Edge-orientation processing in first-order tactile neurons

Emotional contextual effects of face perception: a test of the affective realism hypothesis

Affective feelings naturally infuse individuals' perceptions, serving as valid windows onto the real world. The affective realism hypothesis further explains how these feelings work: as properties of individuals' perceptual experiences, these feelings influence perception. Notably, this hypothesis based on affective feelings with different valences has been substantiated, whereas the existing evidence is not compelling enough. Moreover, whether specific affective feelings can be experienced as properties of target perception remains unclear. Addressing these two issues deepens our understanding of the nature of emotional representation. Hence, we investigated the affective realism hypothesis based on affective feelings with different valences and specific emotions, comparing it with the affective misattribution hypothesis. In Experiment 1, we examined the effects of affective feelings with various valences on targets' perception through the AM (1a) and CFS paradigms (1b). In Experiment 2, we investigated the effects of affective feelings with anger, sadness, and disgust using similar methods. Results from Experiments 1a and 1b consistently indicated significant differences in valence ratings of neutral faces under emotional contexts with varying valences. Experiment 2a revealed significant differences in specific emotion ratings of neutral faces under different specific emotional contexts in the AM paradigm, whereas such differences were not observed in the CFS paradigm in Experiment 2b. We concluded that affective feelings with different valences, rather than specific emotions, can be experienced as inherent properties of target perception, validating the affective realism hypothesis. These findings supported the view that the nature of emotional representation should be described as affective dimensions.

Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional Neural Networks

Multifingered robot hands can be extremely effective in physically exploring and recognizing objects, especially if they are extensively covered with distributed tactile sensors. Convolutional neural networks (CNNs) have been proven successful in processing high dimensional data, such as camera images, and are, therefore, very well suited to analyze distributed tactile information as well. However, a major challenge is to organize tactile inputs coming from different locations on the hand in a coherent structure that could leverage the computational properties of the CNN. Therefore, we introduce a morphology-specific CNN (MS-CNN), in which hierarchical convolutional layers are formed following the physical configuration of the tactile sensors on the robot. We equipped a four-fingered Allegro robot hand with several uSkin tactile sensors; overall, the hand is covered with 240 sensitive elements, each one measuring three-axis contact force. The MS-CNN layers process the tactile data hierarchically: at the level of small local clusters first, then each finger, and then the entire hand. We show experimentally that, after training, the robot hand can successfully recognize objects by a single touch, with a recognition rate of over 95%. Interestingly, the learned MS-CNN representation transfers well to novel tasks: by adding a limited amount of data about new objects, the network can recognize nine types of physical properties.

Impact of Planar and Vertical Organic Field-Effect Transistors on Flexible Electronics

The development of flexible and conformable devices, whose performance can be maintained while being continuously deformed, provides a significant step toward the realization of next-generation wearable and e-textile applications. Organic field-effect transistors (OFETs) are particularly interesting for flexible and lightweight products, because of their low-temperature solution processability, and the mechanical flexibility of organic materials that endows OFETs the natural compatibility with plastic and biodegradable substrates. Here, an in-depth review of two competing flexible OFET technologies, planar and vertical OFETs (POFETs and VOFETs, respectively) is provided. The electrical, mechanical, and physical properties of POFETs and VOFETs are critically discussed, with a focus on four pivotal applications (integrated logic circuits, light-emitting devices, memories, and sensors). It is pointed out that the flexible function of the relatively newer VOFET technology, along with its perspective on advancing the applicability of flexible POFETs, has not been reviewed so far, and the direct comparison regarding the performance of POFET- and VOFET-based flexible applications is most likely absent. With discussions spanning printed and wearable electronics, materials science, biotechnology, and environmental monitoring, this contribution is a clear stimulus to researchers working in these fields to engage toward the plentiful possibilities that POFETs and VOFETs offer to flexible electronics.

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.

Texture recognition based on multi-sensory integration of proprioceptive and tactile signals

The sense of touch plays a fundamental role in enabling us to interact with our surrounding environment. Indeed, the presence of tactile feedback in prostheses greatly assists amputees in doing daily tasks. In this line, the present study proposes an integration of artificial tactile and proprioception receptors for texture discrimination under varying scanning speeds. Here, we fabricated a soft biomimetic fingertip including an 8 × 8 array tactile sensor and a piezoelectric sensor to mimic Merkel, Meissner, and Pacinian mechanoreceptors in glabrous skin, respectively. A hydro-elastomer sensor was fabricated as an artificial proprioception sensor (muscle spindles) to assess the instantaneous speed of the biomimetic fingertip. In this study, we investigated the concept of the complex receptive field of RA-I and SA-I afferents for naturalistic textures. Next, to evaluate the synergy between the mechanoreceptors and muscle spindle afferents, ten naturalistic textures were manipulated by a soft biomimetic fingertip at six different speeds. The sensors' outputs were converted into neuromorphic spike trains to mimic the firing pattern of biological mechanoreceptors. These spike responses are then analyzed using machine learning classifiers and neural coding paradigms to explore the multi-sensory integration in real experiments. This synergy between muscle spindle and mechanoreceptors in the proposed neuromorphic system represents a generalized texture discrimination scheme and interestingly irrespective of the scanning speed.

Bioengineered nerve conduits and wraps for peripheral nerve repair of the upper limb

BACKGROUND

Traumatic peripheral nerve injury is common and incurs significant cost to individuals and society. Healing following direct nerve repair or repair with autograft is slow and can be incomplete. Several bioengineered nerve wraps or devices have become available as an alternative to direct repair or autologous nerve graft. Nerve wraps attempt to reduce axonal escape across a direct repair site and nerve devices negate the need for a donor site defect, required by an autologous nerve graft. Comparative evidence to guide clinicians in their potential use is lacking. We collated existing evidence to guide the clinical application of currently available nerve wraps and conduits.

OBJECTIVES

To assess and compare the effects and complication rates of licensed bioengineered nerve conduits or wraps for surgical repair of traumatic peripheral nerve injuries of the upper limb. To compare effects and complications against the current gold surgical standard (direct repair or nerve autograft).

