Express Visuomotor Responses Reflect Knowledge of Both Target Locations and Contextual Rules during Reaches of Different Amplitudes

When humans reach to visual targets, extremely rapid (∼90 ms) target-directed responses can be observed in task-relevant proximal muscles. Such express visuomotor responses are inflexibly locked in time and space to the target and have been proposed to reflect rapid visuomotor transformations conveyed subcortically via the tecto-reticulo-spinal pathway. Previously, we showed that express visuomotor responses are sensitive to explicit cue-driven information about the target, suggesting that the express pathway can be modulated by cortical signals affording contextual prestimulus expectations. Here, we show that the express visuomotor system incorporates information about the physical hand-to-target distance and contextual rules during visuospatial tasks requiring different movement amplitudes. In one experiment, we recorded the activity from two shoulder muscles as 14 participants (6 females) reached toward targets that appeared at different distances from the reaching hand. Increasing the reaching distance facilitated the generation of frequent and large express visuomotor responses. This suggests that both the direction and amplitude of veridical hand-to-target reaches are encoded along the putative subcortical express pathway. In a second experiment, we modulated the movement amplitude by asking 12 participants (4 females) to deliberately undershoot, overshoot, or stop (control) at the target. The overshoot and undershoot tasks impaired the generation of large and frequent express visuomotor responses, consistent with the inability of the express pathway to generate responses directed toward nonveridical targets as in the anti-reach task. Our findings appear to reflect strategic, cortically driven modulation of the express visuomotor circuit to facilitate rapid and effective response initiation during target-directed actions.SIGNIFICANCE STATEMENT Express (∼90 ms) arm muscle responses that are consistently tuned toward the location of visual stimuli suggest a subcortical contribution to target-directed visuomotor behavior in humans, potentially via the tecto-reticulo-spinal pathway. Here, we show that express muscle responses are modulated appropriately to reach targets at different distances, but generally suppressed when the task required nonveridical responses to overshoot/undershoot the real target. This suggests that the tecto-reticulo-spinal pathway can be exploited strategically by the cerebral cortex to facilitate rapid initiation of effective responses during a visuospatial task.

A hierarchical sensorimotor control framework for human-in-the-loop robotic hands

Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands.

Remembrance of things perceived: Adding thalamocortical function to artificial neural networks

Recent research has illuminated the complexity and importance of the thalamocortical system but it has been difficult to identify what computational functions it performs. Meanwhile, deep-learning artificial neural networks (ANNs) based on bio-inspired models of purely cortical circuits have achieved surprising success solving sophisticated cognitive problems associated historically with human intelligence. Nevertheless, the limitations and shortcomings of artificial intelligence (AI) based on such ANNs are becoming increasingly clear. This review considers how the addition of thalamocortical connectivity and its putative functions related to cortical attention might address some of those shortcomings. Such bio-inspired models are now providing both testable theories of biological cognition and improved AI technology, much of which is happening outside the usual academic venues.

Symbolic cues enhance express visuomotor responses in human arm muscles at the motor planning rather than the visuospatial processing stage

Humans can produce "express" (~100ms) arm muscle responses that are inflexibly locked in time and space to visual target presentations, consistent with subcortical visuomotor transformations via the tecto-reticulo-spinal pathway. These express visuomotor responses are sensitive to explicit cue-driven expectations, but it is unclear at what stage of sensory-to-motor transformation such modulation occurs. Here, we recorded electromyographic activity from shoulder muscles as participants reached toward one of four virtual targets whose physical location was partially predictable from a symbolic cue. In an experiment in which targets could be veridically reached, express responses were inclusive of the biomechanical requirements for reaching the cued locations and not systematically modulated by cue validity. In a second experiment, movements were restricted to the horizontal plane so that the participants could perform only rightward or leftward reaches, irrespective of target position on the vertical axis. Express muscle responses were almost identical for targets that were validly cued in the horizontal direction, regardless of cue validity in the vertical dimension. Together, these findings suggest that the cue-induced enhancements of express responses are dominated by effects at the level of motor plans and not solely via facilitation of early visuospatial target processing. Notably, direct cortico-tectal and cortico-reticular projections exist that are well-placed to modulate pre-stimulus motor preparation state in subcortical circuits. Our results could reflect a neural mechanism by which contextually relevant motor responses to compatible visual inputs are rapidly released via subcortical circuits that are sufficiently along the sensory- to-motor continuum.

Developing Intelligent Robots that Grasp Affordance

Humans and robots operating in unstructured environments both need to classify objects through haptic exploration and use them in various tasks, but currently they differ greatly in their strategies for acquiring such capabilities. This review explores nascent technologies that promise more convergence. A novel form of artificial intelligence classifies objects according to sensory percepts during active exploration and decides on efficient sequences of exploratory actions to identify objects. Representing objects according to the collective experience of manipulating them provides a substrate for discovering causality and affordances. Such concepts that generalize beyond explicit training experiences are an important aspect of human intelligence that has eluded robots. For robots to acquire such knowledge, they will need an extended period of active exploration and manipulation similar to that employed by infants. The efficacy, efficiency and safety of such behaviors depends on achieving smooth transitions between movements that change quickly from exploratory to executive to reflexive. Animals achieve such smoothness by using a hierarchical control scheme that is fundamentally different from those of conventional robotics. The lowest level of that hierarchy, the spinal cord, starts to self-organize during spontaneous movements in the fetus. This allows its connectivity to reflect the mechanics of the musculoskeletal plant, a bio-inspired process that could be used to adapt spinal-like middleware for robots. Implementation of these extended and essential stages of fetal and infant development is impractical, however, for mechatronic hardware that does not heal and replace itself like biological tissues. Instead such development can now be accomplished in silico and then cloned into physical robots, a strategy that could transcend human performance.

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.

