility of our bio-inspired multi-modal feedback system, which resulted in superior performance for both novice and professional users, in comparison to a traditional linear and the existing piecewise discrete algorithms of haptic feedback.

High-resolution imaging of skin deformation shows that afferents from human fingertips signal slip onset

Human tactile afferents provide essential feedback for grasp stability during dexterous object manipulation. Interacting forces between an object and the fingers induce slip events that are thought to provide information about grasp stability. To gain insight into this phenomenon, we made a transparent surface slip against a fixed fingerpad while monitoring skin deformation at the contact. Using microneurography, we simultaneously recorded the activity of single tactile afferents innervating the fingertips. This unique combination allowed us to describe how afferents respond to slip events and to relate their responses to surface deformations taking place inside their receptive fields. We found that all afferents were sensitive to slip events, but fast-adapting type I (FA-I) afferents in particular faithfully encoded compressive strain rates resulting from those slips. Given the high density of FA-I afferents in fingerpads, they are well suited to detect incipient slips and to provide essential information for the control of grip force during manipulation.

Each fingertip hosts thousands of nerve fibers that allow us to handle objects with great dexterity. These fibers relay the amount of friction between the skin and the item, and the brain uses this sensory feedback to adjust the grip as necessary. Yet, exactly how tactile nerve fibers encode information about friction remains largely unknown.

Previous research has suggested that friction might not be recorded per se in nerve signals to the brain. Instead, fibers in the finger pad might be responding to localized ‘partial slips’ that indicate an impending loss of grip. Indeed, when lifting an object, fingertips are loaded with a tangential force that puts strain on the skin, resulting in subtle local deformations. Nerve fibers might be able to detect these skin changes, prompting the brain to adjust an insecure grip before entirely losing grasp of an object.

However, technical challenges have made studying the way tactile nerve fibers respond to slippage and skin strain difficult. For the first time, Delhaye et al. have now investigated how these fibers respond to and encode information about the strain placed on fingertips as they are loaded tangentially. A custom-made imaging apparatus was paired with standard electrodes to record the activity of four different kinds of tactile nerve fibers in participants who had a fingertip placed against a plate of glass. The imaging focused on revealing changes in skin surface as tangential force was applied; the electrodes measured impulses from individual nerve fibers from the fingertip. While all the fibers responded during partial slips, fast-adapting type 1 nerves generated strong responses that signal a local loss of grip. Recordings showed that these fibers consistently encoded changes in the skin strain patterns, and were more sensitive to skin compressions related to slippage than to stretch.

These results show how tactile nerve fibers encode the subtle skin compressions created when fingers handle objects. The methods developed by Delhaye et al. could further be used to explore the response properties of tactile nerve fibers, sensory feedback and grip.

A Simplified Model for Simulating Population Responses of Tactile Afferents and Receptors in the Skin

Tactile information about an object can only be extracted from population responses of tactile receptors and their afferents. Thus, to best control tactile information in robots, neuroprostheses or haptic devices, inputs should represent responses from full populations of afferents. Here, we describe a simplified model that recreates afferent population responses of thousands of tactile afferents in a personal computer. The whole model includes a resistance network model to simplify the skin mechanics and an improved version of a single unit model that we have previously described. The whole model was implemented by short and efficient python code. The parameters of the model were fit based on a simple vibrating stimulus, but the simulated outputs generalize to match receptive field sizes, edge enhancement, and neurophysiological responses to dot textures, embossed letters and curved surfaces. We discuss how to use this work to model haptic perception and provide guidance in designing and controlling highly realistic tactile interfaces in robots, neural prostheses and haptic devices.

Orientation processing by synaptic integration across first-order tactile neurons

Our ability to manipulate objects relies on tactile inputs from first-order tactile neurons that innervate the glabrous skin of the hand. The distal axon of these neurons branches in the skin and innervates many mechanoreceptors, yielding spatially-complex receptive fields. Here we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. In particular, our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales. Taken together, our results provide new insight into the computations occurring in the earliest stages of the human tactile processing pathway and how they may be critical for supporting hand function.

A Python Code for Simulating Single Tactile Receptors and the Spiking Responses of Their Afferents

This work presents a pieces of Python code to rapidly simulate the spiking responses of large numbers of single cutaneous tactile afferents with millisecond precision. To simulate the spike responses of all the major types of cutaneous tactile afferents, we proposed an electromechanical circuit model, in which a two-channel filter was developed to characterize the mechanical selectivity of tactile receptors, and a spike synthesizer was designed to recreate the action potentials evoked in afferents. The parameters of this model were fitted using previous neurophysiological datasets. Several simulation examples were presented in this paper to reproduce action potentials, sensory adaptation, frequency characteristics and spiking timing for each afferent type. The results indicated that the simulated responses matched previous neurophysiological recordings well. The model allows for a real-time reproduction of the spiking responses of about 4,000 tactile units with a timing precision of <6 ms. The current work provides a valuable guidance to designing highly realistic tactile interfaces such as neuroprosthesis and haptic devices.

