Computation identifies structural features that govern neuronal firing properties in slowly adapting touch receptors

Biophysical model of muscle spindle encoding

Muscle spindles encode mechanosensory information by mechanisms that remain only partially understood. Their complexity is expressed in mounting evidence of various molecular mechanisms that play essential roles in muscle mechanics, mechanotransduction and intrinsic modulation of muscle spindle firing behaviour. Biophysical modelling provides a tractable approach to achieve more comprehensive mechanistic understanding of such complex systems that would be difficult/impossible by more traditional, reductionist means. Our objective here was to construct the first integrative biophysical model of muscle spindle firing. We leveraged current knowledge of muscle spindle neuroanatomy and in vivo electrophysiology to develop and validate a biophysical model that reproduces key in vivo muscle spindle encoding characteristics. Crucially, to our knowledge, this is the first computational model of mammalian muscle spindle that integrates the asymmetric distribution of known voltage-gated ion channels (VGCs) with neuronal architecture to generate realistic firing profiles, both of which seem likely to be of great biophysical importance. Results predict that particular features of neuronal architecture regulate specific characteristics of Ia encoding. Computational simulations also predict that the asymmetric distribution and ratios of VGCs is a complementary and, in some instances, orthogonal means to regulate Ia encoding. These results generate testable hypotheses and highlight the integral role of peripheral neuronal structure and ion channel composition and distribution in somatosensory signalling.

How Merkel cells transduce mechanical stimuli: A biophysical model of Merkel cells

Merkel cells combine with Aβ afferents, producing slowly adapting type 1(SA1) responses to mechanical stimuli. However, how Merkel cells transduce mechanical stimuli into neural signals to Aβ afferents is still unclear. Here we develop a biophysical model of Merkel cells for mechanical transduction by incorporating main ingredients such as Ca2+ and K+ voltage-gated channels, Piezo2 channels, internal Ca2+ stores, neurotransmitters release, and cell deformation. We first validate our model with several experiments. Then we reveal that Ca2+ and K+ channels on the plasma membrane shape the depolarization of membrane potentials, further regulating the Ca2+ transients in the cells. We also show that Ca2+ channels on the plasma membrane mainly inspire the Ca2+ transients, while internal Ca2+ stores mainly maintain the Ca2+ transients. Moreover, we show that though Piezo2 channels are rapidly adapting mechanical-sensitive channels, they are sufficient to inspire sustained Ca2+ transients in Merkel cells, which further induce the release of neurotransmitters for tens of seconds. Thus our work provides a model that captures the membrane potentials and Ca2+ transients features of Merkel cells and partly explains how Merkel cells transduce the mechanical stimuli by Piezo2 channels.

Coding of self and environment by Pacinian neurons in freely moving animals

Pacinian corpuscle neurons are specialized low-threshold mechanoreceptors (LTMRs) that are tuned to detect high-frequency vibration (~40-2000 Hz), however it is unclear how Pacinians and other LTMRs encode mechanical forces encountered during naturalistic behavior. Here, we developed methods to record LTMRs in awake, freely moving mice. We find that Pacinians, but not other LTMRs, encode subtle vibrations of surfaces encountered by the animal, including low-amplitude vibrations initiated over two meters away. Strikingly, Pacinians are also highly active during a wide variety of natural behaviors, including walking, grooming, digging, and climbing. Pacinians in the hindlimb are sensitive enough to be activated by forelimb- or upper-body-dominant behaviors. Finally, we find that Pacinian LTMRs have diverse tuning and sensitivity. Our findings suggest a Pacinian population code for the representation of vibro-tactile features generated by self-initiated movements and low-amplitude environmental vibrations emanating from distant locations.

Neuronal branching is increasingly asymmetric near synapses, potentially enabling plasticity while minimizing energy dissipation and conduction time

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time and materials. Moreover, we generally observe that higher degrees of asymmetric branching-potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity-occur closer to the tips than the soma (cell body).

Neuronal Branching is Increasingly Asymmetric Near Synapses, Potentially Enabling Plasticity While Minimizing Energy Dissipation and Conduction Time

Neurons' primary function is to encode and transmit information in the brain and body. The branching architecture of axons and dendrites must compute, respond, and make decisions while obeying the rules of the substrate in which they are enmeshed. Thus, it is important to delineate and understand the principles that govern these branching patterns. Here, we present evidence that asymmetric branching is a key factor in understanding the functional properties of neurons. First, we derive novel predictions for asymmetric scaling exponents that encapsulate branching architecture associated with crucial principles such as conduction time, power minimization, and material costs. We compare our predictions with extensive data extracted from images to associate specific principles with specific biophysical functions and cell types. Notably, we find that asymmetric branching models lead to predictions and empirical findings that correspond to different weightings of the importance of maximum, minimum, or total path lengths from the soma to the synapses. These different path lengths quantitatively and qualitatively affect energy, time, and materials. Moreover, we generally observe that higher degrees of asymmetric branching- potentially arising from extrinsic environmental cues and synaptic plasticity in response to activity- occur closer to the tips than the soma (cell body).

Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin

Artificial skin that simultaneously mimics sensory feedback and mechanical properties of natural skin holds substantial promise for next-generation robotic and medical devices. However, achieving such a biomimetic system that can seamlessly integrate with the human body remains a challenge. Through rational design and engineering of material properties, device structures, and system architectures, we realized a monolithic soft prosthetic electronic skin (e-skin). It is capable of multimodal perception, neuromorphic pulse-train signal generation, and closed-loop actuation. With a trilayer, high-permittivity elastomeric dielectric, we achieved a low subthreshold swing comparable to that of polycrystalline silicon transistors, a low operation voltage, low power consumption, and medium-scale circuit integration complexity for stretchable organic devices. Our e-skin mimics the biological sensorimotor loop, whereby a solid-state synaptic transistor elicits stronger actuation when a stimulus of increasing pressure is applied.