SEARCH METHODS

We used standard, extensive Cochrane search methods. The latest search was 26 January 2022. We searched online and, where not accessible, contacted societies' secretariats to review abstracts from the British Surgical Society of the Hand, International Federation of Surgical Societies of the Hand, Federation of European Surgical Societies of the Hand, and the American Society for Peripheral Nerve from October 2007 to October 2018.

SELECTION CRITERIA

We included parallel group randomised controlled trials (RCTs) and quasi-RCTs of nerve repair in the upper limb using a bioengineered wrap or conduit, with at least 12 months of follow-up.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane procedures. Our primary outcomes were 1. muscle strength and 2. sensory recovery at 24 months or more. Our secondary outcomes were 3. British Medical Research Council (BMRC) grading, 4. integrated functional outcome (Rosén Model Instrument (RMI)), 5. touch threshold, 6. two-point discrimination, 7. cold intolerance, 8. impact on daily living measured using the Disability of Arm Shoulder and Hand Patient-Reported Outcome Measure (DASH-PROM), 9. sensory nerve action potential, 10. cost of the device, and 11. adverse events (any and specific serious adverse events (further surgery)). We used GRADE to assess the certainty of the evidence.

MAIN RESULTS

Five studies involving 213 participants and 257 nerve injuries reconstructed with wraps or conduits (129 participants) or standard repair (128 participants) met the inclusion criteria. Of those in the standard repair group, 119 nerve injuries were managed with direct epineurial repair, and nine autologous nerve grafts were performed. One study excluded the outcome data for the repair using an autologous nerve graft from their analysis, as it was the only autologous nerve graft in the study, so data were available for 127 standard repairs. There was variation in the functional outcome measures reported and the time postoperatively at which they were recorded. Mean sensory recovery, assessed with BMRC sensory grading (range S0 to S4, higher score considered better) was 0.03 points higher in the device group (range 0.43 lower to 0.49 higher; 1 RCT, 28 participants; very low-certainty evidence) than in the standard repair group (mean 2.75 points), which suggested little or no difference between the groups, but the evidence is very uncertain. There may be little or no difference at 24 months in mean touch thresholds between standard repair (0.81) and repair using devices, which was 0.01 higher but this evidence is also very uncertain (95% confidence interval (CI) 0.06 lower to 0.08 higher; 1 trial, 32 participants; very low-certainty evidence). Data were not available to assess BMRC motor grading at 24 months or more. Repair using bioengineered devices may not improve integrated functional outcome scores at 24 months more than standard techniques, as assessed by the Rosén Model Instrument (RMI; range 0 to 3, higher scores better); the CIs allow for both no important difference and a better outcome with standard repair (mean RMI 1.875), compared to the device group (0.17 lower, 95% CI 0.38 lower to 0.05 higher; P = 0.13; 2 trials, 60 participants; low-certainty evidence). Data from one study suggested that the five-year postoperative outcome of RMI may be slightly improved after repair using a device (mean difference (MD) 0.23, 95% CI 0.07 to 0.38; 1 trial, 28 participants; low-certainty evidence). No studies measured impact on daily living using DASH-PROM. The proportion of people with adverse events may be greater with nerve wraps or conduits than with standard techniques, but the evidence is very uncertain (risk ratio (RR) 7.15, 95% CI 1.74 to 29.42; 5 RCTs, 213 participants; very low-certainty evidence). This corresponds to 10 adverse events per 1000 people in the standard repair group and 68 per 1000 (95% CI 17 to 280) in the device group. The use of nerve repair devices may be associated with a greater need for revision surgery but this evidence is also very uncertain (12/129 device repairs required revision surgery (removal) versus 0/127 standard repairs; RR 7.61, 95% CI 1.48 to 39.02; 5 RCTs, 256 nerve repairs; very low-certainty evidence).

AUTHORS' CONCLUSIONS

Based on the available evidence, this review does not support use of currently available nerve repair devices over standard repair. There is significant heterogeneity in participants, injury pattern, repair timing, and outcome measures and their timing across studies of nerve repair using bioengineered devices, which make comparisons unreliable. Studies were generally small and at high or unclear risk of bias. These factors render the overall certainty of evidence for any outcome low or very low. The data reviewed here provide some evidence that more people may experience adverse events with use of currently available bioengineered devices than with standard repair techniques, and the need for revision surgery may also be greater. The evidence for sensory recovery is very uncertain and there are no data for muscle strength at 24 months (our primary outcome measures). We need further trials, adhering to a minimum standard of outcome reporting (with at least 12 months' follow-up, including integrated sensorimotor evaluation and patient-reported outcomes) to provide high-certainty evidence and facilitate more detailed analysis of effectiveness of emerging, increasingly sophisticated, bioengineered repair devices.

Precise and stable edge orientation signaling by human first-order tactile neurons

Fast-adapting type 1 (FA-1) and slow-adapting type 1 (SA-1) first-order neurons in the human tactile system have distal axons that branch in the skin and form many transduction sites, yielding receptive fields with many highly sensitive zones or 'subfields.' We previously demonstrated that this arrangement allows FA-1 and SA-1 neurons to signal the geometric features of touched objects, specifically the orientation of raised edges scanned with the fingertips. Here, we show that such signaling operates for fine edge orientation differences (5-20°) and is stable across a broad range of scanning speeds (15-180 mm/s); that is, under conditions relevant for real-world hand use. We found that both FA-1 and SA-1 neurons weakly signal fine edge orientation differences via the intensity of their spiking responses and only when considering a single scanning speed. Both neuron types showed much stronger edge orientation signaling in the sequential structure of the evoked spike trains, and FA-1 neurons performed better than SA-1 neurons. Represented in the spatial domain, the sequential structure was strikingly invariant across scanning speeds, especially those naturally used in tactile spatial discrimination tasks. This speed invariance suggests that neurons' responses are structured via sequential stimulation of their subfields and thus links this capacity to their terminal organization in the skin. Indeed, the spatial precision of elicited action potentials rationally matched spatial acuity of subfield arrangements, which corresponds to a spatial period similar to the dimensions of individual fingertip ridges.