Rapid geometric feature signaling in the simulated spiking activity of a complete population of tactile nerve fibers

Tactile feature extraction is essential to guide the dexterous manipulation of objects. The longstanding theory is that geometric features at each location of contact between hand and object are extracted from the spatial layout of the response of populations of tactile nerve fibers. However, recent evidence suggests that some features (e.g., edge orientation) are extracted very rapidly (<200 ms), casting doubt that this information relies on a spatial code, which ostensibly requires integrating responses over time. An alternative hypothesis is that orientation is conveyed in precise temporal spiking patterns. Here we simulate, using a recently developed and validated model, the responses of the two relevant subpopulations of tactile fibers from the entire human fingertip (~800 afferents) to edges indented into the skin. We show that edge orientation can be quickly (<50 ms) and accurately (<3°) decoded from the spatial pattern of activation across the afferent population, starting with the very first spike. Next, we implement a biomimetic decoder of edge orientation, consisting of a bank of oriented Gabor filters, designed to mimic the documented responses of cortical neurons. We find that the biomimetic approach leads to orientation decoding performance that approaches the limit set by optimal decoders and is actually more robust to changes in other stimulus features. Finally, we show that orientation signals, measured from single units in the somatosensory cortex of nonhuman primates (2 macaque monkeys, 1 female), follow a time course consistent with that of their counterparts in the nerve. We conclude that a spatial code is fast and accurate enough to support object manipulation. NEW & NOTEWORTHY The dexterous manipulation of objects relies on the rapid and accurate extraction of the objects' geometric features by the sense of touch. Here we simulate the responses of all the nerve fibers that innervate the fingertip when an edge is indented into the skin and characterize the time course over which signals about its orientation evolve in this neural population. We show that orientation can be rapidly and accurately decoded from the spatial pattern of afferent activation using spatial filters that mimic the response properties of neurons in cortical somatosensory neurons along a time course consistent with that observed in cortex. We conclude that the classical model of tactile feature extraction is rapid and accurate enough to support object manipulation.

Fast and accurate edge orientation processing during object manipulation

Quickly and accurately extracting information about a touched object’s orientation is a critical aspect of dexterous object manipulation. However, the speed and acuity of tactile edge orientation processing with respect to the fingertips as reported in previous perceptual studies appear inadequate in these respects. Here we directly establish the tactile system’s capacity to process edge-orientation information during dexterous manipulation. Participants extracted tactile information about edge orientation very quickly, using it within 200 ms of first touching the object. Participants were also strikingly accurate. With edges spanning the entire fingertip, edge-orientation resolution was better than 3° in our object manipulation task, which is several times better than reported in previous perceptual studies. Performance remained impressive even with edges as short as 2 mm, consistent with our ability to precisely manipulate very small objects. Taken together, our results radically redefine the spatial processing capacity of the tactile system.

Putting on a necklace requires using your fingertips to hold open a clasp, which you then insert into a small ring. For you to do this, your nervous system must first work out which way the clasp and the ring are facing relative to one another. It then uses that information to coordinate the movements of your fingertips. If you fasten the necklace behind your head, your nervous system must perform these tasks without information from your eyes. Instead, it must use the way in which the edges of the clasp and the ring indent the skin on your fingertips to work out their orientation.

Earlier studies have examined this process by asking healthy volunteers to judge the orientation of objects – or more precisely edges – that an experimenter has pressed against their fingertips. But people perform worse than expected on this task given their manual dexterity. Pruszynski et al. wondered whether the task might underestimate the abilities of the volunteers because it involves passively perceiving objects, rather than actively manipulating them.

To test this idea, Pruszynski et al. designed a new experiment. Healthy volunteers were asked to use a fingertip to rotate a pointer on a dial to a target location. The participants could not see the dial, and so they had to use touch alone to determine which way the pointer was facing. They performed the task faster and more accurately than volunteers in the earlier passive experiments. Indeed, when the pointer was longer than a fingertip, the volunteers performed almost as well using touch alone as when allowed to look at the dial. Speed and accuracy remained impressive even when the pointer was only 2mm long.

The results of Pruszynski et al. show that we judge orientation more accurately when we manipulate objects than when we passively perceive them. In other words, we do better when we perform tasks in which being aware of orientation is vital. The results also suggest that the nervous system processes sensory information in different ways when it uses sensations to help control objects as opposed to just perceiving them. This could influence the development of new technology that aims to use brain activity to control computers or robotic limbs.

The Effect of Contact Force on the Responses of Tactile Nerve Fibers to Scanned Textures

The perception of fine textures relies on highly precise and repeatable spiking patterns evoked in tactile afferents. These patterns have been shown to depend not only on the surface microstructure and material but also on the speed at which it moves across the skin. Interestingly, the perception of texture is independent of scanning speed, implying the existence of downstream neural mechanisms that correct for scanning speed in interpreting texture signals from the periphery. What force is applied during texture exploration also has negligible effects on how the surface is perceived, but the consequences of changes in contact force on the neural responses to texture have not been described. In the present study, we measure the signals evoked in tactile afferents of macaques to a diverse set of textures scanned across the skin at two different contact forces and find that responses are largely independent of contact force over the range tested. We conclude that the force invariance of texture perception reflects the force independence of texture representations in the nerve.

Simulating tactile signals from the whole hand with millisecond precision

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.