A Model for Self-Organization of Sensorimotor Function: The Spinal Monosynaptic Loop

Recent spinal cord literature abounds with descriptions of genetic preprogramming and the molecular control of circuit formation. In this paper, we explore to what extent circuit formation based on learning rather than preprogramming could explain the selective formation of the monosynaptic projections between muscle spindle primary afferents and homonymous motoneurons. We adjusted the initially randomized gains in the neural network according to a Hebbian plasticity rule while exercising the model system with spontaneous muscle activity patterns similar to those observed during early fetal development. Normal connectivity patterns developed only when we modeled β motoneurons, which are known to innervate both intrafusal and extrafusal muscle fibers in vertebrate muscles but were not considered in previous literature regarding selective formation of these synapses in animals with paralyzed muscles. It was also helpful to correctly model the greatly reduced contractility of extrafusal muscle fibers during early development. Stronger and more coordinated muscle activity patterns such as observed later during neonatal locomotion impaired projection selectivity. These findings imply a generic functionality of a musculoskeletal system to imprint important aspects of its mechanical dynamics onto a neural network, without specific preprogramming other than setting a critical period for the formation and maturation of this general pattern of connectivity. Such functionality would facilitate the successful evolution of new species with altered musculoskeletal anatomy, and it may help to explain patterns of connectivity and associated reflexes that appear during abnormal development.

NEW & NOTEWORTHY A novel model of self-organization of early spinal circuitry based on a biologically realistic plant, sensors, and neuronal plasticity in conjunction with empirical observations of fetal development. Without explicit need for guiding genetic rules, connection matrices emerge that support functional self-organization of the mature pattern of Ia to motoneuron connectivity in the spinal circuitry.

Trial-by-trial modulation of express visuomotor responses induced by symbolic or barely detectable cues

Human cerebral cortex can produce visuomotor responses that are modulated by contextual and task-specific constraints. However, the distributed cortical network for visuomotor transformations limits the minimal response time of that pathway. Notably, humans can generate express visuomotor responses in arm muscles that are inflexibly tuned to the target location and occur 80-120 ms from stimulus presentation [stimulus-locked responses (SLRs)]. This suggests a subcortical pathway for visuomotor transformations that might involve the superior colliculus and its downstream reticulo-spinal projections. Here we investigated whether cognitive expectations can modulate the SLR. In one experiment, we recorded surface electromyogram (EMG) from shoulder muscles as participants reached toward a visual target whose location was unpredictable in control conditions and partially predictable in cue conditions by interpreting a symbolic cue (75% validity). Valid symbolic cues led to earlier and larger SLRs than control conditions; invalid symbolic cues produced later and smaller SLRs than control conditions. This is consistent with a cortical top-down modulation of the putative subcortical SLR network. In a second experiment, we presented high-contrast targets in isolation (control) or ∼24 ms after low-contrast stimuli, which could appear at the same (valid cue) or opposite (invalid cue) location as the target and with equal probability (50% cue validity). We observed earlier SLRs than control with the valid low-contrast cues, whereas the invalid cues led to the opposite results. These findings may reflect bottom-up attentional mechanisms, potentially evolving subcortically via the superior colliculus. Overall, our results support both top-down and bottom-up modulations of the putative subcortical SLR network in humans.NEW & NOTEWORTHY Express visuomotor responses in humans appear to reflect subcortical sensorimotor transformation of visual inputs, potentially conveyed via the tecto-reticulo-spinal pathway. Here we show that the express responses are influenced by both symbolic and barely detectable spatial cues about stimulus location. The symbolic cue-induced effects suggest cortical top-down modulation of the putative subcortical visuomotor network. The effects of barely detectable cues may reflect exogenous facilitation mechanisms of the tecto-reticulo-spinal pathway.

insideOut: A Bio-Inspired Machine Learning Approach to Estimating Posture in Robots Driven by Compliant Tendons

Estimates of limb posture are critical for controlling robotic systems. This is generally accomplished with angle sensors at individual joints that simplify control but can complicate mechanical design and robustness. Limb posture should be derivable from each joint's actuator shaft angle but this is problematic for compliant tendon-driven systems where (i) motors are not placed at the joints and (ii) nonlinear tendon stiffness decouples the relationship between motor and joint angles. Here we propose a novel machine learning algorithm to accurately estimate joint posture during dynamic tasks by limited training of an artificial neural network (ANN) receiving motor angles and tendon tensions, analogous to biological muscle and tendon mechanoreceptors. Simulating an inverted pendulum—antagonistically-driven by motors and nonlinearly-elastic tendons—we compare how accurately ANNs estimate joint angles when trained with different sets of non-collocated sensory information generated via random motor-babbling. Cross-validating with new movements, we find that ANNs trained with motor angles and tendon tension data predict joint angles more accurately than ANNs trained without tendon tension. Furthermore, these results are robust to changes in network/mechanical hyper-parameters. We conclude that regardless of the tendon properties, actuator behavior, or movement demands, tendon tension information invariably improves joint angle estimates from non-collocated sensory signals.

Turning Neural Prosthetics Into Viable Products

Academic researchers concentrate on the scientific and technological feasibility of novel treatments. Investors and commercial partners, however, understand that success depends even more on strategies for regulatory approval, reimbursement, marketing, intellectual property protection and risk management. These considerations are critical for technologically complex and highly invasive treatments that entail substantial costs and risks in small and heterogeneous patient populations. Most implanted neural prosthetic devices for novel applications will be in FDA Device Class III, for which guidance documents have been issued recently. Less invasive devices may be eligible for the recently simplified “de novo” submission routes. We discuss typical timelines and strategies for integrating the regulatory path with approval for reimbursement, securing intellectual property and funding the enterprise, particularly as they might apply to implantable brain-computer interfaces for sensorimotor disabilities that do not yet have a track record of approved products.

