Influence of object shape on responses of human tactile afferents under conditions characteristic of manipulation

Soft Sensors and Actuators for Wearable Human-Machine Interfaces

Haptic human-machine interfaces (HHMIs) combine tactile sensation and haptic feedback to allow humans to interact closely with machines and robots, providing immersive experiences and convenient lifestyles. Significant progress has been made in developing wearable sensors that accurately detect physical and electrophysiological stimuli with improved softness, functionality, reliability, and selectivity. In addition, soft actuating systems have been developed to provide high-quality haptic feedback by precisely controlling force, displacement, frequency, and spatial resolution. In this Review, we discuss the latest technological advances of soft sensors and actuators for the demonstration of wearable HHMIs. We particularly focus on highlighting material and structural approaches that enable desired sensing and feedback properties necessary for effective wearable HHMIs. Furthermore, promising practical applications of current HHMI technology in various areas such as the metaverse, robotics, and user-interactive devices are discussed in detail. Finally, this Review further concludes by discussing the outlook for next-generation HHMI technology.

Artificial Neuron Devices

Efforts to design devices emulating complex cognitive abilities and response processes of biological systems have long been a coveted goal. Recent advancements in flexible electronics, mirroring human tissue's mechanical properties, hold significant promise. Artificial neuron devices, hinging on flexible artificial synapses, bioinspired sensors, and actuators, are meticulously engineered to mimic the biological systems. However, this field is in its infancy, requiring substantial groundwork to achieve autonomous systems with intelligent feedback, adaptability, and tangible problem-solving capabilities. This review provides a comprehensive overview of recent advancements in artificial neuron devices. It starts with fundamental principles of artificial synaptic devices and explores artificial sensory systems, integrating artificial synapses and bioinspired sensors to replicate all five human senses. A systematic presentation of artificial nervous systems follows, designed to emulate fundamental human nervous system functions. The review also discusses potential applications and outlines existing challenges, offering insights into future prospects. We aim for this review to illuminate the burgeoning field of artificial neuron devices, inspiring further innovation in this captivating area of research.

Distinct sensorimotor mechanisms underlie the control of grasp and manipulation forces for dexterous manipulation

Dexterous manipulation relies on the ability to simultaneously attain two goals: controlling object position and orientation (pose) and preventing object slip. Although object manipulation has been extensively studied, most previous work has focused only on the control of digit forces for slip prevention. Therefore, it remains underexplored how humans coordinate digit forces to prevent object slip and control object pose simultaneously. We developed a dexterous manipulation task requiring subjects to grasp and lift a sensorized object using different grasp configurations while preventing it from tilting. We decomposed digit forces into manipulation and grasp forces for pose control and slip prevention, respectively. By separating biomechanically-obligatory from non-obligatory effects of grasp configuration, we found that subjects prioritized grasp stability over efficiency in grasp force control. Furthermore, grasp force was controlled in an anticipatory fashion at object lift onset, whereas manipulation force was modulated following acquisition of somatosensory and visual feedback of object's dynamics throughout object lift. Mathematical modeling of feasible manipulation forces further confirmed that subjects could not accurately anticipate the required manipulation force prior to acquisition of sensory feedback. Our experimental approach and findings open new research avenues for investigating neural mechanisms underlying dexterous manipulation and biomedical applications.

How Tactile Afferents in the Human Fingerpad Encode Tangential Torques Associated with Manipulation: Are Monkeys Better than Us?

Dexterous object manipulation depends critically on information about forces normal and tangential to the fingerpads, and also on torque associated with object orientation at grip surfaces. We investigated how torque information is encoded by human tactile afferents in the fingerpads and compared them to 97 afferents recorded in monkeys (n = 3; 2 females) in our previous study. Human data included slowly-adapting Type-II (SA-II) afferents, which are absent in the glabrous skin of monkeys. Torques of different magnitudes (3.5-7.5 mNm) were applied in clockwise and anticlockwise directions to a standard central site on the fingerpads of 34 human subjects (19 females). Torques were superimposed on a 2, 3, or 4 N background normal force. Unitary recordings were made from fast-adapting Type-I (FA-I, n = 39), and slowly-adapting Type-I (SA-I, n = 31) and Type-II (SA-II, n = 13) afferents supplying the fingerpads via microelectrodes inserted into the median nerve. All three afferent types encoded torque magnitude and direction, with torque sensitivity being higher with smaller normal forces. SA-I afferent responses to static torque were inferior to dynamic stimuli in humans, while in monkeys the opposite was true. In humans this might be compensated by the addition of sustained SA-II afferent input, and their capacity to increase or decrease firing rates with direction of rotation. We conclude that the discrimination capacity of individual afferents of each type was inferior in humans than monkeys which could be because of differences in fingertip tissue compliance and skin friction.SIGNIFICANCE STATEMENT We investigated how individual human tactile nerve fibers encode rotational forces (torques) and compared them to their monkey counterparts. Human hands, but not monkey hands, are innervated by a tactile neuron type (SA-II afferents) specialized to encode directional skin strain yet, so far, torque encoding has only been studied in monkeys. We find that human SA-I afferents were generally less sensitive and less able to discriminate torque magnitude and direction than their monkey counterparts, especially during the static phase of torque loading. However, this shortfall in humans could be compensated by SA-II afferent input. This indicates that variation in afferent types might complement each other signaling different stimulus features possibly providing computational advantage to discriminate stimuli.

Effects of Sensing Tactile Arrays, Shear Force, and Proprioception of Robot on Texture Recognition

In robotics, tactile perception is important for fine control using robot grippers and hands. To effectively incorporate tactile perception in robots, it is essential to understand how humans use mechanoreceptors and proprioceptors to perceive texture. Thus, our study aimed to investigate the impact of tactile sensor arrays, shear force, and the positional information of the robot's end effector on its ability to recognize texture. A deep learning network was employed to classify tactile data from 24 different textures that were explored by a robot. The input values of the deep learning network were modified based on variations in the number of channels of the tactile signal, the arrangement of the tactile sensor, the presence or absence of shear force, and the positional information of the robot. By comparing the accuracy of texture recognition, our analysis revealed that tactile sensor arrays more accurately recognized the texture compared to a single tactile sensor. The utilization of shear force and positional information of the robot resulted in an improved accuracy of texture recognition when using a single tactile sensor. Furthermore, an equal number of sensors placed in a vertical arrangement led to a more accurate distinction of textures during exploration when compared to sensors placed in a horizontal arrangement. The results of this study indicate that the implementation of a tactile sensor array should be prioritized over a single sensor for enhanced accuracy in tactile sensing, and the use of integrated data should be considered for single tactile sensing.