Anatomical contacts between sensory neurons and epidermal cells: an unrecognized anatomical network for neuro-immuno-cutaneous crosstalk

Sensory neurons innervating the skin are conventionally thought to be the sole transducers of touch, temperature, pain and itch. However, recent studies have shown that keratinocytes - like Merkel cells - act as sensory transducers, whether for innocuous or noxious mechanical, thermal or chemical stimuli, and communicate with intraepidermal free nerve endings via chemical synaptic contacts. This paradigm shift leads to consideration of the whole epidermis as a sensory epithelium. Sensory neurons additionally function as an efferent system. Through the release of neuropeptides in intimate neuroepidermal contact areas, they contribute to epidermal homeostasis and to the pathogenesis of inflammatory skin diseases. To counteract the dogma regarding neurocutaneous interactions, seen exclusively from the perspective of soluble and spreading mediators, this review highlights the essential contribution of the unrecognized anatomical contacts between sensory neurons and epidermal cells (keratinocytes, melanocytes, Langerhans cells and Merkel cells), which take part in the reciprocal dialogue between the skin, nervous system and immune system.

Do Merkel complexes initiate mechanical itch?

Itch is a common sensation which is amenable to disabling patients' life under pathological and chronic conditions. Shared assertion easily limits itch to chemical itch, without considering mechanical itch and alloknesis, its pathological counterpart. However, in recent years, our understanding of the mechanical itch pathway, particularly in the central nervous system, has been enhanced. In addition, Merkel complexes, conventionally considered as tactile end organs only responsible for light touch perception due to Piezo2 expressed by both Merkel cells and SA1 Aβ-fibres - low threshold mechanical receptors (LTMRs) -, have recently been identified as modulators of mechanical itch. However, the tactile end organs responsible for initiating mechanical itch remain unexplored. The consensus is that some LTMRs, either SA1 Aβ- or A∂- and C-, are cutaneous initiators of mechanical itch, even though they are not self-sufficient to finely detect and encode light mechanical stimuli into sensory perceptions, which depend on the entire hosting tactile end organ. Consequently, to enlighten our understanding of mechanical itch initiation, this article discusses the opportunity to consider Merkel complexes as potential tactile end organs responsible for initiating mechanical itch, under both healthy and pathological conditions. Their unsuspected modulatory abilities indeed show that they are tuned to detect and encode light mechanical stimuli leading to mechanical itch, especially as they host not only SA1 Aβ-LTMRs but also A∂- and C-fibres.

How axon and dendrite branching are guided by time, energy, and spatial constraints

Neurons are connected by complex branching processes-axons and dendrites-that process information for organisms to respond to their environment. Classifying neurons according to differences in structure or function is a fundamental part of neuroscience. Here, by constructing biophysical theory and testing against empirical measures of branching structure, we develop a general model that establishes a correspondence between neuron structure and function as mediated by principles such as time or power minimization for information processing as well as spatial constraints for forming connections. We test our predictions for radius scale factors against those extracted from neuronal images, measured for species that range from insects to whales, including data from light and electron microscopy studies. Notably, our findings reveal that the branching of axons and peripheral nervous system neurons is mainly determined by time minimization, while dendritic branching is determined by power minimization. Our model also predicts a quarter-power scaling relationship between conduction time delay and body size.

Merkel Cells Are Multimodal Sensory Cells: A Review of Study Methods

Merkel cells (MCs) are rare multimodal epidermal sensory cells. Due to their interactions with slowly adapting type 1 (SA1) Aβ low-threshold mechanoreceptor (Aβ-LTMRs) afferents neurons to form Merkel complexes, they are considered to be part of the main tactile terminal organ involved in the light touch sensation. This function has been explored over time by ex vivo, in vivo, in vitro, and in silico approaches. Ex vivo studies have made it possible to characterize the topography, morphology, and cellular environment of these cells. The interactions of MCs with surrounding cells continue to be studied by ex vivo but also in vitro approaches. Indeed, in vitro models have improved the understanding of communication of MCs with other cells present in the skin at the cellular and molecular levels. As for in vivo methods, the sensory role of MC complexes can be demonstrated by observing physiological or pathological behavior after genetic modification in mouse models. In silico models are emerging and aim to elucidate the sensory coding mechanisms of these complexes. The different methods to study MC complexes presented in this review may allow the investigation of their involvement in other physiological and pathophysiological mechanisms, despite the difficulties in exploring these cells, in particular due to their rarity.

Organic Neuroelectronics: From Neural Interfaces to Neuroprosthetics

Requirements and recent advances in research on organic neuroelectronics are outlined herein. Neuroelectronics such as neural interfaces and neuroprosthetics provide a promising approach to diagnose and treat neurological diseases. However, the current neural interfaces are rigid and not biocompatible, so they induce an immune response and deterioration of neural signal transmission. Organic materials are promising candidates for neural interfaces, due to their mechanical softness, excellent electrochemical properties, and biocompatibility. Also, organic nervetronics, which mimics functional properties of the biological nerve system, is being developed to overcome the limitations of the complex and energy-consuming conventional neuroprosthetics that limit long-term implantation and daily-life usage. Examples of organic materials for neural interfaces and neural signal recordings are reviewed, recent advances of organic nervetronics that use organic artificial synapses are highlighted, and then further requirements for neuroprosthetics are discussed. Finally, the future challenges that must be overcome to achieve ideal organic neuroelectronics for next-generation neuroprosthetics are discussed.

Multiple Spatial Spectral Components of Static Skin Deformation for Predicting Macroscopic Roughness Perception

A previous study suggested a relationship between the spatial spectrum of finger pad skin deformation and perception of macroscopic roughness features. This study tested a new hypothesis that macroscopic roughness perception is the result of a weighted linear combination of multiple spatial spectral components of skin deformation. Experiments were conducted by capturing close-up images of finger pad deformation while the pads were pushed onto specimens with macroscopic features. Additionally, the roughness perceptions of these specimens were collected using a magnitude estimation method. The combination of spectral components predicted the roughness perception more accurately than any single spectral component. This suggests that roughness perception is mediated by multiple Gabor filter-like neural systems with different spatial periods, such as visual perception.

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.

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.

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.

