Segregation of tactile input features in neurons of the cuneate nucleus

Human tactile sensing and sensorimotor mechanism: from afferent tactile signals to efferent motor control

In tactile sensing, decoding the journey from afferent tactile signals to efferent motor commands is a significant challenge primarily due to the difficulty in capturing population-level afferent nerve signals during active touch. This study integrates a finite element hand model with a neural dynamic model by using microneurography data to predict neural responses based on contact biomechanics and membrane transduction dynamics. This research focuses specifically on tactile sensation and its direct translation into motor actions. Evaluations of muscle synergy during in -vivo experiments revealed transduction functions linking tactile signals and muscle activation. These functions suggest similar sensorimotor strategies for grasping influenced by object size and weight. The decoded transduction mechanism was validated by restoring human-like sensorimotor performance on a tendon-driven biomimetic hand. This research advances our understanding of translating tactile sensation into motor actions, offering valuable insights into prosthetic design, robotics, and the development of next-generation prosthetics with neuromorphic tactile feedback.

Multimodal mapping of macaque monkey somatosensory cortex

The somatosensory cortex is a brain region responsible for receiving and processing sensory information from across the body and is structurally and functionally heterogeneous. Since the chemoarchitectonic segregation of the cerebral cortex can be revealed by transmitter receptor distribution patterns, by using a quantitative multireceptor architectonical analysis, we determined the number and extent of distinct areas of the macaque somatosensory cortex. We identified three architectonically distinct cortical entities within the primary somatosensory cortex (i.e., 3bm, 3bli, 3ble), four within the anterior parietal cortex (i.e., 3am, 3al, 1 and 2) and six subdivisions (i.e., S2l, S2m, PVl, PVm, PRl and PRm) within the lateral fissure. We provide an ultra-high resolution 3D atlas of macaque somatosensory areas in stereotaxic space, which integrates cyto- and receptor architectonic features of identified areas. Multivariate analyses of the receptor fingerprints revealed four clusters of identified areas based on the degree of (dis)similarity of their receptor architecture. Each of these clusters can be associated with distinct levels of somatosensory processing, further demonstrating that the functional segregation of cortical areas is underpinned by differences in their molecular organization.

Modulation of somatosensory signal transmission in the primate cuneate nucleus during voluntary hand movement

Primate hands house an array of mechanoreceptors and proprioceptors, which are essential for tactile and kinematic information crucial for daily motor action. While the regulation of these somatosensory signals is essential for hand movements, the specific central nervous system (CNS) location and mechanism remain unclear. Our study demonstrates the attenuation of somatosensory signals in the cuneate nucleus during voluntary movement, suggesting significant modulation at this initial relay station in the CNS. The attenuation is comparable to the cerebral cortex but more pronounced than in the spinal cord, indicating the cuneate nuclei's role in somatosensory perception modulation during movement. Moreover, our findings suggest that the descending motor tract may regulate somatosensory transmission in the cuneate nucleus, enhancing relevant signals and suppressing unnecessary ones for the regulation of movement. This process of recurrent somatosensory modulation between cortical and subcortical areas could be a basic mechanism for modulating somatosensory signals to achieve active perception.

Revisiting a classical theory of sensory specificity: assessing consistency and stability of thermosensitive spots

Thermal sensitivity is not uniform across the skin, and is particularly high in small (∼1 mm2) regions termed "thermosensitive spots." These spots are thought to reflect the anatomical location of specialized thermosensitive nerve endings from single primary afferents. Thermosensitive spots provide foundational support for "labeled line" or specificity theory of sensory perception, which states that different sensory qualities are transmitted by separate and specific neural pathways. This theory predicts a highly stable relation between repetitions of a thermal stimulus and the resulting sensory quality, yet these predictions have rarely been tested systematically. Here, we present the qualitative, spatial, and repeatability properties of 334 thermosensitive spots on the dorsal forearm sampled across four separate sessions. In line with previous literature, we found that spots associated with cold sensations (112 cold spots, 34%) were more frequent than spots associated with warm sensations (41 warm spots, 12%). Still more frequent (165 spots, 49%) were spots that elicited inconsistent sensations when repeatedly stimulated by the same temperature. Remarkably, only 13 spots (4%) conserved their position between sessions. Overall, we show unexpected inconsistency of both the perceptual responses elicited by spot stimulation and of spot locations across time. These observations suggest reappraisals of the traditional view that thermosensitive spots reflect the location of individual thermosensitive, unimodal primary afferents serving as specific labeled lines for corresponding sensory qualities.NEW & NOTEWORTHY Thermosensitive spots are clustered rather than randomly distributed and have the highest density near the wrist. Surprisingly, we found that thermosensitive spots elicit inconsistent sensory qualities and are unstable over time. Our results question the widely believed notion that thermosensitive spots reflect the location of individual thermoreceptive, unimodal primary afferents that serve as labelled lines for corresponding sensory qualities.