Contact evolution of dry and hydrated fingertips at initial touch

Pressing the fingertips into surfaces causes skin deformations that enable humans to grip objects and sense their physical properties. This process involves intricate finger geometry, non-uniform tissue properties, and moisture, complicating the underlying contact mechanics. Here we explore the initial contact evolution of dry and hydrated fingers to isolate the roles of governing physical factors. Two participants gradually pressed an index finger on a glass surface under three moisture conditions: dry, water-hydrated, and glycerin-hydrated. Gross and real contact area were optically measured over time, revealing that glycerin hydration produced strikingly higher real contact area, while gross contact area was similar for all conditions. To elucidate the causes for this phenomenon, we investigated the combined effects of tissue elasticity, skin-surface friction, and fingerprint ridges on contact area using simulation. Our analyses show the dominant influence of elastic modulus over friction and an unusual contact phenomenon, which we call friction-induced hinging.

Size Constancy Mechanisms: Empirical Evidence from Touch

Several studies have shown the presence of large anisotropies for tactile distance perception across several parts of the body. The tactile distance between two touches on the dorsum of the hand is perceived as larger when they are oriented mediolaterally (across the hand) than proximodistally (along the hand). This effect can be partially explained by the characteristics of primary somatosensory cortex representations. However, this phenomenon is significantly attenuated relative to differences in acuity and cortical magnification, suggesting a process of tactile size constancy. It is unknown whether the same kind of compensation also takes place when estimating the size of a continuous object. Here, we investigate whether the tactile anisotropy that typically emerges when participants have to estimate the distance between two touches is also present when a continuous object touches the skin and participants have to estimate its size. In separate blocks, participants judged which of two tactile distances or objects on the dorsum of their hand felt larger. One stimulation (first or second) was aligned with the proximodistal axis (along the hand) and the other with the mediolateral axis (across the hand). Results showed a clear anisotropy for distances between two distinct points, with across distances consistently perceived as larger than along distances, as in previous studies. Critically, however, this bias was significantly reduced or absent for judgments of the length of continuous objects. These results suggest that a tactile size constancy process is more effective when the tactile size of an object has to be approximated compared to when the distance between two touches has to be determined. The possible mechanism subserving these results is described and discussed. We suggest that a lateral inhibition mechanism, when an object touches the skin, provides information through the distribution of the inhibitory subfields of the RF about the shape of the tactile RF itself. Such a process allows an effective tactile size compensatory mechanism where a good match between the physical and perceptual dimensions of the object is achieved.

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.

Artificial SA-I and RA-I afferents for tactile sensing of ridges and gratings

For robot touch to reach the capabilities of human touch, artificial tactile sensors may require transduction principles like those of natural tactile afferents. Here we propose that a biomimetic tactile sensor (the TacTip) could provide suitable artificial analogues of the tactile skin dynamics, afferent responses and population encoding. Our three-dimensionally printed sensor skin is based on the physiology of the dermal-epidermal interface with an underlying mesh of biomimetic intermediate ridges and dermal papillae, comprising inner pins tipped with markers. Slowly adapting SA-I activity is modelled by marker displacements and rapidly adapting RA-I activity by marker speeds. We test the biological plausibility of these artificial population codes with three classic experiments used for natural touch: (1a) responses to normal pressure to test adaptation of single afferents and spatial modulation across the population; (1b) responses to bars, edges and gratings to compare with measurements from monkey primary afferents; and (2) discrimination of grating orientation to compare with human perceptual performance. Our results show a match between artificial and natural touch at single afferent, population and perceptual levels. As expected, natural skin is more sensitive, which raises a challenge to fabricate a biomimetic fingertip that demonstrates human sensitivity using the transduction principles of human touch.

Mechanical Impedance of Rat Glabrous Skin and Its Relation With Skin Morphometry

The mechanical impedance of intact and epidermis-peeled rat glabrous skin was studied at two sites (digit and sole) and at two frequencies (40 Hz and 250 Hz). The thicknesses of skin layers at the corresponding regions were measured histologically from intact- and peeled-skin samples in every subject. Compared to intact sole skin, digital rat skin has thicker layers and higher mechanical resistance, and it is less stiff. The resistance of the skin significantly decreased after epidermal peeling at both the digit and the sole. Furthermore, peeling caused the reactance to become positive due to inertial effects. As the frequency was increased from 40 to 250 Hz, the resistance and stiffness also increased for the intact skin, while the peeled skin showed less frictional (i.e., resistance) but more inertial (i.e., positive reactance) effects. We estimated the mechanical properties of epidermis and dermis with lumped-element models developed for both intact and peeled conditions. The models predicted that dermis has higher mass, lower stiffness, and lower resistance compared to epidermis, similar to the experimental impedance results obtained in the peeled condition which consisted mostly of dermis. The overall impedance was simulated more successfully at 40 Hz. When both frequencies are considered, the models produced consistent results for resistance in both conditions. The results imply that most of the model parameters should be frequency-dependent and suggest that mechanical properties of epidermis can be related to its thickness. These findings may help in designing artificial skin for neuroprosthetic limbs.

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.

The neural mechanisms of manual dexterity

The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.

Measuring fingerpad deformation during active object manipulation

During active object manipulation, the finger-object interactions give rise to complex fingertip skin deformations. These deformations are in turn encoded by the local tactile afferents and provide rich and behaviorally relevant information to the central nervous system. Most of the work studying the mechanical response of the finger to dynamic loading has been performed under a passive setup, thereby precisely controlling the kinematics or the dynamics of the loading. However, to identify aspects of the deformations that are relevant to online control during object manipulation, it is desirable to measure the skin response in an active setup. To that end, we developed a device that allows us to monitor finger forces, skin deformations, and kinematics during fine manipulation. We describe the device in detail and test it to precisely describe how the fingertip skin in contact with the object deforms during a simple vertical oscillation task. We show that the level of grip force directly influences the fingerpad skin strains and that the strain rates are substantial during active manipulation (norm up to 100%/s). The developed setup will enable us to causally relate sensory information, i.e. skin deformation, to online control, i.e. grip force adjustment, in future studies.NEW & NOTEWORTHY We present a novel device, a manipulandum, that enables to image the contact between the finger and the contact surface during active manipulation of the device. The device is tested in a simple vertical oscillation task involving 18 participants. We demonstrate that substantial surface skin strains take place at the finger-object interface and argue that those deformations provide essential information for grasp stability during object manipulation.