Computational approach to understand temporal and spatial tactile transmission processes from mechanical stimuli of the index fingertip to the primary somatosensory cortex

Mechanisms of information transmission using tactile sense are one of major concerns in producing simulated experience in virtual or augmented reality as well as in compensating elderly or impaired people with diminished tactile sensory function. However, important mechanism of the difference of peak latency in the primary somatosensory cortex (SI) between electrical and mechanical stimulations of finger skin is not fully understood. We propose a computational approach to fuse a computational model to simulate temporal and spatial transmission processes from mechanical stimuli to the SI and experimental method using a magnetoencephalograph (MEG). In our model, a tactile model that combined a three-dimensional mechanical model of fingertip skin and a neurophysiological model of a slowly adapting type 1 (SA1) mechanoreceptor was integrated with a somatosensory evoked field (SEF) response model. Electrical and mechanical stimulations were applied to the same locations of the right or left index fingertips of three subjects using a MEG. By identifying parameters of the SEF response model using the electrical stimulation test data, predicted first peak latency due to a mechanical stimulus was identical to its average value obtained from the mechanical stimulation test data, while the spatial map predicted at the multiple SA1 receptors qualitatively corresponded to the MEG image map in the timings of peak latency. This suggests that mechanical change in the skin and neurophysiological responses generate the difference of peak latency in SI between electrical and mechanical stimulations. The computational approach has the potential for detailed investigation of mechanisms of tactile information transmission.

Distinct patterns of activity in individual cortical neurons and local networks in primary somatosensory cortex of mice evoked by square-wave mechanical limb stimulation

Artificial forms of mechanical limb stimulation are used within multiple fields of study to determine the level of cortical excitability and to map the trajectory of neuronal recovery from cortical damage or disease. Square-wave mechanical or electrical stimuli are often used in these studies, but a characterization of sensory-evoked response properties to square-waves with distinct fundamental frequencies but overlapping harmonics has not been performed. To distinguish between somatic stimuli, the primary somatosensory cortex must be able to represent distinct stimuli with unique patterns of activity, even if they have overlapping features. Thus, mechanical square-wave stimulation was used in conjunction with regional and cellular imaging to examine regional and cellular response properties evoked by different frequencies of stimulation. Flavoprotein autofluorescence imaging was used to map the somatosensory cortex of anaesthetized C57BL/6 mice, and in vivo two-photon Ca2+ imaging was used to define patterns of neuronal activation during mechanical square-wave stimulation of the contralateral forelimb or hindlimb at various frequencies (3, 10, 100, 200, and 300 Hz). The data revealed that neurons within the limb associated somatosensory cortex responding to various frequencies of square-wave stimuli exhibit stimulus-specific patterns of activity. Subsets of neurons were found to have sensory-evoked activity that is either primarily responsive to single stimulus frequencies or broadly responsive to multiple frequencies of limb stimulation. High frequency stimuli were shown to elicit more population activity, with a greater percentage of the population responding and greater percentage of cells with high amplitude responses. Stimulus-evoked cell-cell correlations within these neuronal networks varied as a function of frequency of stimulation, such that each stimulus elicited a distinct pattern that was more consistent across multiple trials of the same stimulus compared to trials at different frequencies of stimulation. The variation in cortical response to different square-wave stimuli can thus be represented by the population pattern of supra-threshold Ca2+ transients, the magnitude and temporal properties of the evoked activity, and the structure of the stimulus-evoked correlation between neurons.

Contact-electrification-activated artificial afferents at femtojoule energy

Low power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Highly distributed synaptic sensory neurons are more readily driven by portable, distributed, and ubiquitous power sources. Here, we report a contact-electrification-activated artificial afferent at femtojoule energy. Upon the contact-electrification effect, the induced triboelectric signals activate the ion-gel-gated MoS₂ postsynaptic transistor, endowing the artificial afferent with the adaptive capacity to carry out spatiotemporal recognition/sensation on external stimuli (e.g., displacements, pressures and touch patterns). The decay time of the synaptic device is in the range of sensory memory stage. The energy dissipation of the artificial afferents is significantly reduced to 11.9 fJ per spike. Furthermore, the artificial afferents are demonstrated to be capable of recognizing the spatiotemporal information of touch patterns. This work is of great significance for the construction of next-generation neuromorphic sensory network, self-powered biomimetic electronics and intelligent interactive equipment.

Low power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Here, the authors report contact-electrification-activated artificial afferent at femtojoule energy, which is able to carry out spatiotemporal recognition on external 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.

Information processing in tree networks of excitable elements

We study the collective response of small random tree networks of diffusively coupled excitable elements to stimuli applied to leaf nodes. Such networks model the morphology of certain sensory neurons that possess branched myelinated dendrites with excitable nodes of Ranvier at every branch point and at leaf nodes. Leaf nodes receive random inputs along with a stimulus and initiate action potentials that propagate through the tree. We quantify the collective response registered at the central node using mutual information. We show that in the strong-coupling limit, the statistics of the number of nodes and leaves determines the mutual information. At the same time, the collective response is insensitive to particular node connectivity and distribution of stimulus over leaf nodes. However, for intermediate coupling, the mutual information may strongly depend on the stimulus distribution among leaf nodes. We identify a mechanism behind the competition of leaf nodes that leads to nonmonotonous dependence of mutual information on coupling strength. We show that a localized stimulus given to a tree branch can be occluded by the background firing of unstimulated branches, thus suppressing mutual information. Nonetheless, the mutual information can be enhanced by a proper stimulus localization and tuning of coupling strength.

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.

The Macro- and Micro-Mechanics of the Colon and Rectum II: Theoretical and Computational Methods

Abnormal colorectal biomechanics and mechanotransduction associate with an array of gastrointestinal diseases, including inflammatory bowel disease, irritable bowel syndrome, diverticula disease, anorectal disorders, ileus, and chronic constipation. Visceral pain, principally evoked from mechanical distension, has a unique biomechanical component that plays a critical role in mechanotransduction, the process of encoding mechanical stimuli to the colorectum by sensory afferents. To fully understand the underlying mechanisms of visceral mechanical neural encoding demands focused attention on the macro- and micro-mechanics of colon tissue. Motivated by biomechanical experiments on the colon and rectum, increasing efforts focus on developing constitutive frameworks to interpret and predict the anisotropic and nonlinear biomechanical behaviors of the multilayered colorectum. We will review the current literature on computational modeling of the colon and rectum as well as the mechanical neural encoding by stretch sensitive afferent endings, and then highlight our recent advances in these areas. Current models provide insight into organ- and tissue-level biomechanics as well as the stretch-sensitive afferent endings of colorectal tissues yet an important challenge in modeling theory remains. The research community has not connected the biomechanical models to those of mechanosensitive nerve endings to create a cohesive multiscale framework for predicting mechanotransduction from organ-level biomechanics.