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

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

Proprioception: A New Era Set in Motion by Emerging Genetic and Bionic Strategies?

The generation of an internal body model and its continuous update is essential in sensorimotor control. Although known to rely on proprioceptive sensory feedback, the underlying mechanism that transforms this sensory feedback into a dynamic body percept remains poorly understood. However, advances in the development of genetic tools for proprioceptive circuit elements, including the sensory receptors, are beginning to offer new and unprecedented leverage to dissect the central pathways responsible for proprioceptive encoding. Simultaneously, new data derived through emerging bionic neural machine-interface technologies reveal clues regarding the relative importance of kinesthetic sensory feedback and insights into the functional proprioceptive substrates that underlie natural motor behaviors.

Humans sense by touch the location of objects that roll in handheld containers

Humans use active touch to gain behaviourally relevant information from their environment, including information about contained objects. Although most common, the perceptual basis of interacting with containers remains largely unexplored. The first aim of this study was to determine how accurately people can sense, by touch only, the location of a contained rolling object. Experiment 1 used tubes containing physical balls and demonstrated a considerable degree of accuracy in estimating the rolled distance. The second aim was to identify the relative effectiveness of the various available physical cues. Experiment 2 employed virtual reality technology to present, in isolation and in various combinations, the constituent haptic cues produced by a rolling ball, which are, the mechanical noise during rolling, the jolts from an impact with an internal wall, and the intensity and timing of the jolts resulting from elastic bounces. The rolling noise was of primary importance to the perceptual estimation task suggesting that the implementation of the laws of motion is based on an analysis of the ball's movement velocity. Although estimates became more accurate when the rolling and impact cues were combined, they were not necessarily more precise. The presence of elastic bounces did not affect performance.

The encoding of touch by somatotopically aligned dorsal column subdivisions

The somatosensory system decodes a range of tactile stimuli to generate a coherent sense of touch. Discriminative touch of the body depends on signals conveyed from peripheral mechanoreceptors to the brain through the spinal cord dorsal column and its brainstem target, the dorsal column nuclei (DCN)1,2. Models of somatosensation emphasize that fast-conducting low-threshold mechanoreceptors (LTMRs) innervating the skin drive the DCN3,4. However, postsynaptic dorsal column (PSDC) neurons within the spinal cord dorsal horn also collect mechanoreceptor signals and form a second major input to the DCN5-7. The significance of PSDC neurons and their contributions to the coding of touch have remained unclear since their discovery. Here we show that direct LTMR input to the DCN conveys vibrotactile stimuli with high temporal precision. Conversely, PSDC neurons primarily encode touch onset and the intensity of sustained contact into the high-force range. LTMR and PSDC signals topographically realign in the DCN to preserve precise spatial detail. Different DCN neuron subtypes have specialized responses that are generated by distinct combinations of LTMR and PSDC inputs. Thus, LTMR and PSDC subdivisions of the dorsal column encode different tactile features and differentially converge in the DCN to generate specific ascending sensory processing streams.

A Model for Self-Organization of Sensorimotor Function: Spinal Interneuronal Integration

Control of musculoskeletal systems depends on integration of voluntary commands and somatosensory feedback in the complex neural circuits of the spinal cord. Particular connectivity patterns have been identified experimentally, and it has been suggested that these may result from the wide variety of transcriptional types that have been observed in spinal interneurons. We ask instead whether the details of these connectivity patterns (and perhaps many others) can arise as a consequence of Hebbian adaptation during early development. We constructed an anatomically simplified model plant system with realistic muscles and sensors and connected it to a recurrent, random neuronal network consisting of both excitatory and inhibitory neurons endowed with Hebbian learning rules. We then generated a wide set of randomized muscle twitches typical of those described during fetal development and allowed the network to learn. Multiple simulations consistently resulted in diverse and stable patterns of activity and connectivity that included subsets of the interneurons that were similar to 'archetypical' interneurons described in the literature. We also found that such learning led to an increased degree of cooperativity between interneurons when performing larger limb movements on which it had not been trained. Hebbian learning gives rise to rich sets of diverse interneurons whose connectivity reflects the mechanical properties of the plant. At least some of the transcriptomic diversity may reflect the effects of this process rather than the cause of the connectivity. Such a learning process seems better suited to respond to the musculoskeletal mutations that underlie the evolution of new species.