Multi-structure Cortical States Deduced From Intracellular Representations of Fixed Tactile Input Patterns

The brain has a never-ending internal activity, whose spatiotemporal evolution interacts with external inputs to constrain their impact on brain activity and thereby how we perceive them. We used reproducible touch-related spatiotemporal sensory inputs and recorded intracellularly from rat (Sprague-Dawley, male) neocortical neurons to characterize this interaction. The synaptic responses, or the summed input of the networks connected to the neuron, varied greatly to repeated presentations of the same tactile input pattern delivered to the tip of digit 2. Surprisingly, however, these responses tended to sort into a set of specific time-evolving response types, unique for each neuron. Further, using a set of eight such tactile input patterns, we found each neuron to exhibit a set of specific response types for each input provided. Response types were not determined by the global cortical state, but instead likely depended on the time-varying state of the specific subnetworks connected to each neuron. The fact that some types of responses recurred indicates that the cortical network had a non-continuous landscape of solutions for these tactile inputs. Therefore, our data suggest that sensory inputs combine with the internal dynamics of the brain networks, thereby causing them to fall into one of the multiple possible perceptual attractor states. The neuron-specific instantiations of response types we observed suggest that the subnetworks connected to each neuron represent different components of those attractor states. Our results indicate that the impact of cortical internal states on external inputs is substantially more richly resolvable than previously shown.

Skin-Inspired Piezoelectric Tactile Sensor Array with Crosstalk-Free Row+Column Electrodes for Spatiotemporally Distinguishing Diverse Stimuli

Real-time detection and differentiation of diverse external stimuli with one tactile senor remains a huge challenge and largely restricts the development of electronic skins. Although different sensors have been described based on piezoresistivity, capacitance, and triboelectricity, and these devices are promising for tactile systems, there are few, if any, piezoelectric sensors to be able to distinguish diverse stimuli in real time. Here, a human skin-inspired piezoelectric tactile sensor array constructed with a multilayer structure and row+column electrodes is reported. Integrated with a signal processor and a logical algorithm, the tactile sensor array achieves to sense and distinguish the magnitude, positions, and modes of diverse external stimuli, including gentle slipping, touching, and bending, in real time. Besides, the unique design overcomes the crosstalk issues existing in other sensors. Pressure sensing and bending sensing tests show that the proposed tactile sensor array possesses the characteristics of high sensitivity (7.7 mV kPa-1), long-term durability (80 000 cycles), and rapid response time (10 ms) (less than human skin). The tactile sensor array also shows a superior scalability and ease of massive fabrication. Its ability of real-time detection and differentiation of diverse stimuli for health monitoring, detection of animal movements, and robots is demonstrated.

Stability of perception of the hand's aperture in a grasp

KEY POINTS

How we judge the location of our body parts can be affected by a range of factors that change how our brain interprets proprioceptive signals. We examined the effect of several such factors on how we perceive an object's width and the spacing between our thumb and fingers when grasping. Grasp-related perceptions were slightly wider when using all digits, in line with our tendency to grasp larger objects with the entire hand. Surprisingly, these perceptions were not affected by the frames of reference for judgements (object width versus grasp aperture), whether the object was grasped actively or passively, or the strength of the grasp. These results show that the brain maintains a largely stable representation of the hand when grasping stationary objects. This stability may underpin our dexterity when grasping a vast array of objects.

ABSTRACT

Various factors can alter how the brain interprets proprioceptive signals, leading to errors in how we perceive our body and execute motor tasks. This study determined the effect of critical factors on hand-based perceptions. In Experiment 1, 20 participants grasped without lifting an unseen 6.5 cm-wide object with two grasp configurations: thumb and all fingers, and thumb and index finger. Participants reported perceived grasp aperture (body reference frame) or perceived object width (external reference frame) using visual charts. In Experiment 2, 20 participants grasped the object with three grasp intensities (1, 5 and 15% maximal grasp force) actively or passively and reported perceived grasp aperture. A follow-up experiment addressed whether results from Experiment 2 were influenced by the external force applied during passive grasp. Overall, there was a mean difference of 0.38 cm (95% confidence interval (CI), 0.12 to 0.63) between the two grasp configurations (all digits compared to thumb and index finger). Perceived object width compared to perceived grasp aperture differed by only -0.04 cm (95% CI, -0.30 to 0.21). There was no real effect of grasp intensity on perceived grasp aperture (-0.01 cm; 95% CI, -0.03 to 0.01) or grasp type (active versus passive; 0.18 cm; 95% CI, -0.19 to 0.55). Overall, grasp-related perceptions are slightly wider when using all digits, in line with our tendency to grasp larger objects with the entire hand. The other factors - frame of reference, grasp intensity and grasp type - had no meaningful effect on these perceptions. These results provide evidence that the brain maintains a largely stable representation of the hand.

Focal neocortical lesions impair distant neuronal information processing

KEY POINTS

Parts of the fields of neuroscience and neurology consider the neocortex to be a functionally parcelled structure. Viewed through such a conceptual filter, there are multiple clinical observations after localized stroke lesions that seem paradoxical. We tested the effect that localized stroke-like lesions have on neuronal information processing in a part of the neocortex that is distant to the lesion using animal experiments. We find that the distant lesion degrades the quality of neuronal information processing of tactile input patterns in primary somatosensory cortex. The findings suggest that even the processing of primary sensory information depends on an intact neocortical network, with the implication that all neocortical processing may rely on widespread interactions across large parts of the cortex.

ABSTRACT

Recent clinical studies report a surprisingly weak relationship between the location of cortical brain lesions and the resulting functional deficits. From a neuroscience point of view, such findings raise questions as to what extent functional localization applies in the neocortex and to what extent the functions of different regions depend on the integrity of others. Here we provide an in-depth analysis of the changes in the function of the neocortical neuronal networks after distant focal stroke-like lesions in the anaesthetized rat. Using a recently introduced high resolution analysis of neuronal information processing, consisting of pre-set spatiotemporal patterns of tactile afferent activation against which the neuronal decoding performance can be quantified, we found that stroke-like lesions in distant parts of the cortex significantly degraded the decoding performance of individual neocortical neurons in the primary somatosensory cortex (decoding performance decreased from 30.9% to 24.2% for n = 22 neurons, Wilcoxon signed rank test, P = 0.028). This degrading effect was not due to changes in the firing frequency of the neuron (Wilcoxon signed rank test, P = 0.499) and was stronger the higher the decoding performance of the neuron, indicating a specific impact on the information processing capacity in the cortex. These findings suggest that even primary sensory processing depends on widely distributed cortical networks and could explain observations of focal stroke lesions affecting a large range of functions.