Mechanisms for the Clinical Utility of Low-Frequency Stimulation in Neuromodulation of the Dorsal Root Ganglion

BACKGROUND

Dorsal root ganglion stimulation (DRG-S) involves the electrical modulation of the somata of afferent neural fibers to treat chronic pain. DRG-S has demonstrated clinical efficacy at frequencies lower than typically used with spinal cord stimulation (SCS). In a clinical study, we found that the frequency of DRG-S can be tapered to a frequency as low as 4 Hz with no loss of efficacy. This review discusses possible mechanisms of action underlying effective pain relief with very low-frequency DRG-S.

MATERIALS AND METHODS

We performed a literature review to explore the role of frequency in neural transmission and the corresponding relevance of frequency settings with neuromodulation.

FINDINGS

Sensory neural transmission is a frequency-modulated system, with signal frequency determining which mechanisms are activated in the dorsal horn. In the dorsal horn, low-frequency signaling (<20 Hz) activates inhibitory processes while higher frequencies (>25 Hz) are excitatory. Physiologically, low-threshold mechanoreceptors (LTMRs) fibers transmit or modulate innocuous mechanical touch at frequencies as low as 0.5-5 Hz, while nociceptive fibers transmit pain at high frequencies. We postulate that very low-frequency DRG-S, at least partially, harnesses LTMRs and the native endogenous opioid system. Utilizing lower stimulation frequency decreases the total energy delivery used for DRG-S, extends battery life, and facilitates the development of devices with smaller generators.

Large-Scale Convergence of Receptor Cell Arrays Onto Afferent Terminal Arbors in the Lorenzinian Electroreceptors of Polyodon

Certain sensory receptors contain many transducers, converging onto few afferents. Convergence creates star-topology neural networks, of iterative parallel organization, that may yield special functional properties. We quantitated large-scale convergence in electroreceptors on the rostrum of preadult paddlefish, Polyodon spathula (Acipenseriforme vertebrates), and analyzed the afferent terminal branching underlying the convergence. From neurophysiological mapping, a recorded afferent innervated 23.3 ± 9.1 (range 6-45) ampullary organs, and innervated every ampullary organ within the receptive field's sharp boundary. Ampullary organs each contained ∼665 Lorenzinian receptor cells, from imaging and modeling. We imaged three serial types of afferent branching at electroreceptors, after immunofluorescent labeling for neurite filaments, glial sheaths, or nodal ion channels, or by DiI tracing. (i) Myelinated tree: Each of 3.08 ± 0.51 (2-4) parallel afferents from a cranial nerve (ALLn) entered a receptive field from deeper tissue, then branched into a laminar tree of large myelinated dendrites, parallel to the skin, that branched radially until ∼9 extremities with heminodes, which were candidate sites of spike encoders. (ii) Inline transition: Each myelinated extremity led distally into local unmyelinated arbors originating at inline branching structures covered by terminal (satellite) glia. The unmyelinated transition zones included globular afferent modules, 4-6 microns wide, from which erupted fine fascicles of parallel submicron neurites, a possibly novel type of neuronal branching. The neurite fascicles formed loose bundles projecting ∼105 microns distally to innervate local groups of ∼3 adjacent ampullary organs. (iii) Radial arbors: Receptor cells in an electrosensory neuroepithelium covering the basal pole of each ampullary organ were innervated by bouton endings of radial neurites, unmyelinated and submicron, forming a thin curviplanar lamina distal to the lectin+ basal lamina. The profuse radial neurites diverged from thicker (∼2 micron) basolateral trunks. Overall, an average Polyodon electroreceptor formed a star topology array of ∼9 sensor groups. Total convergence ratios were 15,495 ± 6,052 parallel receptor cells per afferent per mean receptive field, assuming 100% innervation. Large-scale convergence likely increases the signal-to-noise ratio (SNR) of stimulus encoding into spiking afferent output, increasing receiver sensitivity. Unmyelinated arbors may also regenerate and repair the afferent innervation of ampullary organs. LSID: urn:lsid:zoobank.org:act:09BCF04C-3C3C-4B6C-9DC9-A2BF43087369.

The Input-Output Relation of Primary Nociceptive Neurons is Determined by the Morphology of the Peripheral Nociceptive Terminals

The output from the peripheral terminals of primary nociceptive neurons, which detect and encode the information regarding noxious stimuli, is crucial in determining pain sensation. The nociceptive terminal endings are morphologically complex structures assembled from multiple branches of different geometry, which converge in a variety of forms to create the terminal tree. The output of a single terminal is defined by the properties of the transducer channels producing the generation potentials and voltage-gated channels, translating the generation potentials into action potential (AP) firing. However, in the majority of cases, noxious stimuli activate multiple terminals; thus, the output of the nociceptive neuron is defined by the integration and computation of the inputs of the individual terminals. Here, we used a computational model of nociceptive terminal tree to study how the architecture of the terminal tree affects the input-output relation of the primary nociceptive neurons. We show that the input-output properties of the nociceptive neurons depend on the length, the axial resistance (Ra), and location of individual terminals. Moreover, we show that activation of multiple terminals by a capsaicin-like current allows summation of the responses from individual terminals, thus leading to increased nociceptive output. Stimulation of the terminals in simulated models of inflammatory or neuropathic hyperexcitability led to a change in the temporal pattern of AP firing, emphasizing the role of temporal code in conveying key information about changes in nociceptive output in pathologic conditions, leading to pain hypersensitivity.SIGNIFICANCE STATEMENT Noxious stimuli are detected by terminal endings of primary nociceptive neurons, which are organized into morphologically complex terminal trees. The information from multiple terminals is integrated along the terminal tree, computing the neuronal output, which propagates toward the CNS, thus shaping the pain sensation. Here, we revealed that the structure of the nociceptive terminal tree determines the output of nociceptive neurons. We show that the integration of noxious information depends on the morphology of the terminal trees and how this integration and, consequently, the neuronal output change under pathologic conditions. Our findings help to predict how nociceptive neurons encode noxious stimuli and how this encoding changes in pathologic conditions, leading to pain.