A Non-spiking Neuron Model With Dynamic Leak to Avoid Instability in Recurrent Networks

Recurrent circuitry components are distributed widely within the brain, including both excitatory and inhibitory synaptic connections. Recurrent neuronal networks have potential stability problems, perhaps a predisposition to epilepsy. More generally, instability risks making internal representations of information unreliable. To assess the inherent stability properties of such recurrent networks, we tested a linear summation, non-spiking neuron model with and without a “dynamic leak”, corresponding to the low-pass filtering of synaptic input current by the RC circuit of the biological membrane. We first show that the output of this neuron model, in either of its two forms, follows its input at a higher fidelity than a wide range of spiking neuron models across a range of input frequencies. Then we constructed fully connected recurrent networks with equal numbers of excitatory and inhibitory neurons and randomly distributed weights across all synapses. When the networks were driven by pseudorandom sensory inputs with varying frequency, the recurrent network activity tended to induce high frequency self-amplifying components, sometimes evident as distinct transients, which were not present in the input data. The addition of a dynamic leak based on known membrane properties consistently removed such spurious high frequency noise across all networks. Furthermore, we found that the neuron model with dynamic leak imparts a network stability that seamlessly scales with the size of the network, conduction delays, the input density of the sensory signal and a wide range of synaptic weight distributions. Our findings suggest that neuronal dynamic leak serves the beneficial function of protecting recurrent neuronal circuitry from the self-induction of spurious high frequency signals, thereby permitting the brain to utilize this architectural circuitry component regardless of network size or recurrency.

A new approach to medical diagnostic decision support

Data mining is a powerful tool to reduce costs and mitigate errors in the diagnostic analysis and repair of complex engineered system, but it has yet to be applied systematically to the most complex and socially expensive system - the human body. The currently available approaches of knowledge-based and pattern-based artificial intelligence are unsuited to the iterative and often subjective nature of clinician-patient interactions. Furthermore, current electronic health records generally have poor design and low quality for such data mining. Bayesian methods have been developed to suggest multiple possible diagnoses given a set of clinical findings, but the larger problem is advising the physician on useful next steps. A new approach based on inverting Bayesian inference allows identification of the diagnostic actions that are most likely to disambiguate a differential diagnosis at each point in a patient's work-up. This can be combined with personalized cost information to suggest a cost-effective path to the clinician. Because the software is tracking the clinician's decision-making process, it can provide salient suggestions for both diagnoses and diagnostic tests in standard, coded formats that need only to be selected. This would reduce the need to type in free text, which is prone to ambiguities, omissions and errors. As the database of high-quality records grows, the scope, utility and acceptance of the system should also grow automatically, without requiring expert updating or correction.

Force variability is mostly not motor noise: Theoretical implications for motor control

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume 'motor noise' leads to force variability and its 'signal dependence' (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by 'motor noise.' Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.

Shoulder kinematics plus contextual target information enable control of multiple distal joints of a simulated prosthetic arm and hand

Background

Prosthetic restoration of reach and grasp function after a trans-humeral amputation requires control of multiple distal degrees of freedom in elbow, wrist and fingers. However, such a high level of amputation reduces the amount of available myoelectric and kinematic information from the residual limb.

Methods

To overcome these limits, we added contextual information about the target’s location and orientation such as can now be extracted from gaze tracking by computer vision tools. For the task of picking and placing a bottle in various positions and orientations in a 3D virtual scene, we trained artificial neural networks to predict postures of an intact subject’s elbow, forearm and wrist (4 degrees of freedom) either solely from shoulder kinematics or with additional knowledge of the movement goal. Subjects then performed the same tasks in the virtual scene with distal joints predicted from the context-aware network.

Results

Average movement times of 1.22s were only slightly longer than the naturally controlled movements (0.82 s). When using a kinematic-only network, movement times were much longer (2.31s) and compensatory movements from trunk and shoulder were much larger. Integrating contextual information also gave rise to motor synergies closer to natural joint coordination.

Conclusions

Although notable challenges remain before applying the proposed control scheme to a real-world prosthesis, our study shows that adding contextual information to command signals greatly improves prediction of distal joint angles for prosthetic control.

Learning to use Muscles

The human musculoskeletal system is highly complex mechanically. Its neural control must deal successfully with this complexity to perform the diverse, efficient, robust and usually graceful behaviors of which humans are capable. Most of those behaviors might be performed by many different subsets of its myriad possible states, so how does the nervous system decide which subset to use? One solution that has received much attention over the past 50 years would be for the nervous system to be fundamentally limited in the patterns of muscle activation that it can access, a concept known as muscle synergies or movement primitives. Another solution, based on engineering control methodology, is for the nervous system to compute the single optimal pattern of muscle activation for each task according to a cost function. This review points out why neither appears to be the solution used by humans. There is a third solution that is based on trial-and-error learning, recall and interpolation of sensorimotor programs that are good-enough rather than limited or optimal. The solution set acquired by an individual during the protracted development of motor skills starting in infancy forms the basis of motor habits, which are inherently low-dimensional. Such habits give rise to muscle usage patterns that are consistent with synergies but do not reflect fundamental limitations of the nervous system and can be shaped by training or disability. This habit-based strategy provides a robust substrate for the control of new musculoskeletal structures during evolution as well as for efficient learning, athletic training and rehabilitation therapy.

The influence of temporal predictability on express visuomotor responses

Humans are able to generate target-directed visuomotor responses in less than 100 ms after stimulus onset. These "express" responses have been termed stimulus-locked responses (SLRs) and are proposed to be modulated by visuomotor transformations performed subcortically via the superior colliculus. Unfortunately, these responses have proven difficult to detect consistently across individuals. The recent report of an effective paradigm for generating SLRs in 100% of participants appears to change this. The task required the interception of a target moving at a constant velocity that emerged from behind a barrier. Here, we aimed to reproduce the efficacy of this paradigm for eliciting SLRs and to test the hypothesis that its effectiveness derives from the predictability of target onset time as opposed to target motion per se. In one experiment, we recorded surface electromyogram (EMG) from shoulder muscles as participants made reaches to intercept temporally predictable or unpredictable targets. Consistent with our hypothesis, predictably timed targets produced more frequent and stronger SLRs than unpredictably timed targets. In a second experiment, we compared different temporally predictable stimuli and observed that transiently presented targets produced larger and earlier SLRs than sustained moving targets. Our results suggest that target motion is not critical for facilitating the SLR expression and that timing predictability does not rely on extrapolation of a physically plausible motion trajectory. These findings provide support for a mechanism whereby an internal timer, probably located in cerebral cortex, primes the processing of both visual input and motor output within the superior colliculus to produce SLRs.NEW & NOTEWORTHY Express stimulus-driven responses in humans have been proposed to be originated subcortically via the superior colliculus. These short-latency responses are facilitated by the presentation of dynamic visual stimuli. Here, we show that this facilitation is related to the predictable target timing, regardless of its kinematic attributes. We propose that the superior colliculus can be primed to generate express stimulus-driven motor responses via cortical top-down projection.