Structured random receptive fields enable informative sensory encodings

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

Neuromorphic Tactile Edge Orientation Classification in an Unsupervised Spiking Neural Network

Dexterous manipulation in robotic hands relies on an accurate sense of artificial touch. Here we investigate neuromorphic tactile sensation with an event-based optical tactile sensor combined with spiking neural networks for edge orientation detection. The sensor incorporates an event-based vision system (mini-eDVS) into a low-form factor artificial fingertip (the NeuroTac). The processing of tactile information is performed through a Spiking Neural Network with unsupervised Spike-Timing-Dependent Plasticity (STDP) learning, and the resultant output is classified with a 3-nearest neighbours classifier. Edge orientations were classified in 10-degree increments while tapping vertically downward and sliding horizontally across the edge. In both cases, we demonstrate that the sensor is able to reliably detect edge orientation, and could lead to accurate, bio-inspired, tactile processing in robotics and prosthetics applications.

Physiological noise facilitates multiplexed coding of vibrotactile-like signals in somatosensory cortex

Neurons can use different aspects of their spiking to simultaneously represent (multiplex) different features of a stimulus. For example, some pyramidal neurons in primary somatosensory cortex (S1) use the rate and timing of their spikes to, respectively, encode the intensity and frequency of vibrotactile stimuli. Doing so has several requirements. Because they fire at low rates, pyramidal neurons cannot entrain 1:1 with high-frequency (100 to 600 Hz) inputs and, instead, must skip (i.e., not respond to) some stimulus cycles. The proportion of skipped cycles must vary inversely with stimulus intensity for firing rate to encode stimulus intensity. Spikes must phase-lock to the stimulus for spike times (intervals) to encode stimulus frequency, but, in addition, skipping must occur irregularly to avoid aliasing. Using simulations and in vitro experiments in which mouse S1 pyramidal neurons were stimulated with inputs emulating those induced by vibrotactile stimuli, we show that fewer cycles are skipped as stimulus intensity increases, as required for rate coding, and that intrinsic or synaptic noise can induce irregular skipping without disrupting phase locking, as required for temporal coding. This occurs because noise can modulate the reliability without disrupting the precision of spikes evoked by small-amplitude, fast-onset signals. Specifically, in the fluctuation-driven regime associated with sparse spiking, rate and temporal coding are both paradoxically improved by the strong synaptic noise characteristic of the intact cortex. Our results demonstrate that multiplexed coding by S1 pyramidal neurons is not only feasible under in vivo conditions, but that background synaptic noise is actually beneficial.

Neural dynamics of illusory tactile pulling sensations

Directional tactile pulling sensations are integral to everyday life, but their neural mechanisms remain unknown. Prior accounts hold that primary somatosensory (SI) activity is sufficient to generate pulling sensations, with alternative proposals suggesting that amodal frontal or parietal regions may be critical. We combined high-density EEG with asymmetric vibration, which creates an illusory pulling sensation, thereby unconfounding pulling sensations from unrelated sensorimotor processes. Oddballs that created opposite direction pulls to common stimuli were compared to the same oddballs after neutral common stimuli (symmetric vibration) and to neutral oddballs. We found evidence against the sensory-frontal N140 and in favor of the midline P200 tracking the emergence of pulling sensations, specifically contralateral parietal lobe activity 264-320ms, centered on the intraparietal sulcus. This suggests that SI is not sufficient to generate pulling sensations, which instead depend on the parietal association cortex, and may reflect the extraction of orientation information and related spatial processing.

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Tactile pulling sensations are difficult to isolate in the human brain

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Illusory pulls from asymmetric vibration allow neural activity to be isolated

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Pulling sensations are driven by parietal lobe activity 264-320ms post-stimulus

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Spatial processing in the parietal lobe may be essential for pulling sensations

Human muscle spindles are wired to function as controllable signal-processing devices

Muscle spindles are encapsulated sensory organs found in most of our muscles. Prevalent models of sensorimotor control assume the role of spindles is to reliably encode limb posture and movement. Here, I argue that the traditional view of spindles is outdated. Spindle organs can be tuned by spinal γ motor neurons that receive top-down and peripheral input, including from cutaneous afferents. A new model is presented, viewing γ motor activity as an intermediate coordinate transformation that allows multimodal information to converge on spindles, creating flexible coordinate representations at the level of the peripheral nervous system. That is, I propose that spindles play a unique overarching role in the nervous system: that of a peripheral signal-processing device that flexibly facilitates sensorimotor performance, according to task characteristics. This role is compatible with previous findings and supported by recent studies with naturalistically active humans. Such studies have so far shown that spindle tuning enables the independent preparatory control of reflex muscle stiffness, the selective extraction of information during implicit motor adaptation, and for segmental stretch reflexes to operate in joint space. Incorporation of advanced signal-processing at the periphery may well prove a critical step in the evolution of sensorimotor control theories.

Multi-digit tactile perception I: motion integration benefits for tactile trajectories presented bimanually

Interactions with objects involve simultaneous contact with multiple, not necessarily adjacent, skin regions. While advances have been made in understanding the capacity to selectively attend to a single tactile element among distracting stimulations, here, we examine how multiple stimulus elements are explicitly integrated into an overall tactile percept. Across four experiments, participants averaged the direction of two simultaneous tactile motion trajectories of varying discrepancy delivered to different fingerpads. Averaging performance differed between within- and between-hands conditions in terms of sensitivity and precision but was unaffected by somatotopic proximity between stimulated fingers. First, precision was greater in between-hand compared to within-hand conditions, demonstrating a bimanual perceptual advantage in multi-touch integration. Second, sensitivity to the average direction was influenced by the discrepancy between individual motion signals, but only for within-hand conditions. Overall, our experiments identify key factors that influence perception of simultaneous tactile events. In particular, we show that multi-touch integration is constrained by hand-specific rather than digit-specific mechanisms.