Morphological and physiological properties of Rohon-Beard neurons along the zebrafish spinal cord

Primary mechanosensory neurons play an important role in converting mechanical forces into the sense of touch. In zebrafish, Rohon-Beard (RB) neurons serve this role at embryonic and larval stages of development. Here we examine the morphology and physiology of RBs in larval zebrafish to better understand how mechanosensory stimuli are represented along the spinal cord. We report that the morphology of RB neurons differs along the rostrocaudal body axis. Rostral RB neurons arborize in the skin near the cell body whereas caudal cells arborize at a distance posterior to their cell body. Using a novel electrophysiological approach, we also found longitudinal differences in the mechanosensitivity and physiological properties of RB neurons. Rostral RB neurons respond to mechanical stimulations close to the soma and produce up to three spikes with increasing stimulus intensity, whereas caudal cells respond at more distal locations and can produce four or more spikes when the intensity of the mechanical stimulus increases. The mechanosensory properties of RB neurons are consistent with those of rapidly adapting mechanoreceptors and can signal the onset, offset and intensity of mechanical stimulation. This is the first report of the intensity encoding properties of RB neurons, where an increase in spike number and a decrease in spike latency are observed with increasing stimulation intensity. This study reveals an unappreciated complexity of the larval zebrafish mechanosensory system and demonstrates how differences in the morphological and physiological properties of RBs related to their rostrocaudal location can influence the signals that enter the spinal cord.

The Pathways and Processes Underlying Spinal Transmission of Low Back Pain: Observations From Dorsal Root Ganglion Stimulation Treatment

BACKGROUND

Dorsal root ganglion stimulation (DRG-S) is a novel approach to treat chronic pain. Lead placement at L2 has been reported to be an effective treatment for axial low back pain (LBP) primarily of discogenic etiology. We have recently shown, in a diverse cohort including cases of multilevel instrumentation following extensive prior back surgeries, that DRG-S lead placement at T12 is another promising target. Local effects at the T12 DRG, alone, are insufficient to explain these results.

MATERIALS AND METHODS

We performed a literature review to explore the mechanisms of LBP relief with T12 DRG-S.

FINDINGS

Branches of individual spinal nerve roots innervate facet joints and posterior spinal structures, while the discs and anterior vertebrae are carried via L2, and converge in the dorsal horn (DH) of the spinal cord at T8-T9. The T12 nerve root contains cutaneous afferents from the low back and enters the DH of the spinal cord at T10. Low back Aδ and C-fibers then ascend via Lissauer's tract (LT) to T8-T9, converging with other low back afferents. DRG-S at T12, then, results in inhibition of the converged low back fibers via endorphin-mediated and GABAergic frequency-dependent mechanisms. Therefore, T12 lead placement may be the optimal location for DRG-S to treat LBP.

Tactile sensory coding and learning with bio-inspired optoelectronic spiking afferent nerves

The integration and cooperation of mechanoreceptors, neurons and synapses in somatosensory systems enable humans to efficiently sense and process tactile information. Inspired by biological somatosensory systems, we report an optoelectronic spiking afferent nerve with neural coding, perceptual learning and memorizing capabilities to mimic tactile sensing and processing. Our system senses pressure by MXene-based sensors, converts pressure information to light pulses by coupling light-emitting diodes to analog-to-digital circuits, then integrates light pulses using a synaptic photomemristor. With neural coding, our spiking nerve is capable of not only detecting simultaneous pressure inputs, but also recognizing Morse code, braille, and object movement. Furthermore, with dimensionality-reduced feature extraction and learning, our system can recognize and memorize handwritten alphabets and words, providing a promising approach towards e-skin, neurorobotics and human-machine interaction technologies.

The Cellular and Mechanical Basis for Response Characteristics of Identified Primary Afferents in the Rat Vibrissal System

Compared to our understanding of the response properties of receptors in the auditory and visual systems, we have only a limited understanding of the mechanoreceptor responses that underlie tactile sensation. Here, we exploit the stereotyped morphology of the rat vibrissal (whisker) array to investigate coding and transduction properties of identified primary tactile afferents. We performed in vivo intra-axonal recording and labeling experiments to quantify response characteristics of four different types of identified mechanoreceptors in the vibrissal follicle: ring-sinus Merkel; lanceolate; clublike; and rete-ridge collar Merkel. Of these types, only ring-sinus Merkel endings exhibited slowly adapting properties. A weak inverse relationship between response magnitude and onset response latency was found across all types. All afferents exhibited strong "angular tuning," i.e., their response magnitude and latency depended on the whisker's deflection angle. Although previous studies suggested that this tuning should be aligned with the angular location of the mechanoreceptor in the follicle, such alignment was observed only for Merkel afferents; angular tuning of the other afferent types showed no clear alignment with mechanoreceptor location. Biomechanical modeling suggested that this tuning difference might be explained by mechanoreceptors' differential sensitivity to the force directed along the whisker length. Electron microscopic investigations of Merkel endings and lanceolate endings at the level of the ring sinus revealed unique anatomical features that may promote these differential sensitivities. The present study systematically integrates biomechanical principles with the anatomical and morphological characterization of primary afferent endings to describe the physical and cellular processing that shapes the neural representation of touch.