Minimally Invasive Implantation of a Micropacemaker Into the Pericardial Space

BACKGROUND

Permanent cardiac pacemakers require invasive procedures with complications often related to long pacemaker leads. We are developing a percutaneous pacemaker for implantation of an entire pacing system into the pericardial space.

METHODS

Percutaneous micropacemaker implantations were performed in 6 pigs (27.4-34.1 kg) using subxyphoid access to the pericardial space. Modifications in the implantation methods and hardware were made after each experiment as the insertion method was optimized. In the first 5 animals, nonfunctional pacemaker devices were studied. In the final animal, a functional pacemaker was implanted.

RESULTS

Successful placement of the entire nonfunctional pacing system into the pericardial space was demonstrated in 2 of the first 5 animals, and successful implantation and capture was achieved using a functional system in the last animal. A sheath was developed that allows retractable features to secure positioning within the pericardial space. In addition, a miniaturized camera with fiberoptic illumination allowed visualization of the implantation site before electrode insertion into myocardium. All animals studied during follow-up survived without symptoms after the initial postoperative period.

CONCLUSIONS

A novel micropacemaker system allows cardiac pacing without entering the vascular space or surgical exposure of the heart. This pericardial pacemaker system may be an option for a large number of patients currently requiring transvenous pacemakers but is particularly relevant for patients with restricted vascular access, young children, or those with congenital heart disease who require epicardial access.

Prenatal diagnosis and management of congenital complete heart block

Congenital complete heart block (CCHB) is a life-threatening medical condition in the unborn fetus with insufficiently validated prenatal interventions. Maternal administration of medications aimed at decreasing the immune response in the fetus and beta-agonists intended to increase fetal cardiac output have shown only marginal benefits. Anti-inflammatory therapies cannot reverse CCHB, but may decrease myocarditis and improve heart function. Advances in prenatal diagnosis and use of strict surveillance protocols for delivery timing have demonstrated small improvements in morbidity and mortality. Ambulatory surveillance programs and wearable fetal heart rate monitors may afford early identification of evolving fetal heart block allowing for emergent treatment. There is also preliminary data suggesting a roll for prevention of CCHB with hydroxychloroquine, but the efficacy and safety is still being studied. To date, intrauterine fetal pacing has not been successful due to the high-risk invasive placement techniques and potential problems with lead dislodgement. The development of a fully implantable micropacemaker via a minimally invasive approach has the potential to pace fetal patients with CCHB and thus delay delivery and allow fetal hydrops to resolve. The challenge remains to establish accepted prenatal interventions capable of successfully managing CCHB in utero until postnatal pacemaker placement is successfully achieved.

Neural Prosthetics:A Review of Empirical vs. Systems Engineering Strategies

Implantable electrical interfaces with the nervous system were first enabled by cardiac pacemaker technology over 50 years ago and have since diverged into almost all of the physiological functions controlled by the nervous system. There have been a few major clinical and commercial successes, many contentious claims, and some outright failures. These tend to be reviewed within each clinical subspecialty, obscuring the many commonalities of neural control, biophysics, interface materials, electronic technologies, and medical device regulation that they share. This review cites a selection of foundational and recent journal articles and reviews for all major applications of neural prosthetic interfaces in clinical use, trials, or development. The hard-won knowledge and experience across all of these fields can now be amalgamated and distilled into more systematic processes for development of clinical products instead of the often empirical (trial and error) approaches to date. These include a frank assessment of a specific clinical problem, the state of its underlying science, the identification of feasible targets, the availability of suitable technologies, and the path to regulatory and reimbursement approval. Increasing commercial interest and investment facilitates this systematic approach, but it also motivates projects and products whose claims are dubious.

Relationships between full-day arm movement characteristics and developmental status in infants with typical development as they learn to reach: An observational study

Background: Advances in wearable sensor technology now allow us to quantify the number, type and kinematic characteristics of bouts of infant arm movement made across a full day in the natural environment. Our aim here was to determine whether the amount and kinematic characteristics of arm movements made across the day in the natural environment were related to developmental status in infants with typical development as they learned to reach for objects using their arms.

Methods: We used wearable sensors to measure arm movement across days and months as infants developed arm reaching skills. In total, 22 infants with typical development participated, aged between 38 and 203 days. Of the participants, 2 infants were measured once and the other 20 infants were measured once per month for 3 to 6 visits. The Bayley Scales of Infant Development was used to measure developmental level.

Results: Our main findings were: 1) infant arm movement characteristics as measured by full-day wearable sensor data were related to Bayley motor, cognitive and language scores, indicating a relationship between daily movement characteristics and developmental status; 2) infants who moved more had larger increases in language and cognitive scores across visits; and 3) larger changes in movement characteristics across visits were related to higher motor scores.

Conclusions: This was a preliminary, exploratory, small study of the potential importance of infant arm movement characteristics as measured by full-day wearable sensor data. Our results support full-day arm movement activity as an area of interest for future study as a biomarker of neurodevelopmental status and as a target for early intervention.

Relationships between full-day arm movement characteristics and developmental status in infants with typical development as they learn to reach: An observational study

Background: Advances in wearable sensor technology now allow us to quantify the number, type and kinematic characteristics of bouts of infant arm movement made across a full day in the natural environment. Our aim here was to determine whether the amount and kinematic characteristics of arm movements made across the day in the natural environment were related to developmental status in infants with typical development as they learned to reach for objects using their arms. Methods: We used wearable sensors to measure arm movement across days and months as infants developed arm reaching skills. In total, 22 infants with typical development participated, aged between 38 and 203 days. Of the participants, 2 infants were measured once and the other 20 infants were measured once per month for 3 to 6 visits. The Bayley Scales of Infant Development was used to measure developmental level. Results: Our main findings were: 1) infant arm movement characteristics as measured by full-day wearable sensor data were related to Bayley motor, cognitive and language scores, indicating a relationship between daily movement characteristics and developmental status; 2) infants who moved more had larger increases in language and cognitive scores across visits; and 3) larger changes in movement characteristics across visits were related to higher motor scores. Conclusions: This was a preliminary, exploratory, small study of the potential importance of infant arm movement characteristics as measured by full-day wearable sensor data. Our results support full-day arm movement activity as an area of interest for future study as a biomarker of neurodevelopmental status and as a target for early intervention.