Sensory computations in the cuneate nucleus of macaques

Tactile nerve fibers fall into a few classes that can be readily distinguished based on their spatiotemporal response properties. Because nerve fibers reflect local skin deformations, they individually carry ambiguous signals about object features. In contrast, cortical neurons exhibit heterogeneous response properties that reflect computations applied to convergent input from multiple classes of afferents, which confer to them a selectivity for behaviorally relevant features of objects. The conventional view is that these complex response properties arise within the cortex itself, implying that sensory signals are not processed to any significant extent in the two intervening structures-the cuneate nucleus (CN) and the thalamus. To test this hypothesis, we recorded the responses evoked in the CN to a battery of stimuli that have been extensively used to characterize tactile coding in both the periphery and cortex, including skin indentations, vibrations, random dot patterns, and scanned edges. We found that CN responses are more similar to their cortical counterparts than they are to their inputs: CN neurons receive input from multiple classes of nerve fibers, they have spatially complex receptive fields, and they exhibit selectivity for object features. Contrary to consensus, then, the CN plays a key role in processing tactile information.

Generalized attention-weighted reinforcement learning

In neuroscience, attention has been shown to bidirectionally interact with reinforcement learning (RL) to reduce the dimensionality of task representations, restricting computations to relevant features. In machine learning, despite their popularity, attention mechanisms have seldom been administered to decision-making problems. Here, we leverage a theoretical model from computational neuroscience - the attention-weighted RL (AWRL), defining how humans identify task-relevant features (i.e., that allow value predictions) - to design an applied deep RL paradigm. We formally demonstrate that the conjunction of the self-attention mechanism, widely employed in machine learning, with value function approximation is a general formulation of the AWRL model. To evaluate our agent, we train it on three Atari tasks at different complexity levels, incorporating both task-relevant and irrelevant features. Because the model uses semantic observations, we can uncover not only which features the agent elects to base decisions on, but also how it chooses to compile more complex, relational features from simpler ones. We first show that performance depends in large part on the ability to compile new compound features, rather than mere focus on individual features. In line with neuroscience predictions, self-attention leads to high resiliency to noise (irrelevant features) compared to other benchmark models. Finally, we highlight the importance and separate contributions of both bottom-up and top-down attention in the learning process. Together, these results demonstrate the broader validity of the AWRL framework in complex task scenarios, and illustrate the benefits of a deeper integration between neuroscience-derived models and RL for decision making in machine learning.

Electrolyte-gated transistors for enhanced performance bioelectronics

Electrolyte-gated transistors (EGTs), capable of transducing biological and biochemical inputs into amplified electronic signals and stably operating in aqueous environments, have emerged as fundamental building blocks in bioelectronics. In this Primer, the different EGT architectures are described with the fundamental mechanisms underpinning their functional operation, providing insight into key experiments including necessary data analysis and validation. Several organic and inorganic materials used in the EGT structures and the different fabrication approaches for an optimal experimental design are presented and compared. The functional bio-layers and/or biosystems integrated into or interfaced to EGTs, including self-organization and self-assembly strategies, are reviewed. Relevant and promising applications are discussed, including two-dimensional and three-dimensional cell monitoring, ultra-sensitive biosensors, electrophysiology, synaptic and neuromorphic bio-interfaces, prosthetics and robotics. Advantages, limitations and possible optimizations are also surveyed. Finally, current issues and future directions for further developments and applications are discussed.

Neurophysiology of slip sensation and grip reaction: insights for hand prosthesis control of slippage

Sensory feedback is pivotal for a proficient dexterity of the hand. By modulating the grip force in function of the quick and not completely predictable change of the load force, grabbed objects are prevented to slip from the hand. Slippage control is an enabling achievement to all manipulation abilities. However, in hand prosthetics, the performance of even the most innovative research solutions proposed so far to control slippage remain distant from the human physiology. Indeed, slippage control involves parallel and compensatory activation of multiple mechanoceptors, spinal and supraspinal reflexes, and higher-order voluntary behavioral adjustments. In this work, we reviewed the literature on physiological correlates of slippage to propose a three-phases model for the slip sensation and reaction. Furthermore, we discuss the main strategies employed so far in the research studies that tried to restore slippage control in amputees. In the light of the proposed three-phase slippage model and from the weaknesses of already implemented solutions, we proposed several physiology-inspired solutions for slippage control to be implemented in the future hand prostheses. Understanding the physiological basis of slip detection and perception and implementing them in novel hand feedback system would make prosthesis manipulation more efficient and would boost its perceived naturalness, fostering the sense of agency for the hand movements.

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.

Integration of vestibular and hindlimb inputs by vestibular nucleus neurons: multisensory influences on postural control