Progressive recruitment of distal MEC-4 channels determines touch response strength in C. elegans

Touch deforms, or strains, the skin beyond the immediate point of contact. The spatiotemporal nature of the touch-induced strain fields depend on the mechanical properties of the skin and the tissues below. Somatosensory neurons that sense touch branch out within the skin and rely on a set of mechano-electrical transduction channels distributed within their dendrites to detect mechanical stimuli. Here, we sought to understand how tissue mechanics shape touch-induced mechanical strain across the skin over time and how individual channels located in different regions of the strain field contribute to the overall touch response. We leveraged Caenorhabditis elegans' touch receptor neurons as a simple model amenable to in vivo whole-cell patch-clamp recording and an integrated experimental-computational approach to dissect the mechanisms underlying the spatial and temporal dynamics we observed. Consistent with the idea that strain is produced at a distance, we show that delivering strong stimuli outside the anatomical extent of the neuron is sufficient to evoke MRCs. The amplitude and kinetics of the MRCs depended on both stimulus displacement and speed. Finally, we found that the main factor responsible for touch sensitivity is the recruitment of progressively more distant channels by stronger stimuli, rather than modulation of channel open probability. This principle may generalize to somatosensory neurons with more complex morphologies.

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

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

The cellular basis of mechanosensory Merkel-cell innervation during development

Touch sensation is initiated by mechanosensory neurons that innervate distinct skin structures; however, little is known about how these neurons are patterned during mammalian skin development. We explored the cellular basis of touch-receptor patterning in mouse touch domes, which contain mechanosensory Merkel cell-neurite complexes and abut primary hair follicles. At embryonic stage 16.5 (E16.5), touch domes emerge as patches of Merkel cells and keratinocytes clustered with a previously unsuspected population of Bmp4-expressing dermal cells. Epidermal Noggin overexpression at E14.5 disrupted touch-dome formation but not hair-follicle specification, demonstrating a temporally distinct requirement for BMP signaling in placode-derived structures. Surprisingly, two neuronal populations preferentially targeted touch domes during development but only one persisted in mature touch domes. Finally, Keratin-17-expressing keratinocytes but not Merkel cells were necessary to establish innervation patterns during development. These findings identify key cell types and signaling pathways required for targeting Merkel-cell afferents to discrete mechanosensory compartments.

Merkel Cells Activate Sensory Neural Pathways through Adrenergic Synapses

Epithelial-neuronal signaling is essential for sensory encoding in touch, itch, and nociception; however, little is known about the release mechanisms and neurotransmitter receptors through which skin cells govern neuronal excitability. Merkel cells are mechanosensory epidermal cells that have long been proposed to activate neuronal afferents through chemical synaptic transmission. We employed a set of classical criteria for chemical neurotransmission as a framework to test this hypothesis. RNA sequencing of adult mouse Merkel cells demonstrated that they express presynaptic molecules and biosynthetic machinery for adrenergic transmission. Moreover, live-cell imaging directly demonstrated that Merkel cells mediate activity- and VMAT-dependent release of fluorescent catecholamine neurotransmitter analogs. Touch-evoked firing in Merkel-cell afferents was inhibited either by pre-synaptic silencing of SNARE-mediated vesicle release from Merkel cells or by neuronal deletion of β₂-adrenergic receptors. Together, these results identify both pre- and postsynaptic mechanisms through which Merkel cells excite mechanosensory afferents to encode gentle touch. VIDEO ABSTRACT.

Computation predicts rapidly adapting mechanotransduction currents cannot account for tactile encoding in Merkel cell-neurite complexes

Distinct firing properties among touch receptors are influenced by multiple, interworking anatomical structures. Our understanding of the functions and crosstalk of Merkel cells and their associated neurites—the end organs of slowly adapting type I (SAI) afferents—remains incomplete. Piezo2 mechanically activated channels are required both in Merkel cells and in sensory neurons for canonical SAI responses in rodents; however, a central unanswered question is how rapidly inactivating currents give rise to sustained action potential volleys in SAI afferents. The computational model herein synthesizes mechanotransduction currents originating from Merkel cells and neurites, in context of skin mechanics and neural dynamics. Its goal is to mimic distinct spike firing patterns from wildtype animals, as well as Atoh1 knockout animals that completely lack Merkel cells. The developed generator function includes a Merkel cell mechanism that represents its mechanotransduction currents and downstream voltage-activated conductances (slower decay of current) and a neurite mechanism that represents its mechanotransduction currents (faster decay of current). To mimic sustained firing in wildtype animals, a longer time constant was needed than the 200 ms observed for mechanically activated membrane depolarizations in rodent Merkel cells. One mechanism that suffices is to introduce an ultra-slowly inactivating current, with a time constant on the order of 1.7 s. This mechanism may drive the slow adaptation of the sustained response, for which the skin’s viscoelastic relaxation cannot account. Positioned within the sensory neuron, this source of current reconciles the physiology and anatomical characteristics of Atoh1 knockout animals.

Slowly-adapting type I (SAI) cutaneous afferents help us discriminate fine spatial details. Their physiology and anatomy are distinguished by their slow adaptation in firing to held stimuli and innervation of Merkel cells, respectively. How mechanotransduction currents in Merkel cells and sensory neurons combine to give rise to neural spike firing is unknown. In considering wildtype animals, as well as Atoh1 conditional knockout animals that lack Merkel cells, this effort employs a computational modeling approach constrained by biological measurements. For the developed generator function to recapitulate firing responses across genotype, a previously unsuspected current source is required. Thus, the model makes specific predictions for future experimental studies.

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor

Inspired by the biology of human tactile perception, a hardware neuromorphic approach is proposed for spiking model of mechanoreceptors to encode the input force. In this way, a digital circuit is designed for a slowly adapting type I (SA-I) and fast adapting type I (FA-I) mechanoreceptors to be implemented on a low-cost digital hardware, such as field-programmable gate array (FPGA). This system computationally replicates the neural firing responses of both afferents. Then, comparative simulations are shown. The spiking models of mechanoreceptors are first simulated in MATLAB and next the digital neuromorphic circuits simulated in VIVADO are also compared to show that obtained results are in good agreement both quantitatively and qualitatively. Finally, we test the performance of the proposed digital mechanoreceptors in hardware using a prepared experimental set up. Hardware synthesis and physical realization on FPGA indicate that the digital mechanoreceptors are able to replicate essential characteristics of different firing patterns including bursting and spiking responses of the SA-I and FA-I mechanoreceptors. In addition to parallel computation, a main advantage of this method is that the mechanoreceptor digital circuits can be implemented in real-time through low-power neuromorphic hardware. This novel engineering framework is generally suitable for use in robotic and hand-prosthetic applications, so progressing the state of the art for tactile sensing.