Wearable strain gauge-based technology measures manual tactile forces during the activities of daily living

Introduction

Current methods of determining applied forces in the hand rely on grip dynamometers or force-measurement gloves which are limited in their ability to isolate individual finger forces and interfere with the sense of touch. The objective of this study was to develop an improved force measurement system that could be used during various activities of daily living.

Methods

Custom-made strain gauge sensors were secured to the fingernail of four fingers and two middle phalanges and calibrated to measure hand forces in eight healthy individuals during five activities of daily living.

Results

These sensors were capable of measuring forces as small as 0.17 N and did not saturate at high force tasks around 15 N, which is within the envelope of forces experienced during daily life. Preliminary data demonstrate the ability of these tactile sensors to reliably distinguish which fingers/segments were used in various tasks.

Conclusions

Until now, there has been no method for real-time unobtrusive monitoring of force exposure during the tasks of daily life. The system used in this study provides a new type of low-cost wearable technology to monitor forces in the hands without interfering with the contact surface of the hand.

Fins as Mechanosensors for Movement and Touch-Related Behaviors

Mechanosensation is a universal feature of animals that is essential for behavior, allowing detection of animals' own body movement and position as well as physical characteristics of the environment. The extraordinary morphological and behavioral diversity that exists across fish species provide rich opportunities for comparative mechanosensory studies in fins. The fins of fishes have been found to function as proprioceptors, by providing feedback on fin ray position and movement, and as tactile sensors, by encoding pressures applied to the fin surface. Across fish species, and among fins, the afferent response is remarkably consistent, suggesting that the ability of fin rays and membrane to sense deformation is a fundamental feature of fish fins. While fin mechanosensation has been known in select, often highly specialized, species for decades, only in the last decade have we explored mechanosensation in typical propulsive fins and considered its role in behavior, particularly locomotion. In this paper, we synthesize the current understanding of the anatomy and physiology of fin mechanosensation, looking toward key directions for research. We argue that a mechanosensory perspective informs studies of fin-based propulsion and other fin-driven behaviors and should be considered in the interpretation of fin morphology and behavior. In addition, we compare the mechanosensory system innervating the fins of fishes to the systems innervating the limbs of mammals and wings of insects in order to identify shared mechanosensory strategies and how different organisms have evolved to meet similar functional challenges. Finally, we discuss how understanding the biological organization and function of fin sensors can inform the design of control systems for engineered fins and fin-driven robotics.

Ubiquitous Neocortical Decoding of Tactile Input Patterns

Whereas functional localization historically has been a key concept in neuroscience, direct neuronal recordings show that input of a particular modality can be recorded well outside its primary receiving areas in the neocortex. Here, we wanted to explore if such spatially unbounded inputs potentially contain any information about the quality of the input received. We utilized a recently introduced approach to study the neuronal decoding capacity at a high resolution by delivering a set of electrical, highly reproducible spatiotemporal tactile afferent activation patterns to the skin of the contralateral second digit of the forepaw of the anesthetized rat. Surprisingly, we found that neurons in all areas recorded from, across all cortical depths tested, could decode the tactile input patterns, including neurons of the primary visual cortex. Within both somatosensory and visual cortical areas, the combined decoding accuracy of a population of neurons was higher than for the best performing single neuron within the respective area. Such cooperative decoding indicates that not only did individual neurons decode the input, they also did so by generating responses with different temporal profiles compared to other neurons, which suggests that each neuron could have unique contributions to the tactile information processing. These findings suggest that tactile processing in principle could be globally distributed in the neocortex, possibly for comparison with internal expectations and disambiguation processes relying on other modalities.

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.

Linear Integration of Tactile and Non-tactile Inputs Mediates Estimation of Fingertip Relative Position

While skin, joints and muscles receptors alone provide lower level information about individual variables (e.g., exerted limb force and limb displacement), the distance between limb endpoints (i.e., relative position) has to be extracted from high level integration of somatosensory and motor signals. In particular, estimation of fingertip relative position likely involves more complex sensorimotor transformations than those underlying hand or arm position sense: the brain has to estimate where each fingertip is relative to the hand and where fingertips are relative to each other. It has been demonstrated that during grasping, feedback of digit position drives rapid adjustments of fingers force control. However, it has been shown that estimation of fingertips' relative position can be biased by digit forces. These findings raise the question of how the brain combines concurrent tactile (i.e., cutaneous mechanoreceptors afferents induced by skin pressure and stretch) and non-tactile (i.e., both descending motor command and joint/muscle receptors signals associated to muscle contraction) digit force-related inputs for fingertip distance estimation. Here we addressed this question by quantifying the contribution of tactile and non-tactile force-related inputs for the estimation of fingertip relative position. We asked subjects to match fingertip vertical distance relying only on either tactile or non-tactile inputs from the thumb and index fingertip, and compared their performance with the condition where both types of inputs were combined. We found that (a) the bias in the estimation of fingertip distance persisted when tactile inputs and non-tactile force-related signals were presented in isolation; (b) tactile signals contributed the most to the estimation of fingertip distance; (c) linear summation of the matching errors relying only on either tactile or non-tactile inputs was comparable to the matching error when both inputs were simultaneously available. These findings reveal a greater role of tactile signals for sensing fingertip distance and suggest a linear integration mechanism with non-tactile inputs for the estimation of fingertip relative position.

A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics

Tactile sensing is required for the dexterous manipulation of objects in robotic applications. In particular, the ability to measure and distinguish in real time normal and shear forces is crucial for slip detection and interaction with fragile objects. Here, we report a biomimetic soft electronic skin (e-skin) that is composed of an array of capacitors and capable of measuring and discriminating in real time both normal and tangential forces. It is enabled by a three-dimensional structure that mimics the interlocked dermis-epidermis interface in human skin. Moreover, pyramid microstructures arranged along nature-inspired phyllotaxis spirals resulted in an e-skin with increased sensitivity, minimal hysteresis, excellent cycling stability, and response time in the millisecond range. The e-skin provided sensing feedback for controlling a robot arm in various tasks, illustrating its potential application in robotics with tactile feedback.