Accelerated life-test methods and results for implantable electronic devices with adhesive encapsulation

We have developed and applied new methods to estimate the functional life of miniature, implantable, wireless electronic devices that rely on non-hermetic, adhesive encapsulants such as epoxy. A comb pattern board with a high density of interdigitated electrodes (IDE) could be used to detect incipient failure from water vapor condensation. Inductive coupling of an RF magnetic field was used to provide DC bias and to detect deterioration of an encapsulated comb pattern. Diodes in the implant converted part of the received energy into DC bias on the comb pattern. The capacitance of the comb pattern forms a resonant circuit with the inductor by which the implant receives power. Any moisture affects both the resonant frequency and the Q-factor of the resonance of the circuitry, which was detected wirelessly by its effects on the coupling between two orthogonal RF coils placed around the device. Various defects were introduced into the comb pattern devices to demonstrate sensitivity to failures and to correlate these signals with visual inspection of failures. Optimized encapsulation procedures were validated in accelerated life tests of both comb patterns and a functional neuromuscular stimulator under development. Strong adhesive bonding between epoxy and electronic circuitry proved to be necessary and sufficient to predict 1 year packaging reliability of 99.97% for the neuromuscular stimulator.

Muscle and Limb Mechanics

Understanding of the musculoskeletal system has evolved from the collection of individual phenomena in highly selected experimental preparations under highly controlled and often unphysiological conditions. At the systems level, it is now possible to construct complete and reasonably accurate models of the kinetics and energetics of realistic muscles and to combine them to understand the dynamics of complete musculoskeletal systems performing natural behaviors. At the reductionist level, it is possible to relate most of the individual phenomena to the anatomical structures and biochemical processes that account for them. Two large challenges remain. At a systems level, neuroscience must now account for how the nervous system learns to exploit the many complex features that evolution has incorporated into muscle and limb mechanics. At a reductionist level, medicine must now account for the many forms of pathology and disability that arise from the many diseases and injuries to which this highly evolved system is inevitably prone. © 2017 American Physiological Society. Compr Physiol 7:429-462, 2017.

Neuromorphic meets neuromechanics, part II: the role of fusimotor drive

OBJECTIVE

We studied the fundamentals of muscle afferentation by building a Neuro-mechano-morphic system actuating a cadaveric finger. This system is a faithful implementation of the stretch reflex circuitry. It allowed the systematic exploration of the effects of different fusimotor drives to the muscle spindle on the closed-loop stretch reflex response.

APPROACH

As in Part I of this work, sensory neurons conveyed proprioceptive information from muscle spindles (with static and dynamic fusimotor drive) to populations of α-motor neurons (with recruitment and rate coding properties). The motor commands were transformed into tendon forces by a Hill-type muscle model (with activation-contraction dynamics) via brushless DC motors. Two independent afferented muscles emulated the forces of flexor digitorum profundus and the extensor indicis proprius muscles, forming an antagonist pair at the metacarpophalangeal joint of a cadaveric index finger. We measured the physical response to repetitions of bi-directional ramp-and-hold rotational perturbations for 81 combinations of static and dynamic fusimotor drives, across four ramp velocities, and three levels of constant cortical drive to the α-motor neuron pool.

MAIN RESULTS

We found that this system produced responses compatible with the physiological literature. Fusimotor and cortical drives had nonlinear effects on the reflex forces. In particular, only cortical drive affected the sensitivity of reflex forces to static fusimotor drive. In contrast, both static fusimotor and cortical drives reduced the sensitivity to dynamic fusimotor drive. Interestingly, realistic signal-dependent motor noise emerged naturally in our system without having been explicitly modeled.

SIGNIFICANCE

We demonstrate that these fundamental features of spinal afferentation sufficed to produce muscle function. As such, our Neuro-mechano-morphic system is a viable platform to study the spinal mechanisms for healthy muscle function-and its pathologies such as dystonia and spasticity. In addition, it is a working prototype of a robust biomorphic controller for compliant robotic limbs and exoskeletons.

Analytical Modeling for Computing Lead Stress in a Novel Epicardial Micropacemaker

Implantation and maintenance of a permanent cardiac pacing system in children remains challenging due to small patient size, congenital heart defects and somatic growth. We are developing a novel epicardial micropacemaker for children that can be implanted on the epicardium within the pericardial space via a minimally-invasive technique. The key design configurations include a novel open-coiled lead in which living tissue replaces the usual polymeric support for the coiled conductor. To better understand and be able to predict the behavior of the implanted lead, we performed a radiographic image-based modeling study on a chronic animal test. We report a pilot study in which two mechanical dummy pacemakers with epicardial leads were implanted into an adult pig model via a minimally invasive approach. Fluoroscopy was obtained on the animal on Post-Operative Days #9, #35 and #56 (necropsy). We then constructed an analytic model to estimate the in vivo stress conditions on the open-coil lead based on the analysis of orthogonal biplane radiographic images. We obtained geometric deformation data of the implanted lead including elongation magnitudes and bending radii from sequenced films of cardiac motion cycles. The lead stress distribution was investigated on each film frame and the point of maximum stress (Mean Stress = 531.4 MPa; Alternating Stress = ± 216.4 MPa) was consistently where one of the leads exited the pericardial space, a deployment that we expected to be unfavorable. These results suggest the modeling approach can provide a basis for further design optimization. More animal tests and modeling will be needed to validate whether the novel lead design could meet the requirements to withstand ~200 million cardiac motion cycles over 5 years.