A bioinspired flexible organic artificial afferent nerve

The distributed network of receptors, neurons, and synapses in the somatosensory system efficiently processes complex tactile information. We used flexible organic electronics to mimic the functions of a sensory nerve. Our artificial afferent nerve collects pressure information (1 to 80 kilopascals) from clusters of pressure sensors, converts the pressure information into action potentials (0 to 100 hertz) by using ring oscillators, and integrates the action potentials from multiple ring oscillators with a synaptic transistor. Biomimetic hierarchical structures can detect movement of an object, combine simultaneous pressure inputs, and distinguish braille characters. Furthermore, we connected our artificial afferent nerve to motor nerves to construct a hybrid bioelectronic reflex arc to actuate muscles. Our system has potential applications in neurorobotics and neuroprosthetics.

Fast and accurate edge orientation processing during object manipulation

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

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

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

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

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

A DISCRETE-EVENT SIMULATION APPROACH TO IDENTIFY RULES THAT GOVERN ARBOR REMODELING FOR BRANCHING CUTANEOUS AFFERENTS IN HAIRY SKIN

In mammals, touch is encoded by sensory receptors embedded in the skin. For one class of receptors in the mouse, the architecture of its Merkel cells, unmyelinated neurites, and heminodes follow particular renewal and remodeling trends over hair cycle stages from ages 4 to 10 weeks. As it is currently impossible to observe such trends across a single animal's hair cycle, this work employs discrete event simulation to identify and evaluate policies of Merkel cell and heminode dynamics. Well matching the observed data, the results show that the baseline model replicates dynamic remodeling behaviors between stages of the hair cycle - based on particular addition and removal polices and estimated probabilities tied to constituent parts of Merkel cells, terminal branch neurites and heminodes. The analysis shows further that certain policies hold greater influence than others. This use of computation is a novel approach to understanding neuronal development.

Developing a sense of touch

The sensation of touch is mediated by mechanosensory neurons that are embedded in skin and relay signals from the periphery to the central nervous system. During embryogenesis, axons elongate from these neurons to make contact with the developing skin. Concurrently, the epithelium of skin transforms from a homogeneous tissue into a heterogeneous organ that is made up of distinct layers and microdomains. Throughout this process, each neuronal terminal must form connections with an appropriate skin region to serve its function. This Review presents current knowledge of the development of the sensory microdomains in mammalian skin and the mechanosensory neurons that innervate them.

Touch Receptors Undergo Rapid Remodeling in Healthy Skin

Sensory tissues exposed to the environment, such as skin, olfactory epithelia, and taste buds, continuously renew; therefore, peripheral neurons must have mechanisms to maintain appropriate innervation patterns. Although somatosensory neurons regenerate after injury, little is known about how these neurons cope with normal target organ changes. To elucidate neuronal plasticity in healthy skin, we analyzed the structure of Merkel-cell afferents, which are gentle touch receptors, during skin remodeling that accompanies mouse hair-follicle regeneration. The number of Merkel cells is reduced by 90% and axonal arbors are simplified during active hair growth. These structures rebound within just days. Computational modeling predicts that Merkel-cell changes are probabilistic, but myelinated branch stability depends on Merkel-cell inputs. Electrophysiology and behavior demonstrate that tactile responsiveness is less reliable during active growth than in resting skin. These results reveal that somatosensory neurons display structural plasticity at the cost of impairment in the reliability of encoding gentle touch.

Determining the biomechanics of touch sensation in C. elegans

The sense of touch is a fundamental mechanism that nearly all organisms use to interact with their surroundings. However, the process of mechanotransduction whereby a mechanical stimulus gives rise to a neuronal response is not well understood. In this paper we present an investigation of the biomechanics of touch using the model organism C. elegans. By developing a custom micromanipulation and force sensing system around a high resolution optical microscope, we measured the spatial deformation of the organism’s cuticle and force response to controlled uniaxial indentations. We combined these experimental results with anatomical data to create a multilayer computational biomechanical model of the organism and accurately derive its material properties such as the elastic modulus and poisson’s ratio. We demonstrate the utility of this model by combining it with previously published electrophysiological data to provide quantitative insights into different biomechanical states for mechanotransduction, including the first estimate of the sensitivity of an individual mechanoreceptor to an applied stimulus (parameterised as strain energy density). We also interpret empirical behavioural data to estimate the minimum number of mechanoreceptors which must be activated to elicit a behavioural response.

Simulating tactile signals from the whole hand with millisecond precision

When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.

Emergent stochastic oscillations and signal detection in tree networks of excitable elements

We study the stochastic dynamics of strongly-coupled excitable elements on a tree network. The peripheral nodes receive independent random inputs which may induce large spiking events propagating through the branches of the tree and leading to global coherent oscillations in the network. This scenario may be relevant to action potential generation in certain sensory neurons, which possess myelinated distal dendritic tree-like arbors with excitable nodes of Ranvier at peripheral and branching nodes and exhibit noisy periodic sequences of action potentials. We focus on the spiking statistics of the central node, which fires in response to a noisy input at peripheral nodes. We show that, in the strong coupling regime, relevant to myelinated dendritic trees, the spike train statistics can be predicted from an isolated excitable element with rescaled parameters according to the network topology. Furthermore, we show that by varying the network topology the spike train statistics of the central node can be tuned to have a certain firing rate and variability, or to allow for an optimal discrimination of inputs applied at the peripheral nodes.

A 3D Computational Model of Transcutaneous Electrical Nerve Stimulation for Estimating Aβ Tactile Nerve Fiber Excitability

Tactile sensory feedback plays an important role in our daily life. Transcutaneous electrical nerve stimulation (TENS) is widely accepted to produce artificial tactile sensation. To explore the underlying mechanism of tactile sensation under TENS, this paper presented a novel 3D TENS computational model including an active Aβ tactile nerve fiber (TNF) model and a forearm finite element model with the fine-layered skin structure. The conduction velocity vs. fiber diameter and strength-duration relationships in this combined TENS model matched well with experimental data. Based on this validated TENS model, threshold current variation were further investigated under different stimulating electrode sizes with varied fiber diameters. The computational results showed that the threshold current intensity increased with electrode size, and larger nerve fibers were recruited at lower current intensities. These results were comparable to our psychophysical experimental data from six healthy subjects. This novel 3D TENS model would further guide the floorplan of the surface electrodes, and the stimulating paradigms for tactile sensory feedback.