We recently demonstrated in decerebrate and conscious cat preparations that hindlimb somatosensory inputs converge with vestibular afferent input onto neurons in multiple central nervous system (CNS) locations that participate in balance control. Although it is known that head position and limb state modulate postural reflexes, presumably through vestibulospinal and reticulospinal pathways, the combined influence of the two inputs on the activity of neurons in these brainstem regions is unknown. In the present study, we evaluated the responses of vestibular nucleus (VN) neurons to vestibular and hindlimb stimuli delivered separately and together in conscious cats. We hypothesized that VN neuronal firing during activation of vestibular and limb proprioceptive inputs would be well fit by an additive model. Extracellular single-unit recordings were obtained from VN neurons. Sinusoidal whole body rotation in the roll plane was used as the search stimulus. Units responding to the search stimulus were tested for their responses to 10° ramp-and-hold roll body rotation, 60° extension hindlimb movement, and both movements delivered simultaneously. Composite response histograms were fit by a model of low- and high-pass filtered limb and body position signals using least squares nonlinear regression. We found that VN neuronal activity during combined vestibular and hindlimb proprioceptive stimulation in the conscious cat is well fit by a simple additive model for signals with similar temporal dynamics. The mean R² value for goodness of fit across all units was 0.74 ± 0.17. It is likely that VN neurons that exhibit these integrative properties participate in adjusting vestibulospinal outflow in response to limb state.NEW & NOTEWORTHY Vestibular nucleus neurons receive convergent information from hindlimb somatosensory inputs and vestibular inputs. In this study, extracellular single-unit recordings of vestibular nucleus neurons during conditions of passively applied limb movement, passive whole body rotations, and combined stimulation were well fit by an additive model. The integration of hindlimb somatosensory inputs with vestibular inputs at the first stage of vestibular processing suggests that vestibular nucleus neurons account for limb position in determining vestibulospinal responses to postural perturbations.

Touch inhibits touch: sanshool-induced paradoxical tingling reveals perceptual interaction between somatosensory submodalities

Human perception of touch is mediated by inputs from multiple channels. Classical theories postulate independent contributions of each channel to each tactile feature, with little or no interaction between channels. In contrast to this view, we show that inputs from two sub-modalities of mechanical input channels interact to determine tactile perception. The flutter-range vibration channel was activated anomalously using hydroxy-α-sanshool, a bioactive compound of Szechuan pepper, which chemically induces vibration-like tingling sensations. We tested whether this tingling sensation on the lips was modulated by sustained mechanical pressure. Across four experiments, we show that sustained touch inhibits sanshool tingling sensations in a location-specific, pressure-level and time-dependent manner. Additional experiments ruled out the mediation of this interaction by nociceptive or affective (C-tactile) channels. These results reveal novel inhibitory influence from steady pressure onto flutter-range tactile perceptual channels, consistent with early-stage interactions between mechanoreceptor inputs within the somatosensory pathway.

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.

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.

Functional organization and connectivity of the dorsal column nuclei complex reveals a sensorimotor integration and distribution hub

The dorsal column nuclei complex (DCN-complex) includes the dorsal column nuclei (DCN, referring to the gracile and cuneate nuclei collectively), external cuneate, X, and Z nuclei, and the median accessory nucleus. The DCN are organized by both somatotopy and modality, and have a diverse range of afferent inputs and projection targets. The functional organization and connectivity of the DCN implicate them in a variety of sensorimotor functions, beyond their commonly accepted role in processing and transmitting somatosensory information to the thalamus, yet this is largely underappreciated in the literature. To consolidate insights into their sensorimotor functions, this review examines the morphology, organization, and connectivity of the DCN and their associated nuclei. First, we briefly discuss the receptors, afferent fibers, and pathways involved in conveying tactile and proprioceptive information to the DCN. Next, we review the modality and somatotopic arrangements of the remaining constituents of the DCN-complex. Finally, we examine and discuss the functional implications of the myriad of DCN-complex projection targets throughout the diencephalon, midbrain, and hindbrain, in addition to their modulatory inputs from the cortex. The organization and connectivity of the DCN-complex suggest that these nuclei should be considered a complex integration and distribution hub for sensorimotor information.

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high-density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

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

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

Restoring Somatosensation: Advantages and Current Limitations of Targeting the Brainstem Dorsal Column Nuclei Complex

Current neural prostheses can restore limb movement to tetraplegic patients by translating brain signals coding movements to control a variety of actuators. Fast and accurate somatosensory feedback is essential for normal movement, particularly dexterous tasks, but is currently lacking in motor neural prostheses. Attempts to restore somatosensory feedback have largely focused on cortical stimulation which, thus far, have succeeded in eliciting minimal naturalistic sensations. Yet, a question that deserves more attention is whether the cortex is the best place to activate the central nervous system to restore somatosensation. Here, we propose that the brainstem dorsal column nuclei are an ideal alternative target to restore somatosensation. We review some of the recent literature investigating the dorsal column nuclei functional organization and neurophysiology and highlight some of the advantages and limitations of the dorsal column nuclei as a future neural prosthetic target. Recent evidence supports the dorsal column nuclei as a potential neural prosthetic target, but also identifies several gaps in our knowledge as well as potential limitations which need to be addressed before such a goal can become reality.