Time, touch and temperature affect perceived finger position and ownership in the grasp illusion

KEY POINTS

The brain's internal model of the body and the sense of body ownership are fundamental to interaction with the world. It is thought that temporally congruent, repetitive multisensory stimuli are required to elicit a sense of body ownership. Here we investigate the ability of static cutaneous stimuli - passively grasping an artificial finger - to induce body ownership and alter perceived body position; we also investigate how physical characteristics of grasped objects alter these senses. We show that static cutaneous stimuli can alter perceived body position and induce an illusion of ownership and also that signals of temperature, texture and shape of grasped finger-sized objects influence body ownership. Thus, these aspects of human proprioception can be altered by a single sustained sensory stimulus and by the physical characteristics of held objects.

ABSTRACT

Perceived body position and ownership are fundamental to our ability to sense and interact with the world. Previous work indicates that temporally congruent, repetitive multisensory stimuli are needed to alter the sense of body ownership. In the present study 30 subjects passively grasped an artificial rubber finger with their left index and thumb while their right index finger, located 12 cm below, was lightly clamped. Fingers with varied physical characteristics were also passively grasped to determine how these characteristics influenced perceived body position and ownership. Subjects immediately felt their hands to be 5.3 cm [3.4-7.3] (mean [95%CI]) closer, a feeling that remained after 3 min (6.0 cm [4.5-7.5]). By the end of the trial, perceived ownership increased by 1.2 [0.6-1.9] points on a 7-point Likert scale, with the group average moving from 'neither agree or disagree' at the start to 'somewhat agree' at the end. Compared to grasping a control rubber finger, grasping a cold, rough, oddly shaped or rectangular shaped finger-like object reduced perceived ownership. These results provide new insights into the role of cutaneous sensory receptors in defining these aspects of proprioception, and the speed with which these effects occur. Static touch rapidly induces large, sustained changes in perceived body position and prolonged exposure to these cutaneous inputs, alone, can induce a sense of body ownership. Also, certain physical characteristics of grasped objects influence the sense of body ownership.

Artificial spatiotemporal touch inputs reveal complementary decoding in neocortical neurons

Investigations of the mechanisms of touch perception and decoding has been hampered by difficulties in achieving invariant patterns of skin sensor activation. To obtain reproducible spatiotemporal patterns of activation of sensory afferents, we used an artificial fingertip equipped with an array of neuromorphic sensors. The artificial fingertip was used to transduce real-world haptic stimuli into spatiotemporal patterns of spikes. These spike patterns were delivered to the skin afferents of the second digit of rats via an array of stimulation electrodes. Combined with low-noise intra- and extracellular recordings from neocortical neurons in vivo, this approach provided a previously inaccessible high resolution analysis of the representation of tactile information in the neocortical neuronal circuitry. The results indicate high information content in individual neurons and reveal multiple novel neuronal tactile coding features such as heterogeneous and complementary spatiotemporal input selectivity also between neighboring neurons. Such neuronal heterogeneity and complementariness can potentially support a very high decoding capacity in a limited population of neurons. Our results also indicate a potential neuroprosthetic approach to communicate with the brain at a very high resolution and provide a potential novel solution for evaluating the degree or state of neurological disease in animal models.

Importance of spike timing in touch: an analogy with hearing?

Touch is often conceived as a spatial sense akin to vision. However, touch also involves the transduction and processing of signals that vary rapidly over time, inviting comparisons with hearing. In both sensory systems, first order afferents produce spiking responses that are temporally precise and the timing of their responses carries stimulus information. The precision and informativeness of spike timing in the two systems invites the possibility that both implement similar mechanisms to extract behaviorally relevant information from these precisely timed responses. Here, we explore the putative roles of spike timing in touch and hearing and discuss common mechanisms that may be involved in processing temporal spiking patterns.

Robo-Psychophysics: Extracting Behaviorally Relevant Features from the Output of Sensors on a Prosthetic Finger

Efforts are underway to restore sensorimotor function in amputees and tetraplegic patients using anthropomorphic robotic hands. For this approach to be clinically viable, sensory signals from the hand must be relayed back to the patient. To convey tactile feedback necessary for object manipulation, behaviorally relevant information must be extracted in real time from the output of sensors on the prosthesis. In the present study, we recorded the sensor output from a state-of-the-art bionic finger during the presentation of different tactile stimuli, including punctate indentations and scanned textures. Furthermore, the parameters of stimulus delivery (location, speed, direction, indentation depth, and surface texture) were systematically varied. We developed simple decoders to extract behaviorally relevant variables from the sensor output and assessed the degree to which these algorithms could reliably extract these different types of sensory information across different conditions of stimulus delivery. We then compared the performance of the decoders to that of humans in analogous psychophysical experiments. We show that straightforward decoders can extract behaviorally relevant features accurately from the sensor output and most of them outperform humans.

Pursuing prosthetic electronic skin

Skin plays an important role in mediating our interactions with the world. Recreating the properties of skin using electronic devices could have profound implications for prosthetics and medicine. The pursuit of artificial skin has inspired innovations in materials to imitate skin's unique characteristics, including mechanical durability and stretchability, biodegradability, and the ability to measure a diversity of complex sensations over large areas. New materials and fabrication strategies are being developed to make mechanically compliant and multifunctional skin-like electronics, and improve brain/machine interfaces that enable transmission of the skin's signals into the body. This Review will cover materials and devices designed for mimicking the skin's ability to sense and generate biomimetic signals.

The Bayesian Decoding of Force Stimuli from Slowly Adapting Type I Fibers in Humans

It is well known that signals encoded by mechanoreceptors facilitate precise object manipulation in humans. It is therefore of interest to study signals encoded by the mechanoreceptors because this will contribute further towards the understanding of fundamental sensory mechanisms that are responsible for coordinating force components during object manipulation. From a practical point of view, this may suggest strategies for designing sensory-controlled biomedical devices and robotic manipulators. We use a two-stage nonlinear decoding paradigm to reconstruct the force stimulus given signals from slowly adapting type one (SA-I) tactile afferents. First, we describe a nonhomogeneous Poisson encoding model which is a function of the force stimulus and the force’s rate of change. In the decoding phase, we use a recursive nonlinear Bayesian filter to reconstruct the force profile, given the SA-I spike patterns and parameters described by the encoding model. Under the current encoding model, the mode ratio of force to its derivative is: 1.26 to 1.02. This indicates that the force derivative contributes significantly to the rate of change to the SA-I afferent spike modulation. Furthermore, using recursive Bayesian decoding algorithms is advantageous because it can incorporate past and current information in order to make predictions—consistent with neural systems—with little computational resources. This makes it suitable for interfacing with prostheses.