Neuro-inspired electronic skin for robots

Touch is a complex sensing modality owing to large number of receptors (mechano, thermal, pain) nonuniformly embedded in the soft skin all over the body. These receptors can gather and encode the large tactile data, allowing us to feel and perceive the real world. This efficient somatosensation far outperforms the touch-sensing capability of most of the state-of-the-art robots today and suggests the need for neural-like hardware for electronic skin (e-skin). This could be attained through either innovative schemes for developing distributed electronics or repurposing the neuromorphic circuits developed for other sensory modalities such as vision and audio. This Review highlights the hardware implementations of various computational building blocks for e-skin and the ways they can be integrated to potentially realize human skin-like or peripheral nervous system-like functionalities. The neural-like sensing and data processing are discussed along with various algorithms and hardware architectures. The integration of ultrathin neuromorphic chips for local computation and the printed electronics on soft substrate used for the development of e-skin over large areas are expected to advance robotic interaction as well as open new avenues for research in medical instrumentation, wearables, electronics, and neuroprosthetics.

Printed synaptic transistor-based electronic skin for robots to feel and learn

An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body-like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system-like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.

Skin and Mechanoreceptor Contribution to Tactile Input for Perception: A Review of Simulation Models

We review four current computational models that simulate the response of mechanoreceptors in the glabrous skin to tactile stimulation. The aim is to inform researchers in psychology, sensorimotor science and robotics who may want to implement this type of quantitative model in their research. This approach proves relevant to understanding of the interaction between skin response and neural activity as it avoids some of the limitations of traditional measurement methods of tribology, for the skin, and neurophysiology, for tactile neurons. The main advantage is to afford new ways of looking at the combined effects of skin properties on the activity of a population of tactile neurons, and to examine different forms of coding by tactile neurons. Here, we provide an overview of selected models from stimulus application to neuronal spiking response, including their evaluation in terms of existing data, and their applicability in relation to human tactile perception.

A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods

Multifingered robotic hands (usually referred to as dexterous hands) are designed to achieve human-level or human-like manipulations for robots or as prostheses for the disabled. The research dates back 30 years ago, yet, there remain great challenges to effectively design and control them due to their high dimensionality of configuration, frequently switched interaction modes, and various task generalization requirements. This article aims to give a brief overview of multifingered robotic manipulation from three aspects: a) the biological results, b) the structural evolvements, and c) the learning methods, and discuss potential future directions. First, we investigate the structure and principle of hand-centered visual sensing, tactile sensing, and motor control and related behavioral results. Then, we review several typical multifingered dexterous hands from task scenarios, actuation mechanisms, and in-hand sensors points. Third, we report the recent progress of various learning-based multifingered manipulation methods, including but not limited to reinforcement learning, imitation learning, and other sub-class methods. The article concludes with open issues and our thoughts on future directions.

Malleability of the cortical hand map following a finger nerve block

Electrophysiological studies in monkeys show that finger amputation triggers local remapping within the deprived primary somatosensory cortex (S1). Human neuroimaging research, however, shows persistent S1 representation of the missing hand's fingers, even decades after amputation. Here, we explore whether this apparent contradiction stems from underestimating the distributed peripheral and central representation of fingers in the hand map. Using pharmacological single-finger nerve block and 7-tesla neuroimaging, we first replicated previous accounts (electrophysiological and other) of local S1 remapping. Local blocking also triggered activity changes to nonblocked fingers across the entire hand area. Using methods exploiting interfinger representational overlap, however, we also show that the blocked finger representation remained persistent despite input loss. Computational modeling suggests that both local stability and global reorganization are driven by distributed processing underlying the topographic map, combined with homeostatic mechanisms. Our findings reveal complex interfinger representational features that play a key role in brain (re)organization, beyond (re)mapping.

Global surface features contribute to human haptic roughness estimations

Previous studies have paid special attention to the relationship between local features (e.g., raised dots) and human roughness perception. However, the relationship between global features (e.g., curved surface) and haptic roughness perception is still unclear. In the present study, a series of roughness estimation experiments was performed to investigate how global features affect human roughness perception. In each experiment, participants were asked to estimate the roughness of a series of haptic stimuli that combined local features (raised dots) and global features (sinusoidal-like curves). Experiments were designed to reveal whether global features changed their haptic roughness estimation. Furthermore, the present study tested whether the exploration method (direct, indirect, and static) changed haptic roughness estimations and examined the contribution of global features to roughness estimations. The results showed that sinusoidal-like curved surfaces with small periods were perceived to be rougher than those with large periods, while the direction of finger movement and indirect exploration did not change this phenomenon. Furthermore, the influence of global features on roughness was modulated by local features, regardless of whether raised-dot surfaces or smooth surfaces were used. Taken together, these findings suggested that an object’s global features contribute to haptic roughness perceptions, while local features change the weight of the contribution that global features make to haptic roughness perceptions.

Distribution of Large and Small Dorsal Root Ganglion Neurons in Common Marmosets

The aim of this study was to elucidate the size and distribution of dorsal root ganglion (DRG) neurons in non-human primates and to compare them with those of rodent DRG neurons. By measuring the size of NeuN-, NF200-, and peripherin-positive DRG neurons in the lumbar spinal cord of rats and marmosets, we found that the cell size distribution pattern was comparable in both species, although DRG neurons in marmosets were larger than those of rodents. This is the first demonstration that DRG neurons in marmosets have a bimodal size distribution, which has been well established in rodents and humans.

Do epidermal keratinocytes have sensory and information processing systems?

It was long considered that the role of epidermal keratinocytes is solely to construct a water-impermeable protective membrane, the stratum corneum, at the uppermost layer of the skin. However, in the last two decades, it has been found that keratinocytes contain multiple sensory systems that detect environmental changes, including mechanical stimuli, sound, visible radiation, electric fields, magnetic fields, temperature and chemical stimuli, and also a variety of receptor molecules associated with olfactory or taste sensation. Moreover, neurotransmitters and their receptors that play crucial roles in the brain are functionally expressed in keratinocytes. Recent studies have demonstrated that excitation of keratinocytes can induce sensory perception in the brain. Here, we review the sensory and information processing capabilities of keratinocytes. We discuss the possibility that epidermal keratinocytes might represent the earliest stage in the development of the brain during the evolution of vertebrates.