A Digital Hardware System for Spiking Network of Tactile Afferents

In the present research, we explore the possibility of utilizing a hardware-based neuromorphic approach to develop a tactile sensory system at the level of first-order afferents, which are slowly adapting type 1 (SA-I) and fast adapting type 1 (FA-I) afferents. Four spiking models are used to mimic neural signals of both SA-I and FA-I primary afferents. Next, a digital circuit is designed for each spiking model for both afferents to be implemented on the field-programmable gate array (FPGA). The four different digital circuits are then compared from source utilization point of view to find the minimum cost circuit for creating a population of digital afferents. In this way, the firing responses of both SA-I and FA-I afferents are physically measured in hardware. Finally, a population of 243 afferents consisting of 90 SA-I and 153 FA-I digital neuromorphic circuits are implemented on the FPGA. The FPGA also receives nine inputs from the force sensors through an interfacing board. Therefore, the data of multiple inputs are processed by the spiking network of tactile afferents, simultaneously. Benefiting from parallel processing capabilities of FPGA, the proposed architecture offers a low-cost neuromorphic structure for tactile information processing. Applying machine learning algorithms on the artificial spiking patterns collected from FPGA, we successfully classified three different objects based on the firing rate paradigm. Consequently, the proposed neuromorphic system provides the opportunity for development of new tactile processing component for robotic and prosthetic applications.

Somatosensory Cortex Efficiently Processes Touch Located Beyond the Body

The extent to which a tool is an extension of its user is a question that has fascinated writers and philosophers for centuries [1]. Despite two decades of research [2-7], it remains unknown how this could be instantiated at the neural level. To this aim, the present study combined behavior, electrophysiology and neuronal modeling to characterize how the human brain could treat a tool like an extended sensory "organ." As with the body, participants localize touches on a hand-held tool with near-perfect accuracy [7]. This behavior is owed to the ability of the somatosensory system to rapidly and efficiently use the tool as a tactile extension of the body. Using electroencephalography (EEG), we found that where a hand-held tool was touched was immediately coded in the neural dynamics of primary somatosensory and posterior parietal cortices of healthy participants. We found similar neural responses in a proprioceptively deafferented patient with spared touch perception, suggesting that location information is extracted from the rod's vibrational patterns. Simulations of mechanoreceptor responses [8] suggested that the speed at which these patterns are processed is highly efficient. A second EEG experiment showed that touches on the tool and arm surfaces were localized by similar stages of cortical processing. Multivariate decoding algorithms and cortical source reconstruction provided further evidence that early limb-based processes were repurposed to map touch on a tool. We propose that an elementary strategy the human brain uses to sense with tools is to recruit primary somatosensory dynamics otherwise devoted to the body.

Global Tactile Coding in Rat Barrel Cortex in the Absence of Local Cues

Although whisker-related perception is based predominantly on local, near-instantaneous coding, global, intensive coding, which integrates the vibrotactile signal over time, has also been shown to play a role given appropriate behavioral conditions. Here, we study global coding in isolation by studying head-fixed rats that identified pulsatile stimuli differing in pulse frequency but not in pulse waveforms, thus abolishing perception based on local coding. We quantified time locking and spike counts as likely variables underpinning the 2 coding schemes. Both neurometric variables contained substantial stimulus information, carried even by spikes of single barrel cortex neurons. To elucidate which type of information is actually used by the rats, we systematically compared psychometric with neurometric sensitivity based on the 2 coding schemes. Neurometric performance was calculated by using a population-encoding model incorporating the properties of our recorded neuron sample. We found that sensitivity calculated from spike counts sampled over long periods (>1 s) matched the performance of rats better than the one carried by spikes time-locked to the stimulus. We conclude that spike counts are more relevant to tactile perception when instantaneous kinematic parameters are not available.

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.

Peripheral Nerve Activation Evokes Machine-Learnable Signals in the Dorsal Column Nuclei

The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.

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.

Neural Basis of Touch and Proprioception in Primate Cortex

The sense of proprioception allows us to keep track of our limb posture and movements and the sense of touch provides us with information about objects with which we come into contact. In both senses, mechanoreceptors convert the deformation of tissues-skin, muscles, tendons, ligaments, or joints-into neural signals. Tactile and proprioceptive signals are then relayed by the peripheral nerves to the central nervous system, where they are processed to give rise to percepts of objects and of the state of our body. In this review, we first examine briefly the receptors that mediate touch and proprioception, their associated nerve fibers, and pathways they follow to the cerebral cortex. We then provide an overview of the different cortical areas that process tactile and proprioceptive information. Next, we discuss how various features of objects-their shape, motion, and texture, for example-are encoded in the various cortical fields, and the susceptibility of these neural codes to attention and other forms of higher-order modulation. Finally, we summarize recent efforts to restore the senses of touch and proprioception by electrically stimulating somatosensory cortex. © 2018 American Physiological Society. Compr Physiol 8:1575-1602, 2018.