Feeling form: the neural basis of haptic shape perception

The tactile perception of the shape of objects critically guides our ability to interact with them. In this review, we describe how shape information is processed as it ascends the somatosensory neuraxis of primates. At the somatosensory periphery, spatial form is represented in the spatial patterns of activation evoked across populations of mechanoreceptive afferents. In the cerebral cortex, neurons respond selectively to particular spatial features, like orientation and curvature. While feature selectivity of neurons in the earlier processing stages can be understood in terms of linear receptive field models, higher order somatosensory neurons exhibit nonlinear response properties that result in tuning for more complex geometrical features. In fact, tactile shape processing bears remarkable analogies to its visual counterpart and the two may rely on shared neural circuitry. Furthermore, one of the unique aspects of primate somatosensation is that it contains a deformable sensory sheet. Because the relative positions of cutaneous mechanoreceptors depend on the conformation of the hand, the haptic perception of three-dimensional objects requires the integration of cutaneous and proprioceptive signals, an integration that is observed throughout somatosensory cortex.

Decoding tactile afferent activity to obtain an estimate of instantaneous force and torque applied to the fingerpad

Dexterous manipulation is not possible without sensory information about object properties and manipulative forces. Fundamental neuroscience has been unable to demonstrate how information about multiple stimulus parameters may be continuously extracted, concurrently, from a population of tactile afferents. This is the first study to demonstrate this, using spike trains recorded from tactile afferents innervating the monkey fingerpad. A multiple-regression model, requiring no a priori knowledge of stimulus-onset times or stimulus combination, was developed to obtain continuous estimates of instantaneous force and torque. The stimuli consisted of a normal-force ramp (to a plateau of 1.8, 2.2, or 2.5 N), on top of which -3.5, -2.0, 0, +2.0, or +3.5 mNm torque was applied about the normal to the skin surface. The model inputs were sliding windows of binned spike counts recorded from each afferent. Models were trained and tested by 15-fold cross-validation to estimate instantaneous normal force and torque over the entire stimulation period. With the use of the spike trains from 58 slow-adapting type I and 25 fast-adapting type I afferents, the instantaneous normal force and torque could be estimated with small error. This study demonstrated that instantaneous force and torque parameters could be reliably extracted from a small number of tactile afferent responses in a real-time fashion with stimulus combinations that the model had not been exposed to during training. Analysis of the model weights may reveal how interactions between stimulus parameters could be disentangled for complex population responses and could be used to test neurophysiologically relevant hypotheses about encoding mechanisms.

Segregation of tactile input features in neurons of the cuneate nucleus

Our tactile perception of external objects depends on skin-object interactions. The mechanics of contact dictates the existence of fundamental spatiotemporal input features—contact initiation and cessation, slip, and rolling contact—that originate from the fact that solid objects do not interpenetrate. However, it is unknown whether these features are represented within the brain. We used a novel haptic interface to deliver such inputs to the glabrous skin of finger/digit pads and recorded from neurons of the cuneate nucleus (the brain’s first level of tactile processing) in the cat. Surprisingly, despite having similar receptive fields and response properties, each cuneate neuron responded to a unique combination of these inputs. Hence, distinct haptic input features are encoded already at subcortical processing stages. This organization maps skin-object interactions into rich representations provided to higher cortical levels and may call for a re-evaluation of our current understanding of the brain’s somatosensory systems.

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Specific haptic input features were selectively delivered to glabrous skin

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The input features were segregated in the neurons of the cuneate nucleus

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These observations may call for a shift in current views of tactile processing

Digit forces bias sensorimotor transformations underlying control of fingertip position

Humans are able to modulate digit forces as a function of position despite changes in digit placement that might occur from trial to trial or when changing grip type for object manipulation. Although this phenomenon is likely to rely on sensing the position of the digits relative to each other and the object, the underlying mechanisms remain unclear. To address this question, we asked subjects (n = 30) to match perceived vertical distance between the center of pressure (CoP) of the thumb and index finger pads (d_y) of the right hand (“reference” hand) using the same hand (“test” hand). The digits of reference hand were passively placed collinearly (d_y = 0 mm). Subjects were then asked to exert different combinations of normal and tangential digit forces (F_n and F_tan, respectively) using the reference hand and then match the memorized d_y using the test hand. The reference hand exerted F_tan of thumb and index finger in either same or opposite direction. We hypothesized that, when the tangential forces of the digits are produced in opposite directions, matching error (1) would be biased toward the directions of the tangential forces; and (2) would be greater when the remembered relative contact points are matched with negligible digit force production. For the test hand, digit forces were either negligible (0.5–1 N, 0 ± 0.25 N; Experiment 1) or the same as those exerted by the reference hand (Experiment 2).Matching error was biased towards the direction of digit tangential forces: thumb CoP was placed higher than the index finger CoP when thumb and index finger F_tan were directed upward and downward, respectively, and vice versa (p < 0.001). However, matching error was not dependent on whether the reference and test hand exerted similar or different forces. We propose that the expected sensory consequence of motor commands for tangential forces in opposite directions overrides estimation of fingertip position through haptic sensory feedback.

Validating a population model of tactile mechanotransduction of slowly adapting type I afferents at levels of skin mechanics, single-unit response and psychophysics

Previous models of touch have linked skin mechanics to neural firing rate, neural dynamics to action potential elicitation, and mechanoreceptor populations to psychophysical discrimination. However, no one model spans all levels. The objective of work herein is to build a multi-level, computational model of tactile neurons embedded in cutaneous skin, and then validate its predictions of skin surface deflection, single-afferent firing to indenter shift, and population response for sphere discrimination. The model includes a 3D finite element representation of the distal phalange with hyper- and visco-elastic mechanics. Distributed over its surface, a population of receptor models is comprised of bi-phasic functions to represent Merkel cells' transformation of stress/strain to membrane current and a leaky integrate-and-fire neuronal models to generate the timing of action potentials. After including neuronal noise, the predictions of two population encoding strategies (gradient sum and euclidean distance) are compared to psychophysical discrimination of spheres. Results indicate that predicted skin surface deflection matches Srinivasan's observations for 50 micron and 3.17 mm diameter cylinders and single-afferent responses achieve R(2) = 0.81 when compared to Johnson's recordings. Discrimination results correlate with Goodwin's experiments, whereby 287 and 365 m(-1) spheres are more discriminable than 287 and 296 m(-1).