A Biomimetic Circuit for Electronic Skin With Application in Hand Prosthesis

One major challenge in upper limb prostheses is providing sensory feedback to amputees. Reproducing the spiking patterns of human primary tactile afferents can be considered as the first step for this challenging problem. In this study, a novel biomimetic circuit for SA-I and RA-I afferents is proposed to functionally replicate the spiking response of the biological tactile afferents to indentation stimuli. The circuit has been designed, laid out, and simulated in TSMC 180nm CMOS technology with a 1.8V supply voltage. A pair of SA-I and RA-I afferent circuits consume [Formula: see text] of power. The occupied silicon area is [Formula: see text] for 32 afferents. To provide the inputs for circuit testing, a patch of skin with a grid of mechanoreceptors is simulated and tested by an edge stimulus presented at different orientations. Experimental data are collected using indentation of 3D-printed edges at different orientations on a tactile sensor mounted on a robotic arm. Inspired by innervation patterns observed in biology, the artificial afferents are connected to several neighboring mechanoreceptors with different weights to form complex receptive fields which cover the entire mechanoreceptor grid. Machine learning algorithms are applied offline to classify the edge orientations based on the pattern of neural responses. Our results show that the complex receptive fields arising from the innervation pattern led to smaller circuit area and lower power consumption, while facilitating data encoding from high-resolution sensors. The proposed biomimetic circuit and tactile encoding example demonstrate potential applications in modern tactile sensing modules for developing novel bio-robotic and prosthetic technologies.

Humans Use a Temporally Local Code for Vibrotactile Perception

Sensory environments are commonly characterized by specific physical features, which sensory systems might exploit using dedicated processing mechanisms. In the tactile sense, one such characteristic feature is frictional movement, which gives rise to short-lasting (<10 ms), information-carrying integument vibrations. Rather than generic integrative encoding (i.e., averaging or spectral analysis capturing the "intensity" and "best frequency"), the tactile system might benefit from, what we call a "temporally local" coding scheme that instantaneously detects and analyzes shapes of these short-lasting features. Here, by employing analytic psychophysical measurements, we tested whether the prerequisite of temporally local coding exists in the human tactile system. We employed pulsatile skin indentations at the fingertip that allowed us to trade manipulation of local pulse shape against changes in global intensity and frequency, achieved by adding pulses of the same shape. We found that manipulation of local pulse shape has strong effects on psychophysical performance, arguing for the notion that humans implement a temporally local coding scheme for perceptual decisions. As we found distinct differences in performance using different kinematic layouts of pulses, we inquired whether temporally local coding is tuned to a unique kinematic variable. This was not the case, since we observed different preferred kinematic variables in different ranges of pulse shapes. Using an established encoding model for primary afferences and indentation stimuli, we were able to demonstrate that the found kinematic preferences in human performance, may well be explained by the response characteristics of Pacinian corpuscles (PCs), a class of human tactile primary afferents.

Synaptic Interactions in Scorpion Peg Sensilla Appear to Maintain Chemosensory Neurons within Dynamic Firing Range

Simple Summary

Scorpions have unusual taste organs called pectines that they drag over the ground as they walk. Minute, peg-shaped sensilla adorn the ground-facing surfaces of the pectines, and each of these “pegs” contains several chemosensitive neurons and at least one mechanosensitive neuron. Of particular interest is that some of these neurons interact synaptically at the level of the peg sensillum prior to relay to the scorpion brain. Here we use a technique called “conditional cross-interval correlation analysis” to show that heightened activity of two of the neurons appears to induce a third neuron, which in turn inhibits the previous two. We suggest that the dynamics of this simple feedback circuit might serve to maintain the sensory neurons in a sensitive range so that substrate information can be accurately detected and processed, such as during tracking sexual pheromone trails and/or recapitulating home-directed training paths.

Abstract

Scorpions have elaborate chemo-tactile organs called pectines on their ventral mesosoma. The teeth of the comb-like pectines support thousands of minute projections called peg sensilla (a.k.a. “pegs”), each containing approximately 10 chemosensory neurons. Males use pectines to detect pheromones released by females, and both sexes apparently use pectines to find prey and navigate to home retreats. Electrophysiological recordings from pegs of Paruroctonus utahensis reveal three spontaneously active cells (A₁, A₂, and B), which appear to interact synaptically. We made long-term extracellular recordings from the bases of peg sensilla and used a combination of conditional cross-interval and conditional interspike-interval analyses to assess the temporal dynamics of the A and B spike trains. Like previous studies, we found that A cells are inhibited by B cells for tens of milliseconds. However, after normalizing our records, we also found clear evidence that the A cells excite the B cells. This simple local circuit appears to maintain the A cells in a dynamic firing range and may have important implications for tracking pheromonal trails and sensing substrate chemistry for navigation.

The interaction between motion and texture in the sense of touch

Besides providing information on elementary properties of objects, like texture, roughness, and softness, the sense of touch is also important in building a representation of object movement and the movement of our hands. Neural and behavioral studies shed light on the mechanisms and limits of our sense of touch in the perception of texture and motion, and of its role in the control of movement of our hands. The interplay between the geometrical and mechanical properties of the touched objects, such as shape and texture, the movement of the hand exploring the object, and the motion felt by touch, will be discussed in this article. Interestingly, the interaction between motion and textures can generate perceptual illusions in touch. For example, the orientation and the spacing of the texture elements on a static surface induces the illusion of surface motion when we move our hand on it or can elicit the perception of a curved trajectory during sliding, straight hand movements. In this work we present a multiperspective view that encompasses both the perceptual and the motor aspects, as well as the response of peripheral and central nerve structures, to analyze and better understand the complex mechanisms underpinning the tactile representation of texture and motion. Such a better understanding of the spatiotemporal features of the tactile stimulus can reveal novel transdisciplinary applications in neuroscience and haptics.