Minimally invasive implantable fetal micropacemaker: mechanical testing and technical refinements

This paper discusses the technical and safety requirements for cardiac pacing of a human fetus with heart failure and hydrops fetalis secondary to complete heart block. Engineering strategies to meet specific technical requirements were integrated into a systematic design and implementation consisting of a novel fetal micropacemaker, a percutaneous implantation system, and a sterile package that enables device storage and recharging maintenance in a clinical setting. We further analyzed observed problems on myocardial fixation and pacing lead fatigue previously reported in earlier preclinical trials. This paper describes the technical refinements of the implantable fetal micropacemaker to overcome these challenges. The mechanical performance has been extensively tested to verify the improvement of reliability and safety margins of the implantation system.

Major remaining gaps in models of sensorimotor systems

Experimental descriptions of the anatomy and physiology of individual components of sensorimotor systems have revealed substantial complexity, making it difficult to intuit how complete systems might work. This has led to increasing efforts to develop and employ mathematical models to study the emergent properties of such systems. Conversely, the development of such models tends to reveal shortcomings in the experimental database upon which models must be constructed and validated. In both cases models are most useful when they point up discrepancies between what we think we know and possibilities that we may have overlooked. This overview considers those components of complete sensorimotor systems that currently appear to be potentially important but poorly understood. These are generally omitted completely from modeled systems or buried in implicit assumptions that underlie the design of the model.

A percutaneously implantable fetal pacemaker

A miniaturized, self-contained pacemaker that could be implanted with a minimally invasive technique would dramatically improve the survival rate for fetuses that develop hydrops fetalis as a result of congenital heart block. We are currently validating a device that we developed to address this bradyarrhythmia. Preclinical studies in a fetal sheep model are underway to demonstrate that the device can be implanted via a minimally invasive approach, can mechanically withstand the harsh bodily environment, can induce effective contractions of the heart muscle with an adequate safety factor, and can successfully operate for the required device lifetime of three months using the previously-developed closed loop transcutaneous recharging system.

Bayesian action&perception: representing the world in the brain

Theories of perception seek to explain how sensory data are processed to identify previously experienced objects, but they usually do not consider the decisions and effort that goes into acquiring the sensory data. Identification of objects according to their tactile properties requires active exploratory movements. The sensory data thereby obtained depend on the details of those movements, which human subjects change rapidly and seemingly capriciously. Bayesian Exploration is an algorithm that uses prior experience to decide which next exploratory movement should provide the most useful data to disambiguate the most likely possibilities. In previous studies, a simple robot equipped with a biomimetic tactile sensor and operated according to Bayesian Exploration performed in a manner similar to and actually better than humans on a texture identification task. Expanding on this, "Bayesian Action&Perception" refers to the construction and querying of an associative memory of previously experienced entities containing both sensory data and the motor programs that elicited them. We hypothesize that this memory can be queried (i) to identify useful next exploratory movements during identification of an unknown entity ("action for perception") or (ii) to characterize whether an unknown entity is fit for purpose ("perception for action") or (iii) to recall what actions might be feasible for a known entity (Gibsonian affordance). The biomimetic design of this mechatronic system may provide insights into the neuronal basis of biological action and perception.

Estimation of excitatory drive from sparse motoneuron sampling

It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the user's intentions to be detected and converted into control signals sent to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation (TMR) can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.

First Clinical Experience with BION Implants for Therapeutic Electrical Stimulation

The objective of this study was to assess the usability and safety of BION injectable neuromuscular microstimulators for therapeutic electrical stimulation (TES) to treat two conditions involving disuse atrophy: poststroke shoulder subluxation in hemiplegic subjects and knee osteoarthritis. Clinicians were provided with PC-based software to track implants and to design the exercise programs. Subjects self-administered TES (3 sessions/day, 10-30 min/session) for 6 or 12 weeks. Outcome measures included subluxation for the shoulder study and knee function and pain for the osteoarthritis study. All subjects were comfortable with the BION equipment and therapy; eight of 10 experimental subjects elected to continue treatment after the study period. Shoulder subluxation was reduced by 55% ± 54%; knee function was enhanced by 65% ± 24%; and knee pain decreased by 78% ± 18%. The devices did not migrate and did not cause inflammation or pain. Thresholds were stable over time. We conclude that the use of BION implants to exercise atrophic muscles was well-accepted and provided effective rehabilitation in these two clinical conditions.

Are muscle synergies useful for neural control?

The observation that the activity of multiple muscles can be well approximated by a few linear synergies is viewed by some as a sign that such low-dimensional modules constitute a key component of the neural control system. Here, we argue that the usefulness of muscle synergies as a control principle should be evaluated in terms of errors produced not only in muscle space, but also in task space. We used data from a force-aiming task in two dimensions at the wrist, using an electromyograms (EMG)-driven virtual biomechanics technique that overcomes typical errors in predicting force from recorded EMG, to illustrate through simulation how synergy decomposition inevitably introduces substantial task space errors. Then, we computed the optimal pattern of muscle activation that minimizes summed-squared muscle activities, and demonstrated that synergy decomposition produced similar results on real and simulated data. We further assessed the influence of synergy decomposition on aiming errors (AEs) in a more redundant system, using the optimal muscle pattern computed for the elbow-joint complex (i.e., 13 muscles acting in two dimensions). Because EMG records are typically not available from all contributing muscles, we also explored reconstructions from incomplete sets of muscles. The redundancy of a given set of muscles had opposite effects on the goodness of muscle reconstruction and on task achievement; higher redundancy is associated with better EMG approximation (lower residuals), but with higher AEs. Finally, we showed that the number of synergies required to approximate the optimal muscle pattern for an arbitrary biomechanical system increases with task-space dimensionality, which indicates that the capacity of synergy decomposition to explain behavior depends critically on the scope of the original database. These results have implications regarding the viability of muscle synergy as a putative neural control mechanism, and also as a control algorithm to restore movements.

Percutaneously injectable fetal pacemaker: electronics, pacing thresholds, and power budget

We are developing a cardiac pacemaker that is designed to be implanted percutaneously into a fetus to treat complete heart block and consequent hydrops fetalis, which is otherwise fatal. One of the most significant considerations for this device is the technical challenges presented by the battery and charging system. The size of the device is limited to about 3 mm in diameter; batteries on this scale have very small charge capacities. The smaller capacity means that the device needs to be designed so that it uses as little current as possible and so that its battery can be recharged wirelessly. We determined the pacing thresholds for a simple relaxation oscillator that can be assembled from discrete, surface mount components and analyzed the power consumption of the device given different electrode configurations and stimulus parameters. An inductive recharging system will be required for some patients; it is feasible within the package constraints and under development.