Differential sensitivity to surface compliance by tactile afferents in the human finger pad

We undertook a neurophysiological investigation of the responses of low-threshold mechanoreceptors in the human finger pad to surfaces of differing softness. Unitary recordings were made from 26 slowly adapting type I (SAI), 17 fast-adapting type I (FAI), and 9 slowly adapting type II (SAII) afferents via tungsten microelectrodes inserted into the median nerve at the wrist. A servo-controlled stimulator applied ramp-and-hold forces (1, 2, 4 N) at a constant loading and unloading rate (2 N/s) via a flat silicone disc over the center of the finger pad. Nine discs were used, which linearly increased in stiffness across the range. Population responses of the SAI afferents showed the greatest sensitivity to compliance, with a steep monotonic increase in mean firing rate with increasing stiffness (decreasing compliance) of the surface during the loading and plateau (but not unloading) phases. FAI afferents also showed a linear increase in firing during the loading but not unloading phase, although the slope was significantly lower than that of the SAI afferents at all amplitudes. Conversely, SAII afferents were influenced by object compliance only in certain conditions. Given their high density in the finger pads and their linear relationship between firing rate and object compliance during the loading and plateau phases, SAI afferents (together with FAI afferents during the loading phase) are ideally suited to contributing information on surface compliance to the overall estimation of softness, but the SAII afferents appear to play only a minor role.

Integration of sensory quanta in cuneate nucleus neurons in vivo

Discriminative touch relies on afferent information carried to the central nervous system by action potentials (spikes) in ensembles of primary afferents bundled in peripheral nerves. These sensory quanta are first processed by the cuneate nucleus before the afferent information is transmitted to brain networks serving specific perceptual and sensorimotor functions. Here we report data on the integration of primary afferent synaptic inputs obtained with in vivo whole cell patch clamp recordings from the neurons of this nucleus. We find that the synaptic integration in individual cuneate neurons is dominated by 4-8 primary afferent inputs with large synaptic weights. In a simulation we show that the arrangement with a low number of primary afferent inputs can maximize transfer over the cuneate nucleus of information encoded in the spatiotemporal patterns of spikes generated when a human fingertip contact objects. Hence, the observed distributions of synaptic weights support high fidelity transfer of signals from ensembles of tactile afferents. Various anatomical estimates suggest that a cuneate neuron may receive hundreds of primary afferents rather than 4-8. Therefore, we discuss the possibility that adaptation of synaptic weight distribution, possibly involving silent synapses, may function to maximize information transfer in somatosensory pathways.

Touching curvature and feeling size: a contrast illusion

We know that our eyes can be deceiving. Here we demonstrate that we should not always trust our sense of touch either. Previous studies have shown that when pinching an object between thumb and index finger, we can under many circumstances accurately perceive its size. In contrast, the current results show that the local curvature at the areas of contact between the object and the fingers causes systematic under- or overestimation of the object's size. This is rather surprising given that local curvature is not directly related to the object's size. We suggest an explanation in terms of a contrast between the finger separation and an inferred relationship between local curvature and size. This study provides the first demonstration of an illusory haptic size percept caused by local curvature in a pinch grip.

An involuntary stereotypical grasp tendency pervades voluntary dynamic multifinger manipulation

We used a novel apparatus with three hinged finger pads to characterize collaborative multifinger interactions during dynamic manipulation requiring individuated control of fingertip motions and forces. Subjects placed the thumb, index, and middle fingertips on each hinged finger pad and held it-unsupported-with constant total grasp force while voluntarily oscillating the thumb's pad. This task combines the need to 1) hold the object against gravity while 2) dynamically reconfiguring the grasp. Fingertip force variability in this combined motion and force task exhibited strong synchrony among normal (i.e., grasp) forces. Mechanical analysis and simulation show that such synchronous variability is unnecessary and cannot be explained solely by signal-dependent noise. Surprisingly, such variability also pervaded control tasks requiring different individuated fingertip motions and forces, but not tasks without finger individuation such as static grasp. These results critically extend notions of finger force variability by exposing and quantifying a pervasive challenge to dynamic multifinger manipulation: the need for the neural controller to carefully and continuously overlay individuated finger actions over mechanically unnecessary synchronous interactions. This is compatible with-and may explain-the phenomenology of strong coupling of hand muscles when this delicate balance is not yet developed, as in early childhood, or when disrupted, as in brain injury. We conclude that the control of healthy multifinger dynamic manipulation has barely enough neuromechanical degrees of freedom to meet the multiple demands of ecological tasks and critically depends on the continuous inhibition of synchronous grasp tendencies, which we speculate may be of vestigial evolutionary origin.

Haptic feedback enhances grip force control of sEMG-controlled prosthetic hands in targeted reinnervation amputees

In this study, we hypothesized that haptic feedback would enhance grip force control of surface electromyography (sEMG)-controlled prosthetic hands for targeted reinnervation (TR) amputees. A new miniature haptic device, a tactor, that can deliver touch, pressure, shear, and temperature sensation, allows modality-matching haptic feedback. TR surgery that creates sensory regions on the patient's skin that refer to the surface of the missing limb allows somatotopic-matching haptic feedback. This paper evaluates the hypothesis via an sEMG-controlled virtual prosthetic arm operated by TR amputees under diverse haptic feedback conditions. The results indicate that the grip force control is significantly enhanced via the haptic feedback. However, the simultaneous display of two haptic channels (pressure and shear) does not enhance, but instead degrades, grip force control.