Tactile learning transfer from the hand to the face but not to the forearm implies a special hand-face relationship

In the primary somatosensory cortex, large-scale cortical and perceptual changes have been demonstrated following input deprivation. Recently, we found that the cortical and perceptual changes induced by repetitive somatosensory stimulation (RSS) at a finger transfer to the face. However, whether such cross-border changes are specific to the face remains elusive. Here, we investigated whether RSS-induced acuity changes at the finger can also transfer to the forearm, which is the body part represented on the other side of the hand representation. Our results confirmed the transfer of tactile learning from the stimulated finger to the lip, but no significant changes were observed at the forearm. A second experiment revealed that the same regions on the forearm exhibited improved tactile acuity when RSS was applied there, excluding the possibility of low plastic ability at the arm representation. This provides also the first evidence that RSS can be efficient on body parts other than the hand. These results suggest that RSS-induced tactile learning transfers preferentially from the hand to the face rather than to the forearm. This specificity could arise from a stronger functional connectivity between the cortical hand and face representations, reflecting a fundamental coupling between these body parts.

Intracellular Dynamics in Cuneate Nucleus Neurons Support Self-Stabilizing Learning of Generalizable Tactile Representations

How the brain represents the external world is an unresolved issue for neuroscience, which could provide fundamental insights into brain circuitry operation and solutions for artificial intelligence and robotics. The neurons of the cuneate nucleus form the first interface for the sense of touch in the brain. They were previously shown to have a highly skewed synaptic weight distribution for tactile primary afferent inputs, suggesting that their connectivity is strongly shaped by learning. Here we first characterized the intracellular dynamics and inhibitory synaptic inputs of cuneate neurons in vivo and modeled their integration of tactile sensory inputs. We then replaced the tactile inputs with input from a sensorized bionic fingertip and modeled the learning-induced representations that emerged from varied sensory experiences. The model reproduced both the intrinsic membrane dynamics and the synaptic weight distributions observed in cuneate neurons in vivo. In terms of higher level model properties, individual cuneate neurons learnt to identify specific sets of correlated sensors, which at the population level resulted in a decomposition of the sensor space into its recurring high-dimensional components. Such vector components could be applied to identify both past and novel sensory experiences and likely correspond to the fundamental haptic input features these neurons encode in vivo. In addition, we show that the cuneate learning architecture is robust to a wide range of intrinsic parameter settings due to the neuronal intrinsic dynamics. Therefore, the architecture is a potentially generic solution for forming versatile representations of the external world in different sensor systems.

Temporal Patterns of Individual Neuronal Firing in Rat Dorsal Column Nuclei Provide Information Required for Somatosensory Discrimination

A wealth of mechanical information from the body generates various forms of sensory experience during touch or kinesthesia. Dorsal column nuclei (DCN) in the medulla are the first relay station for somatosensory inputs from peripheral receptors. These nuclei integrate somatosensory information and send the output to higher-order centers; therefore, investigating the firing patterns of DCN neurons should elucidate coding principles within the somatosensory system. In this study, we quantified the firing patterns of DCN neurons and examined whether the firing patterns of particular neurons are altered when moving tactile stimuli are applied in different directions. The activities of 17 neurons in the DCN of anesthetized rats were selected and their firing patterns were analyzed using LvR, which refers to the local variation of intervals of action potentials (i.e., the cross-correlation between consecutive intervals of action potentials) compensated by the refractoriness constant, R. The LvR of the 17 neurons ranged widely from 0.35 to 2.28. Of the 17 neurons, 12 responded to hair deflection (hair neurons), whereas five responded specifically to movement of forelimb joints. In 11 of 12 hair neurons, moving stimuli were applied in two to four different directions, which yielded 25 pairs of comparisons. Of these, 14 pairs (56%) showed significant differences in LvR. Among these 14 pairs, the range of LvR fluctuation was 0.13 ± 0.06 (mean ± standard deviation) and its effect size (Cohen's d) was 0.6 ± 0.2. These results suggest that the firing pattern of individual DCN neurons may contribute to somatosensory discrimination.

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.

Neuromorphic Vibrotactile Stimulation of Fingertips for Encoding Object Stiffness in Telepresence Sensory Substitution and Augmentation Applications

We present a tactile telepresence system for real-time transmission of information about object stiffness to the human fingertips. Experimental tests were performed across two laboratories (Italy and Ireland). In the Italian laboratory, a mechatronic sensing platform indented different rubber samples. Information about rubber stiffness was converted into on-off events using a neuronal spiking model and sent to a vibrotactile glove in the Irish laboratory. Participants discriminated the variation of the stiffness of stimuli according to a two-alternative forced choice protocol. Stiffness discrimination was based on the variation of the temporal pattern of spikes generated during the indentation of the rubber samples. The results suggest that vibrotactile stimulation can effectively simulate surface stiffness when using neuronal spiking models to trigger vibrations in the haptic interface. Specifically, fractional variations of stiffness down to 0.67 were significantly discriminated with the developed neuromorphic haptic interface. This is a performance comparable, though slightly worse, to the threshold obtained in a benchmark experiment evaluating the same set of stimuli naturally with the own hand. Our paper presents a bioinspired method for delivering sensory feedback about object properties to human skin based on contingency-mimetic neuronal models, and can be useful for the design of high performance haptic devices.