Neuromorphic Artificial Touch for Categorization of Naturalistic Textures

We implemented neuromorphic artificial touch and emulated the firing behavior of mechanoreceptors by injecting the raw outputs of a biomimetic tactile sensor into an Izhikevich neuronal model. Naturalistic textures were evaluated with a passive touch protocol. The resulting neuromorphic spike trains were able to classify ten naturalistic textures ranging from textiles to glass to BioSkin, with accuracy as high as 97%. Remarkably, rather than on firing rate features calculated over the stimulation window, the highest achieved decoding performance was based on the precise spike timing of the neuromorphic output as captured by Victor Purpura distance. We also systematically varied the sliding velocity and the contact force to investigate the role of sensing conditions in categorizing the stimuli via the artificial sensory system. We found that the decoding performance based on the timing of neuromorphic spike events was robust for a broad range of sensing conditions. Being able to categorize naturalistic textures in different sensing conditions, these neurorobotic results pave the way to the use of neuromorphic tactile sensors in future real-life neuroprosthetic applications.

The biology of skin wetness perception and its implications in manual function and for reproducing complex somatosensory signals in neuroprosthetics

Our perception of skin wetness is generated readily, yet humans have no known receptor (hygroreceptor) to signal this directly. It is easy to imagine the sensation of water running over our hands or the feel of rain on our skin. The synthetic sensation of wetness is thought to be produced from a combination of specific skin thermal and tactile inputs, registered through thermoreceptors and mechanoreceptors, respectively. The present review explores how thermal and tactile afference from the periphery can generate the percept of wetness centrally. We propose that the main signals include information about skin cooling, signaled primarily by thinly myelinated thermoreceptors, and rapid changes in touch, through fast-conducting, myelinated mechanoreceptors. Potential central sites for integration of these signals, and thus the perception of skin wetness, include the primary and secondary somatosensory cortices and the insula cortex. The interactions underlying these processes can also be modeled to aid in understanding and engineering the mechanisms. Furthermore, we discuss the role that sensing wetness could play in precision grip and the dexterous manipulation of objects. We expand on these lines of inquiry to the application of the knowledge in designing and creating skin sensory feedback in prosthetics. The addition of real-time, complex sensory signals would mark a significant advance in the use and incorporation of prosthetic body parts for amputees in everyday life.NEW & NOTEWORTHY Little is known about the underlying mechanisms that generate the perception of skin wetness. Humans have no specific hygroreceptor, and thus temperature and touch information combine to produce wetness sensations. The present review covers the potential mechanisms leading to the perception of wetness, both peripherally and centrally, along with their implications for manual function. These insights are relevant to inform the design of neuroengineering interfaces, such as sensory prostheses for amputees.

Distribution of TTX-sensitive voltage-gated sodium channels in primary sensory endings of mammalian muscle spindles

Knowledge of the molecular mechanisms underlying signaling of mechanical stimuli by muscle spindles remains incomplete. In particular, the ionic conductances that sustain tonic firing during static muscle stretch are unknown. We hypothesized that tonic firing by spindle afferents depends on sodium persistent inward current (INaP) and tested for the necessary presence of the appropriate voltage-gated sodium (NaV) channels in primary sensory endings. The NaV_1.6 isoform was selected for both its capacity to produce INaP and for its presence in other mechanosensors that fire tonically. The present study shows that NaV_1.6 immunoreactivity (IR) is concentrated in heminodes, presumably where tonic firing is generated, and we were surprised to find NaV_1.6 IR strongly expressed also in the sensory terminals, where mechanotransduction occurs. This spatial pattern of NaV_1.6 IR distribution was consistent for three mammalian species (rat, cat, and mouse), as was tonic firing by primary spindle afferents. These findings meet some of the conditions needed to establish participation of INaP in tonic firing by primary sensory endings. The study was extended to two additional NaV isoforms, selected for their sensitivity to TTX, excluding TTX-resistant NaV channels, which alone are insufficient to support firing by primary spindle endings. Positive immunoreactivity was found for NaV_1.1, predominantly in sensory terminals together with NaV_1.6 and for NaV_1.7, mainly in preterminal axons. Differential distribution in primary sensory endings suggests specialized roles for these three NaV isoforms in the process of mechanosensory signaling by muscle spindles.NEW & NOTEWORTHY The molecular mechanisms underlying mechanosensory signaling responsible for proprioceptive functions are not completely elucidated. This study provides the first evidence that voltage-gated sodium channels (NaVs) are expressed in the spindle primary sensory ending, where NaVs are found at every site involved in transduction or encoding of muscle stretch. We propose that NaVs contribute to multiple steps in sensory signaling by muscle spindles as it does in other types of slowly adapting sensory neurons.

Feeling force: physical and physiological principles enabling sensory mechanotransduction

Organisms as diverse as microbes, roundworms, insects, and mammals detect and respond to applied force. In animals, this ability depends on ionotropic force receptors, known as mechanoelectrical transduction (MeT) channels, that are expressed by specialized mechanoreceptor cells embedded in diverse tissues and distributed throughout the body. These cells mediate hearing, touch, and proprioception and play a crucial role in regulating organ function. Here, we attempt to integrate knowledge about the architecture of mechanoreceptor cells and their sensory organs with principles of cell mechanics, and we consider how engulfing tissues contribute to mechanical filtering. We address progress in the quest to identify the proteins that form MeT channels and to understand how these channels are gated. For clarity and convenience, we focus on sensory mechanobiology in nematodes, fruit flies, and mice. These themes are emphasized: asymmetric responses to applied forces, which may reflect anisotropy of the structure and mechanics of sensory mechanoreceptor cells, and proteins that function as MeT channels, which appear to have emerged many times through evolution.