Rodents rely on Merkel cells for texture discrimination tasks

The cutaneous somatosensory system contains multiple types of mechanoreceptors that detect different mechanical stimuli (Johnson, 2001). These stimuli, either alone or in combination, are ultimately interpreted by the brain as different aspects of the sense of touch. Psychophysical and electrophysiological experiments in humans and other mammals implicate one of these mechanoreceptors, the Merkel cell/neurite complex, in two-point discrimination and the detection of curvature, shape, and texture (Johnson and Lamb, 1981; Johnson et al., 2000; Johnson, 2001). However, whether Merkel cell/neurite complex function is required for the detection of these stimuli is unknown. We genetically engineered mice that lack Merkel cells (Maricich et al., 2009; Morrison et al., 2009) to directly test the hypothesis that Merkel cell/neurite complexes are necessary to perform these types of sensory discrimination tasks. We found that mice devoid of Merkel cells could not detect textured surfaces with their feet while other measures of motor and sensory function were unaffected. Interestingly, these mice retained the ability to discriminate both texture and shape using their whiskers, suggesting that other somatosensory afferents can functionally substitute for Merkel cell/neurite complexes in this sensory organ. These findings suggest that Merkel cell/neurite complexes are essential for texture discrimination tasks involving glabrous skin but not whiskers.

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

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

Cutaneous afferents from the monkeys fingers: responses to tangential and normal forces

Control of tangential force plays a key role in everyday manipulations. In anesthetized monkeys, forces tangential to the skin were applied at a range of magnitudes comparable to those used in routine manipulations and in eight different directions. The paradigm used enabled separation of responses to tangential force from responses to the background normal force. For slowly adapting type I (SAI) afferents, tangential force responses ranged from excitatory through no response to suppression, with both a static and dynamic component. For fast adapting type I (FAI) afferents, responses were dynamic and excitatory only. Responses of both afferent types were scaled by tangential force magnitude, elucidating the neural basis for previous human psychophysical scaling data. Most afferents were direction selective with a range of preferred directions and a range in sharpness of tuning. Both the preferred direction and the degree of tuning were independent of the background normal force. Preferred directions were distributed uniformly over 360 degrees for SAI afferents, but for FAI afferents they were biased toward the proximo-ulnar direction. Afferents from all over the glabrous skin of the distal segment of the finger responded; there was no evident relationship between the position of an afferent's receptive field on the finger and its preferred direction or its degree of tuning. Nor were preferred directions biased either toward or away from the receptive field center. In response to the relatively large normal forces, some afferents saturated and others did not, regardless of the positions of their receptive fields. Total afferent response matched human psychophysical scaling functions for normal force.

Slowing of dexterous manipulation in old age: force and kinematic findings from the 'nut-and-rod' task

The task of sliding a nut from a rod has been used to study manual slowing in old age (Smith et al. in Neurology 53:1458-1461, 1999; Neurobiol Aging 26:883-890, 2005). In this experiment, we sought to determine if the age-related slowing in this task occurs with losses of motor precision, as indicated by the forces exerted on the rod. The forces exerted by the nut on the rod were monitored along with the kinematics of the hand in old and young adults while they attempted to lift a nut from three vertically oriented rods of different shape (straight, single curve, double curve). Old adults performed the task 64% slower than young adults for the straight rod, 100% slower for the single-curve rod, and 80% slower for the double-curve rod. Old adults did not differ from the young adults in the amount of force exerted against the rods in the horizontal plane, or in the steadiness of these forces, but exerted greater force impulses in the vertical direction over the course of a trial (359% straight, 236% single curve, 214% double curve) and much more force in the vertical direction (255% straight, 267% single curve, 159% double curve). Old adults also performed the task with 35% greater average roll of the hand into pronation. We suspect that old adults tilted the nut, even for the straight rod, dragging it against the rod to create the elevated vertical forces. These observations support previous speculation that old adults do not control the external moments applied to grasped objects as well as young adults.

Information about complex fingertip parameters in individual human tactile afferent neurons

Although information in tactile afferent neurons represented by firing rates has been studied extensively over nearly a century, recent studies suggest that precise spike timing might be more important than firing rates. Here, we used information theory to compare the information content in the discharges of 92 tactile afferents distributed over the entire terminal segment of the fingertip when it was contacted by surfaces with different curvatures and force directions representative of everyday manipulations. Estimates of the information content with regard to curvature and force direction based on the precise timing of spikes were at least 2.2 times and 1.6 times, respectively, larger than that of spike counts during a 125 ms period of force increase. Moreover, the information regarding force direction based on the timing of the very first elicited spike was comparable with that provided by spike counts and more than twice as large with respect to object shape. For all encoding schemes, afferents terminating close to the stimulation site tended to convey more information about surface curvature than more remote afferents that tended to convey more information about force direction. Finally, coding schemes based on spike timing and spike counts overall contributed mostly independent information. We conclude that information about tactile stimuli in timing of spikes in primary afferents, even if limited to the first spikes, surpasses that contained in firing rates and that these measures of afferents' responses might capture different aspects of the stimulus.

Slowly adapting mechanoreceptors in the borders of the human fingernail encode fingertip forces

There are clusters of slowly adapting (SA) mechanoreceptors in the skin folds bordering the nail. These "SA-IInail" afferents, which constitute nearly one fifth of the tactile afferents innervating the fingertip, possess the general discharge characteristics of slowly adapting type II (SA-II) tactile afferents located elsewhere in the glabrous skin of the human hand. Little is known about the signals in the SA-IInail afferents when the fingertips interact with objects. Here we show that SA-IInail afferents reliably respond to fingertip forces comparable to those arising in everyday manipulations. Using a flat stimulus surface, we applied forces to the finger pad while recording impulse activity in 17 SA-IInail afferents. Ramp-and-hold forces (amplitude 4 N, rate 10 N/s) were applied normal to the skin, and at 10, 20, or 30 degrees from the normal in eight radial directions with reference to the primary site of contact (25 force directions in total). All afferents responded to the force stimuli, and the responsiveness of all but one afferents was broadly tuned to a preferred direction of force. The preferred directions among afferents were distributed all around the angular space, suggesting that the population of SA-IInail afferents could encode force direction. We conclude that signals in the population of SA-IInail afferents terminating in the nail walls contain vectorial information about fingertip forces. The particular tactile features of contacted surfaces would less influence force-related signals in SA-IInail afferents than force-related signals present in afferents terminating in the volar skin areas that directly contact objects.

Coding and use of tactile signals from the fingertips in object manipulation tasks

During object manipulation tasks, the brain selects and implements action-phase controllers that use sensory predictions and afferent signals to tailor motor output to the physical properties of the objects involved. Analysis of signals in tactile afferent neurons and central processes in humans reveals how contact events are encoded and used to monitor and update task performance.