The mechanosensory neurons of touch and their mechanisms of activation

Our sense of touch emerges from an array of mechanosensory structures residing within the fabric of our skin. These tactile end organ structures convert innocuous forces acting on the skin into electrical signals that propagate to the CNS via the axons of low-threshold mechanoreceptors (LTMRs). Our rich capacity for tactile discrimination arises from the dissimilar intrinsic properties of the LTMR subtypes that innervate different regions of the skin and the structurally distinct end organ complexes with which they associate. These end organ structures comprise a range of non-neuronal cell types, which may themselves actively contribute to the transformation of tactile forces into neural impulses within the LTMR afferents. Although the mechanism and the site of transduction across end organs remain unclear, PIEZO2 has emerged as the principal mechanosensitive channel involved in light touch of the skin. Here we review the physiological properties of LTMR subtypes and discuss how features of their cutaneous end organ complexes shape subtype-specific tuning.

Scanned optogenetic control of mammalian somatosensory input to map input-specific behavioral outputs

Somatosensory stimuli guide and shape behavior, from immediate protective reflexes to longer-term learning and higher-order processes related to pain and touch. However, somatosensory inputs are challenging to control in awake mammals due to the diversity and nature of contact stimuli. Application of cutaneous stimuli is currently limited to relatively imprecise methods as well as subjective behavioral measures. The strategy we present here overcomes these difficulties, achieving 'remote touch' with spatiotemporally precise and dynamic optogenetic stimulation by projecting light to a small defined area of skin. We mapped behavioral responses in freely behaving mice with specific nociceptor and low-threshold mechanoreceptor inputs. In nociceptors, sparse recruitment of single-action potentials shapes rapid protective pain-related behaviors, including coordinated head orientation and body repositioning that depend on the initial body pose. In contrast, activation of low-threshold mechanoreceptors elicited slow-onset behaviors and more subtle whole-body behaviors. The strategy can be used to define specific behavioral repertoires, examine the timing and nature of reflexes, and dissect sensory, motor, cognitive, and motivational processes guiding behavior.

Neuromorphic Devices for Bionic Sensing and Perception

Neuromorphic devices that can emulate the bionic sensory and perceptual functions of neural systems have great applications in personal healthcare monitoring, neuro-prosthetics, and human-machine interfaces. In order to realize bionic sensing and perception, it's crucial to prepare neuromorphic devices with the function of perceiving environment in real-time. Up to now, lots of efforts have been made in the incorporation of the bio-inspired sensing and neuromorphic engineering in the booming artificial intelligence industry. In this review, we first introduce neuromorphic devices based on diverse materials and mechanisms. Then we summarize the progress made in the emulation of biological sensing and perception systems. Finally, the challenges and opportunities in these fields are also discussed.

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.

The roles of lower- and higher-order surface statistics in tactile texture perception

Humans can haptically discriminate surface textures when there is a significant difference in the statistics of the surface profile. Previous studies on tactile texture discrimination have emphasized the perceptual effects of lower-order statistical features such as carving depth, inter-ridge distance, and anisotropy, which can be characterized by local amplitude spectra or spatial-frequency/orientation subband histograms. However, the real-world surfaces we encounter in everyday life also differ in the higher-order statistics, such as statistics about correlations of nearby spatial-frequencies/orientations. For another modality, vision, the human brain has the ability to use the textural differences in both higher- and lower-order image statistics. In this work, we examined whether the haptic texture perception can use higher-order surface statistics as visual texture perception does, by three-dimensional (3-D)-printing textured surfaces transcribed from different "photos" of natural scenes such as stones and leaves. Even though the maximum carving depth was well above the haptic detection threshold, some texture pairs were hard to discriminate. Specifically, those texture pairs with similar amplitude spectra were difficult to discriminate, which suggests that the lower-order statistics have the dominant effect on tactile texture discrimination. To directly test the poor sensitivity of the tactile texture perception to higher-order surface statistics, we matched the lower-order statistics across different textures using a texture synthesis algorithm and found that haptic discrimination of the matched textures was nearly impossible unless the stimuli contained salient local features. We found no evidence for the ability of the human tactile system to use higher-order surface statistics for texture discrimination.NEW & NOTEWORTHY Humans can discriminate subtle spatial patterns differences in the surrounding world through their hands, but the underlying computation remains poorly understood. Here, we 3-D-printed textured surfaces and analyzed the tactile discrimination performance regarding the sensitivity to surface statistics. The results suggest that observers have sensitivity to lower-order statistics whereas not to higher-order statistics. That is, touch differs from vision not only in spatiotemporal resolution but also in (in)sensitivity to high-level surface statistics.

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.

Artificial stimulus-response system capable of conscious response

A stimulus-response system and conscious response enable humans to respond effectively to environmental changes and external stimuli. This paper presents an artificial stimulus-response system that is inspired by human conscious response and is capable of emulating it. The system is composed of an artificial visual receptor, artificial synapse, artificial neuron circuits, and actuator. By incorporating these artificial nervous components, a series of conscious response processes that markedly reduces response time as a result of learning from repeated stimuli are demonstrated. The proposed artificial stimulus-response system offers the promise of a new research field that would aid the development of artificial intelligence-based organs for patients with neurological disorders.

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.

A review of the neurobiomechanical processes underlying secure gripping in object manipulation

O'SHEA, H. and S. J. Redmond. A review of the neurobiomechanical processes underlying secure gripping in object manipulation. NEUROSCI BIOBEHAV REV 286-300, 2021. Humans display skilful control over the objects they manipulate, so much so that biomimetic systems have yet to emulate this remarkable behaviour. Two key control processes are assumed to facilitate such dexterity: predictive cognitive-motor processes that guide manipulation procedures by anticipating action outcomes; and reactive sensorimotor processes that provide important error-based information for movement adaptation. Notwithstanding increased interdisciplinary research interest in object manipulation behaviour, the complexity of the perceptual-sensorimotor-cognitive processes involved and the theoretical divide regarding the fundamentality of control mean that the essential mechanisms underlying manipulative action remain undetermined. In this paper, following a detailed discussion of the theoretical and empirical bases for understanding human dexterous movement, we emphasise the role of tactile-related sensory events in secure object handling, and consider the contribution of certain biophysical and biomechanical phenomena. We aim to provide an integrated account of the current state-of-art in skilled human-object interaction that bridges the literature in neuroscience, cognitive psychology, and biophysics. We also propose novel directions for future research exploration in this area.