Methodological considerations for a chronic neural interface with the cuneate nucleus of macaques

While the response properties of neurons in the somatosensory nerves and anterior parietal cortex have been extensively studied, little is known about the encoding of tactile and proprioceptive information in the cuneate nucleus (CN) or external cuneate nucleus (ECN), the first recipients of upper limb somatosensory afferent signals. The major challenge in characterizing neural coding in CN/ECN has been to record from these tiny, difficult-to-access brain stem structures. Most previous investigations of CN response properties have been carried out in decerebrate or anesthetized animals, thereby eliminating the well-documented top-down signals from cortex, which likely exert a strong influence on CN responses. Seeking to fill this gap in our understanding of somatosensory processing, we describe an approach to chronically implanting arrays of electrodes in the upper limb representation in the brain stem in primates. First, we describe the topography of CN/ECN in rhesus macaques, including its somatotopic organization and the layout of its submodalities (touch and proprioception). Second, we describe the design of electrode arrays and the implantation strategy to obtain stable recordings. Third, we show sample responses of CN/ECN neurons in brain stem obtained from awake, behaving monkeys. With this method, we are in a position to characterize, for the first time, somatosensory representations in CN and ECN of primates.NEW & NOTEWORTHY In primates, the neural basis of touch and of our sense of limb posture and movements has been studied in the peripheral nerves and in somatosensory cortex, but coding in the cuneate and external cuneate nuclei, the first processing stage for these signals in the central nervous system, remains an enigma. We have developed a method to record from these nuclei, thereby paving the way to studying how sensory information from the limb is encoded there.

Short-latency muscle response patterns to multi-directional, unpredictable perturbations to balance applied to the arm are context dependent

A number of studies have shown that sensory inputs from the hand can have a profound effect in stabilizing upright posture. This suggests that the central nervous system can extract information about body motion and external forces acting on the body from cutaneous sensory signals. We have recently shown that the central nervous system determines the direction of an unpredictable force applied to the hand so rapidly that it is able to activate ankle muscles in advance of the perturbing effect that this force has at the ankles. In this study we investigate whether this rapid change in activation of lower limb muscles is an invariant response determined by the pattern of somatosensory information arising from sensory receptors in the hand or whether it adapts to changes in postural stability. We manipulated lateral stability of upright stance by changing stance width which had no effect on the activation of upper limb muscles or hand kinematics, but produced profound changes in the activation patterns of lower limb muscles when perturbations were in the medial/lateral direction without affecting the activation patterns of muscles when perturbations were in the anterior/posterior direction.

Spike Timing Matters in Novel Neuronal Code Involved in Vibrotactile Frequency Perception

Skin vibrations sensed by tactile receptors contribute significantly to the perception of object properties during tactile exploration [1-4] and to sensorimotor control during object manipulation [5]. Sustained low-frequency skin vibration (<60 Hz) evokes a distinct tactile sensation referred to as flutter whose frequency can be clearly perceived [6]. How afferent spiking activity translates into the perception of frequency is still unknown. Measures based on mean spike rates of neurons in the primary somatosensory cortex are sufficient to explain performance in some frequency discrimination tasks [7-11]; however, there is emerging evidence that stimuli can be distinguished based also on temporal features of neural activity [12, 13]. Our study's advance is to demonstrate that temporal features are fundamental for vibrotactile frequency perception. Pulsatile mechanical stimuli were used to elicit specified temporal spike train patterns in tactile afferents, and subsequently psychophysical methods were employed to characterize human frequency perception. Remarkably, the most salient temporal feature determining vibrotactile frequency was not the underlying periodicity but, rather, the duration of the silent gap between successive bursts of neural activity. This burst gap code for frequency represents a previously unknown form of neural coding in the tactile sensory system, which parallels auditory pitch perception mechanisms based on purely temporal information where longer inter-pulse intervals receive higher perceptual weights than short intervals [14]. Our study also demonstrates that human perception of stimuli can be determined exclusively by temporal features of spike trains independent of the mean spike rate and without contribution from population response factors.

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.

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.