Percutaneously injectable fetal pacemaker: electrodes, mechanical design and implantation

We are developing a self-contained cardiac pacemaker with a small, cylindrical shape (~3 × 20 mm) that permits it to be implanted percutaneously into a fetus to treat complete heart block and consequent hydrops fetalis, which is otherwise fatal. The device uses off-the-shelf components including a rechargeable lithium cell and a highly efficient relaxation oscillator encapsulated in epoxy and glass. A corkscrew electrode made from activated iridium can be screwed into the myocardium, followed by release of the pacemaker and a short, flexible lead entirely within the chest of the fetus to avoid dislodgement from fetal movement. The feasibility of implanting the device percutaneously under ultrasonic imaging guidance was demonstrated in acute adult rabbit experiments.

Optimal isn't good enough

The notion that biological systems come to embody optimal solutions seems consistent with the competitive drive of evolution. It has been used to interpret many examples of sensorimotor behavior. It is attractive from the viewpoint of control engineers because it solves the redundancy problem by identifying the one optimal motor strategy out of many similarly acceptable possibilities. This perspective examines whether there is sufficient basis to apply the formal engineering tools of optimal control to a reductionist understanding of biological systems. For an experimental biologist, this translates into whether the theory of optimal control generates nontrivial and testable hypotheses that accurately predict novel phenomena, ideally at deeper levels of structure than the observable behavior. The methodology of optimal control is applicable when there is (i) a single, known cost function to be optimized, (ii) an invertible model of the plant, and (iii) simple noise interfering with optimal performance. None of these is likely to be true for biological organisms. Furthermore, their motivation is usually good-enough rather than globally optimal behavior. Even then, the performance of a biological organism is often much farther from optimal than the physical limits of its hardware because the brain is continuously testing the acceptable limits of performance as well as just performing the task. This perspective considers an alternative strategy called "good-enough" control, in which the organism uses trial-and-error learning to acquire a repertoire of sensorimotor behaviors that are known to be useful, but not necessarily optimal. This leads to a diversity of solutions that tends to confer robustness on the individual organism and its evolution. It is also more consistent with the capabilities of higher sensorimotor structures, such as cerebral cortex, which seems to be designed to classify and recall complex sets of information, thereby allowing the organism to learn from experience, rather than to compute new strategies online. Optimal control has been a useful metaphor for understanding some superficial aspects of motor psychophysics. Reductionists who want to understand the underlying neural mechanisms need to move on.

Virtual biomechanics: a new method for online reconstruction of force from EMG recordings

Current methods to reconstruct muscle contributions to joint torque usually combine electromyograms (EMGs) with cadaver-based estimates of biomechanics, but both are imperfect representations of reality. Here, we describe a new method that enables online force reconstruction in which we optimize a "virtual" representation of muscle biomechanics. We first obtain tuning curves for the five major wrist muscles from the mean rectified EMG during the hold phase of an isometric aiming task when a cursor is driven by actual force recordings. We then apply a custom, gradient-descent algorithm to determine the set of "virtual pulling vectors" that best reach the target forces when combined with the observed muscle activity. When these pulling vectors are multiplied by the rectified and low-pass-filtered (1.3 Hz) EMG of the five muscles online, the reconstructed force provides a close spatiotemporal match to the true force exerted at the wrist. In three separate experiments, we demonstrate that the technique works equally well for surface and fine-wire recordings and is sensitive to biomechanical changes elicited by a modification of the forearm posture. In all conditions tested, muscle tuning curves obtained when the task was performed with feedback of reconstructed force were similar to those obtained when the task was performed with real force feedback. This online force reconstruction technique provides new avenues to study the relationship between neural control and limb biomechanics since the "virtual biomechanics" can be systematically altered at will.

Use of tactile feedback to control exploratory movements to characterize object compliance

Humans have been shown to be good at using active touch to perceive subtle differences in compliance. They tend to use highly stereotypical exploratory strategies, such as applying normal force to a surface. We developed similar exploratory and perceptual algorithms for a mechatronic robotic system (Barrett arm/hand system) equipped with liquid-filled, biomimetic tactile sensors (BioTac(®) from SynTouch LLC). The distribution of force on the fingertip was measured by the electrical resistance of the conductive liquid trapped between the elastomeric skin and a cluster of four electrodes on the flat fingertip surface of the rigid core of the BioTac. These signals provided closed-loop control of exploratory movements, while the distribution of skin deformations, measured by more lateral electrodes and by the hydraulic pressure, were used to estimate material properties of objects. With this control algorithm, the robot plus tactile sensor was able to discriminate the relative compliance of various rubber samples.

Bayesian exploration for intelligent identification of textures

In order to endow robots with human-like abilities to characterize and identify objects, they must be provided with tactile sensors and intelligent algorithms to select, control, and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a subset of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologically inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create a database of prior experience to use for identifying these same textures in future encounters. When exploring a texture, the discrimination algorithm adaptively selects the optimal movement to make and property to measure based on previous experience to differentiate the texture from a set of plausible candidates, a process we call Bayesian exploration. Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities. Absolute classification from the entire set of 117 textures generally required a small number of well-chosen exploratory movements (median = 5) and yielded a 95.4% success rate. The method of Bayesian exploration developed and tested in this paper may generalize well to other cognitive problems.

Natural and accelerated recovery from brain damage: experimental and theoretical approaches

The goal of the Caltech group is to gain insight into the processes that occur within the primate nervous system during dexterous reaching and grasping and to see whether natural recovery from local brain damage can be accelerated by artificial means. We will create computational models of the nervous system embodying this insight and explain a variety of clinically observed neurological deficits in human subjects using these models.