Sharpness recognition based on synergy between bio-inspired nociceptors and tactile mechanoreceptors

Touch and pain sensations are complementary aspects of daily life that convey crucial information about the environment while also providing protection to our body. Technological advancements in prosthesis design and control mechanisms assist amputees to regain lost function but often they have no meaningful tactile feedback or perception. In the present study, we propose a bio-inspired tactile system with a population of 23 digital afferents: 12 RA-I, 6 SA-I, and 5 nociceptors. Indeed, the functional concept of the nociceptor is implemented on the FPGA for the first time. One of the main features of biological tactile afferents is that their distal axon branches in the skin, creating complex receptive fields. Given these physiological observations, the bio-inspired afferents are randomly connected to the several neighboring mechanoreceptors with different weights to form their own receptive field. To test the performance of the proposed neuromorphic chip in sharpness detection, a robotic system with three-degree of freedom equipped with the tactile sensor indents the 3D-printed objects. Spike responses of the biomimetic afferents are then collected for analysis by rate and temporal coding algorithms. In this way, the impact of the innervation mechanism and collaboration of afferents and nociceptors on sharpness recognition are investigated. Our findings suggest that the synergy between sensory afferents and nociceptors conveys more information about tactile stimuli which in turn leads to the robustness of the proposed neuromorphic system against damage to the taxels or afferents. Moreover, it is illustrated that spiking activity of the biomimetic nociceptors is amplified as the sharpness increases which can be considered as a feedback mechanism for prosthesis protection. This neuromorphic approach advances the development of prosthesis to include the sensory feedback and to distinguish innocuous (non-painful) and noxious (painful) stimuli.

A functional spiking neuronal network for tactile sensing pathway to process edge orientation

To obtain deeper insights into the tactile processing pathway from a population-level point of view, we have modeled three stages of the tactile pathway from the periphery to the cortex in response to indentation and scanned edge stimuli at different orientations. Three stages in the tactile pathway are, (1) the first-order neurons which innervate the cutaneous mechanoreceptors, (2) the cuneate nucleus in the midbrain and (3) the cortical neurons of the somatosensory area. In the proposed network, the first layer mimics the spiking patterns generated by the primary afferents. These afferents have complex skin receptive fields. In the second layer, the role of lateral inhibition on projection neurons in the cuneate nucleus is investigated. The third layer acts as a biomimetic decoder consisting of pyramidal and cortical interneurons that correspond to heterogeneous receptive fields with excitatory and inhibitory sub-regions on the skin. In this way, the activity of pyramidal neurons is tuned to the specific edge orientations. By modifying afferent receptive field size, it is observed that the larger receptive fields convey more information about edge orientation in the first spikes of cortical neurons when edge orientation stimuli move across the patch of skin. In addition, the proposed spiking neural model can detect edge orientation at any location on the simulated mechanoreceptor grid with high accuracy. The results of this research advance our knowledge about tactile information processing and can be employed in prosthetic and bio-robotic applications.

Evidence for sparse C-tactile afferent innervation of glabrous human hand skin

C-tactile (CT) afferents were long-believed to be lacking in humans, but these were subsequently shown to densely innervate the face and arm skin, and to a lesser extent the leg. Their firing frequency to stroking touch at different velocities has been correlated with ratings of tactile pleasantness. CT afferents were thought to be absent in human glabrous skin; however, tactile pleasantness can be perceived across the whole body, including glabrous hand skin. We used microneurography to investigate mechanoreceptive afferents in the glabrous skin of the human hand, during median and radial nerve recordings. We describe CTs found in the glabrous skin, with characteristics comparable with those in hairy arm skin, and detail recordings from three such afferents. CTs were infrequently encountered in the glabrous skin and we estimate that the ratio of recorded CTs relative to myelinated mechanoreceptors (1:80) corresponds to an absolute innervation density of around seven times lower than in hairy skin. This sparse innervation sheds light on discrepancies between psychophysical findings of touch perception on glabrous skin and hairy skin, although the role of these CT afferents in the glabrous skin remains subject to future work.NEW & NOTEWORTHY Human touch is encoded by low-threshold mechanoreceptors, including myelinated Aβ afferents and unmyelinated C-tactile (CT) afferents. CTs are abundant in hairy skin and are thought to code gentle, stroking touch that signals positive affective interactions. CTs have never been described in human glabrous skin, yet we show evidence of their existence on the hand, albeit at a relatively low density. Glabrous skin CTs may provide modulatory reinforcement of gentle tactile interactions during touch using the hands.

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 Habituation Sensory Nervous System with Memristors

The sensory nervous system (SNS) builds up the association between external stimuli and the response of organisms. In this system, habituation is a fundamental characteristic that filters out irrelevantly repetitive information and makes the SNS adapt to the external environment. To emulate this critical process in electronic devices, a Li_x SiO_y -based memristor (TiN/Li_x SiO_y /Pt) is developed where the temporal response under repetitive stimulation is similar to that of habituation. By connecting this synaptic device to a leaky integrate-and-fire neuron based on a Ag/SiO₂ :Ag/Au memristor, a fully memristive SNS with habituation is experimentally demonstrated. Finally, a habituation spiking neural network based on the SNS is built and its application in obstacle avoidance for robot navigation is successfully presented. The results provide that a direct emulation of the biologically inspired learning process by memristors could be a sound choice for neuromorphic hardware implementation.

Tactile innervation densities across the whole body

The skin is our largest sensory organ and innervated by afferent fibers carrying tactile information to the spinal cord and onto the brain. The density with which different classes of tactile afferents innervate the skin is not constant but varies considerably across different body regions. However, precise estimates of innervation density are only available for some body parts, such as the hands, and estimates of the total number of tactile afferent fibers are inconsistent and incomplete. Here we reconcile different estimates and provide plausible ranges and best estimates for the number of different tactile fiber types innervating different regions of the skin, using evidence from dorsal root fiber counts, microneurography, histology, and psychophysics. We estimate that the skin across the whole body of young adults is innervated by ∼230,000 tactile afferent fibers (plausible range: 200,000–270,000), with a subsequent decrement of 5–8% every decade due to aging. Fifteen percent of fibers innervate the palmar skin of both hands and 19% the region surrounding the face and lips. Slowly and fast-adapting fibers are split roughly evenly, but this breakdown varies with skin region. Innervation density correlates well with psychophysical spatial acuity across different body regions, and, additionally, on hairy skin, with hair follicle density. Innervation density is also weakly correlated with the size of the cortical somatotopic representation but cannot fully account for the magnification of the hands and the face.