Cerebellar Synaptic Plasticity and the Credit Assignment Problem

The mechanism by which a learnt synaptic weight change can contribute to learning or adaptation of brain function is a type of credit assignment problem, which is a key issue for many parts of the brain. In the cerebellum, detailed knowledge not only of the local circuitry connectivity but also of the topography of different sources of afferent/external information makes this problem particularly tractable. In addition, multiple forms of synaptic plasticity and their general rules of induction have been identified. In this review, we will discuss the possible roles of synaptic and cellular plasticity at specific locations in contributing to behavioral changes. Focus will be on the parts of the cerebellum that are devoted to limb control, which constitute a large proportion of the cortex and where the knowledge of the external connectivity is particularly well known. From this perspective, a number of sites of synaptic plasticity appear to primarily have the function of balancing the overall level of activity in the cerebellar circuitry, whereas the locations at which synaptic plasticity leads to functional changes in terms of limb control are more limited. Specifically, the postsynaptic forms of long-term potentiation (LTP) and long-term depression (LTD) at the parallel fiber synapses made on interneurons and Purkinje cells, respectively, are the types of plasticity that mediate the widest associative capacity and the tightest link between the synaptic change and the external functions that are to be controlled.

Representation of Afferent Signals from Forearm Muscle and Cutaneous Nerves in the Primary Somatosensory Cortex of the Macaque Monkey

Proprioception is one's overall sense of the relative positions and movements of the various parts of one's body. The primary somatosensory cortex (SI) is involved in generating the proprioception by receiving peripheral sensory inputs from both cutaneous and muscle afferents. In particular, area 3a receives input from muscle afferents and areas 3b and 1 from cutaneous afferents. However, segregation of two sensory inputs to these cortical areas has not been evaluated quantitatively because of methodological difficulties in distinguishing the incoming signals. To overcome this, we applied electrical stimulation separately to two forearm nerves innervating muscle (deep radial nerve) and skin (superficial radial nerve), and examined the spatiotemporal distribution of sensory evoked potentials (SEPs) in SI of anaesthetized macaques. The SEPs arising from the deep radial nerve were observed exclusively at the bottom of central sulcus (CS), which was identified as area 3a using histological reconstruction. In contrast, SEPs evoked by stimulation of the superficial radial nerve were observed in the superficial part of SI, identified as areas 3b and 1. In addition to these earlier, larger potentials, we also found small and slightly delayed SEPs evoked by cutaneous nerve stimulation in area 3a. Coexistence of the SEPs from both deep and superficial radial nerves suggests that area 3a could integrate muscle and cutaneous signals to shape proprioception.

Cerebellar physiology: links between microcircuitry properties and sensorimotor functions

Existing knowledge of the cerebellar microcircuitry structure and physiology allows a rather detailed description of what it in itself can and cannot do. Combined with a known mapping of different cerebellar regions to afferent systems and motor output target structures, there are several constraints that can be used to describe how specific components of the cerebellar microcircuitry may work during sensorimotor control. In fact, as described in this review, the major factor that hampers further progress in understanding cerebellar function is the limited insights into the circuitry-level function of the targeted motor output systems and the nature of the information in the mossy fiber afferents. The cerebellar circuitry in itself is here summarized as a gigantic associative memory element, primarily consisting of the parallel fiber synapses, whereas most other circuitry components, including the climbing fiber system, primarily has the role of maintaining activity balance in the intracerebellar and extracerebellar circuitry. The review explores the consistency of this novel interpretational framework with multiple diverse observations at the synaptic and microcircuitry level within the cerebellum.

The brain uses extrasomatic information to estimate limb displacement

A fundamental problem faced by the brain is to estimate whether a touched object is rigidly attached to a ground reference or is movable. A simple solution to this problem would be for the brain to test whether pushing on the object with a limb is accompanied by limb displacement. The mere act of pushing excites large populations of mechanoreceptors, generating a sensory response that is only weakly sensitive to limb displacement if the movements are small, and thus can hardly be used to determine the mobility of the object. In the mechanical world, displacement or deformation of objects frequently co-occurs with microscopic fluctuations associated with the frictional sliding of surfaces in contact or with micro-failures inside an object. In this study,we provide compelling evidence that the brain relies on these microscopic mechanical events to estimate the displacement of the limb in contact with an object, and hence the mobility of the touched object. We show that when pressing with a finger on a stiff surface, fluctuations that resemble the mechanical response of granular solids provoke a sensation of limb displacement. Our findings suggest that when acting on an external object, prior knowledge about the sensory consequences of interacting with the object contributes to proprioception.

Parallel Transformation of Tactile Signals in Central Circuits of Drosophila

To distinguish between complex somatosensory stimuli, central circuits must combine signals from multiple peripheral mechanoreceptor types, as well as mechanoreceptors at different sites in the body. Here, we investigate the first stages of somatosensory integration in Drosophila using in vivo recordings from genetically labeled central neurons in combination with mechanical and optogenetic stimulation of specific mechanoreceptor types. We identify three classes of central neurons that process touch: one compares touch signals on different parts of the same limb, one compares touch signals on right and left limbs, and the third compares touch and proprioceptive signals. Each class encodes distinct features of somatosensory stimuli. The axon of an individual touch receptor neuron can diverge to synapse onto all three classes, meaning that these computations occur in parallel, not hierarchically. Representing a stimulus as a set of parallel comparisons is a fast and efficient way to deliver somatosensory signals to motor circuits.