Behavioral and Neural Bases of Tactile Shape Discrimination Learning in Head-Fixed Mice

Tactile shape recognition requires the perception of object surface angles. We investigate how neural representations of object angles are constructed from sensory input and how they reorganize across learning. Head-fixed mice learned to discriminate object angles by active exploration with one whisker. Calcium imaging of layers 2-4 of the barrel cortex revealed maps of object-angle tuning before and after learning. Three-dimensional whisker tracking demonstrated that the sensory input components that best discriminate angles (vertical bending and slide distance) also have the greatest influence on object-angle tuning. Despite the high turnover in active ensemble membership across learning, the population distribution of object-angle tuning preferences remained stable. Angle tuning sharpened, but only in neurons that preferred trained angles. This was correlated with a selective increase in the influence of the most task-relevant sensory component on object-angle tuning. These results show how discrimination training enhances stimulus selectivity in the primary somatosensory cortex while maintaining perceptual stability.

Organic Haptics: Intersection of Materials Chemistry and Tactile Perception

The goal of the field of haptics is to create technologies that manipulate the sense of touch. In virtual and augmented reality, haptic devices are for touch what loudspeakers and RGB displays are for hearing and vision. Haptic systems that utilize micromotors or other miniaturized mechanical devices (e.g., for vibration and pneumatic actuation) produce interesting effects, but are quite far from reproducing the feeling of real materials. They are especially deficient in recapitulating surface properties: fine texture, friction, viscoelasticity, tack, and softness. The central argument of this Progress Report is that to reproduce the feel of everyday objects requires chemistry: molecular control over the properties of materials and ultimately design of materials which can change these properties in real time. Stimuli-responsive organic materials, such as polymers and composites, are a class of materials which can change their oxidation state, conductivity, shape, and rheological properties, and thus might be useful in future haptic technologies. Moreover, the use of such materials in research on tactile perception could help elucidate the limits of human tactile sensitivity. The work described represents the beginnings of this new area of inquiry, in which the defining approach is the marriage of materials science and psychology.

Voluntary modification of rapid tactile-motor responses during reaching differs from its visuomotor counterpart

People commonly hold and manipulate a variety of objects in everyday life, and these objects have different physical properties. To successfully control this wide range of objects, people must associate new patterns of tactile stimuli with appropriate motor outputs. We performed a series of experiments investigating the extent to which people can voluntarily modify tactile-motor associations in the context of a rapid tactile-motor response guiding the hand to a moving target (previously described in Pruszynski JA, Johansson RS, Flanagan JR. Curr Biol 26: 788-792, 2016) by using an anti-reach paradigm in which participants were instructed to move their hands in the opposite direction of a target jump. We compared performance to that observed when people make visually guided reaches to a moving target (cf. Day BL, Lyon IN. Exp Brain Res 130: 159-168, 2000; Pisella L, Grea H, Tilikete C, Vighetto A, Desmurget M, Rode G, Boisson D, Rossetti Y. Nat Neurosci 3: 729-736, 2000). When participants had visual feedback, motor responses during the anti-reach task showed early automatic responses toward the moving target before later modification to move in the instructed direction. When the same participants had only tactile feedback, however, they were able to suppress this early phase of the motor response, which occurs <100 ms after the target jump. Our results indicate that while the tactile motor and visual motor systems both support rapid responses that appear similar under some conditions, the circuits underlying responses show sharp distinctions in terms of their malleability.NEW & NOTEWORTHY When people reach toward a visual target that moves suddenly, they automatically correct their reach to follow the object; even when explicitly instructed not to follow a moving visual target, people exhibit an initial incorrect movement before moving in the correct direction. We show that when people use tactile feedback, they do not show an initial incorrect response, even though early muscle activity still occurs.

Tapping Into the Language of Touch: Using Non-invasive Stimulation to Specify Tactile Afferent Firing Patterns

The temporal pattern of action potentials can convey rich information in a variety of sensory systems. We describe a new non-invasive technique that enables precise, reliable generation of action potential patterns in tactile peripheral afferent neurons by brief taps on the skin. Using this technique, we demonstrate sophisticated coding of temporal information in the somatosensory system, that shows that perceived vibration frequency is not encoded in peripheral afferents as was expected by either their firing rate or the underlying periodicity of the stimulus. Instead, a burst gap or silent gap between trains of action potentials conveys frequency information. This opens the possibility of new encoding strategies that could be deployed to convey sensory information using mechanical or electrical stimulation in neural prostheses and brain-machine interfaces, and may extend to senses beyond artificial encoding of aspects of touch. We argue that a focus on appropriate use of effective temporal coding offers more prospects for rapid improvement in the function of these interfaces than attempts to scale-up existing devices.

Cuneate spiking neural network learning to classify naturalistic texture stimuli under varying sensing conditions

We implemented a functional neuronal network that was able to learn and discriminate haptic features from biomimetic tactile sensor inputs using a two-layer spiking neuron model and homeostatic synaptic learning mechanism. The first order neuron model was used to emulate biological tactile afferents and the second order neuron model was used to emulate biological cuneate neurons. We have evaluated 10 naturalistic textures using a passive touch protocol, under varying sensing conditions. Tactile sensor data acquired with five textures under five sensing conditions were used for a synaptic learning process, to tune the synaptic weights between tactile afferents and cuneate neurons. Using post-learning synaptic weights, we evaluated the individual and population cuneate neuron responses by decoding across 10 stimuli, under varying sensing conditions. This resulted in a high decoding performance. We further validated the decoding performance across stimuli, irrespective of sensing velocities using a set of 25 cuneate neuron responses. This resulted in a median decoding performance of 96% across the set of cuneate neurons. Being able to learn and perform generalized discrimination across tactile stimuli, makes this functional spiking tactile system effective and suitable for further robotic applications.