Evaluation of a noninvasive command scheme for upper-limb prostheses in a virtual reality reach and grasp task

C5/C6 tetraplegic patients and transhumeral amputees may be able to use voluntary shoulder motion as command signals for a functional electrical stimulation system or transhumeral prosthesis. Stereotyped relationships, termed "postural synergies," among the shoulder, forearm, and wrist joints emerge during goal-oriented reaching and transport movements as performed by able-bodied subjects. Thus, the posture of the shoulder can potentially be used to infer the desired posture of the elbow and forearm joints during reaching and transporting movements. We investigated how well able-bodied subjects could learn to use a noninvasive command scheme based on inferences from these postural synergies to control a simulated transhumeral prosthesis in a virtual reality task. We compared the performance of subjects using the inferential command scheme (ICS) with subjects operating the simulated prosthesis in virtual reality according to complete motion tracking of their actual arm and hand movements. Initially, subjects performed poorly with the ICS but improved rapidly with modest amounts of practice, eventually achieving performance only slightly less than subjects using complete motion tracking. Thus, inferring the desired movement of distal joints from voluntary shoulder movements appears to be an intuitive and noninvasive approach for obtaining command signals for prostheses to restore reaching and grasping functions.

Toward Perceiving Robots as Humans: Three Handshake Models Face the Turing-Like Handshake Test

In the Turing test a computer model is deemed to "think intelligently" if it can generate answers that are indistinguishable from those of a human. We developed an analogous Turing-like handshake test to determine if a machine can produce similarly indistinguishable movements. The test is administered through a telerobotic system in which an interrogator holds a robotic stylus and interacts with another party - artificial or human with varying levels of noise. The interrogator is asked which party seems to be more human. Here, we compare the human-likeness levels of three different models for handshake: (1) Tit-for-Tat model, (2) λ model, and (3) Machine Learning model. The Tit-for-Tat and the Machine Learning models generated handshakes that were perceived as the most human-like among the three models that were tested. Combining the best aspects of each of the three models into a single robotic handshake algorithm might allow us to advance our understanding of the way the nervous system controls sensorimotor interactions and further improve the human-likeness of robotic handshakes.

Real-time animation software for customized training to use motor prosthetic systems

Research on control of human movement and development of tools for restoration and rehabilitation of movement after spinal cord injury and amputation can benefit greatly from software tools for creating precisely timed animation sequences of human movement. Despite their ability to create sophisticated animation and high quality rendering, existing animation software are not adapted for application to neural prostheses and rehabilitation of human movement. We have developed a software tool known as MSMS (MusculoSkeletal Modeling Software) that can be used to develop models of human or prosthetic limbs and the objects with which they interact and to animate their movement using motion data from a variety of offline and online sources. The motion data can be read from a motion file containing synthesized motion data or recordings from a motion capture system. Alternatively, motion data can be streamed online from a real-time motion capture system, a physics-based simulation program, or any program that can produce real-time motion data. Further, animation sequences of daily life activities can be constructed using the intuitive user interface of Microsoft's PowerPoint software. The latter allows expert and nonexpert users alike to assemble primitive movements into a complex motion sequence with precise timing by simply arranging the order of the slides and editing their properties in PowerPoint. The resulting motion sequence can be played back in an open-loop manner for demonstration and training or in closed-loop virtual reality environments where the timing and speed of animation depends on user inputs. These versatile animation utilities can be used in any application that requires precisely timed animations but they are particularly suited for research and rehabilitation of movement disorders. MSMS's modeling and animation tools are routinely used in a number of research laboratories around the country to study the control of movement and to develop and test neural prostheses for patients with paralysis or amputations.

Mammalian muscle model for predicting force and energetics during physiological behaviors

Muscles convert metabolic energy into mechanical work. A computational model of muscle would ideally compute both effects efficiently for the entire range of muscle activation and kinematic conditions (force and length). We have extended the original Virtual Muscle algorithm (Cheng , 2000) to predict energy consumption for both slow- and fast-twitch muscle fiber types, partitioned according to the activation process (E(a)), cross-bridge cycling (E(xb)) and ATP/PCr recovery (E(recovery)). Because the terms of these functions correspond to identifiable physiological processes, their coefficients can be estimated directly from the types of experiments that are usually performed and extrapolated to dynamic conditions of natural motor behaviors. We also implemented a new approach to lumped modeling of the gradually recruited and frequency modulated motor units comprising each fiber type, which greatly reduced computational time. The emergent behavior of the model has significant implications for studies of optimal motor control and development of rehabilitation strategies because its trends were quite different from traditional estimates of energy (e.g., activation, force, stress, work, etc.). The model system was scaled to represent three different human experimental paradigms in which muscle heat was measured during voluntary exercise; predicted and observed energy rate agreed well both qualitatively and quantitatively.

Modeling the potentiality of spinal-like circuitry for stabilization of a planar arm system

The design of control systems for limb prostheses seems likely to benefit from an understanding of how sensorimotor integration is achieved in the intact system. Traditional BMIs guess what movement parameters are encoded by brain activity and then decode them to drive prostheses directly. Modeling the known structure and emergent properties of the biological decoder itself is likely to be more effective in bridging from normal brain activity to functionally useful limb movement. In this study, we have extended a model of spinal circuitry (termed SLR for spinal-like regulator; see Raphael, G., Tsianos, G. A., & Loeb G. E. 2010, Spinal-like regulator facilitates control of a two-degree-of-freedom wrist. The Journal of Neuroscience, 30(28), 9431-9444.) to a planar elbow-shoulder system to investigate how the spinal cord contributes to the control of a musculoskeletal system with redundant and multiarticular musculature and interaction (Coriolis) torques, which are common control problems for multisegment linkages throughout the body. The SLR consists of a realistic set of interneuronal pathways (monosynaptic Ia-excitatory, reciprocal Ia-inhibitory, Renshaw inhibitory, Ib-inhibitory, and propriospinal) that are driven by unmodulated step commands with learned amplitudes. We simulated the response of a planar arm to a brief, oblique impulse at the hand and investigated the role of cocontraction in learning to resist it. Training the SLR without cocontraction led to generally poor performance that was significantly worse than training with cocontraction. Further, removing cocontraction from the converged solutions and retraining the system achieved better performance than the SLR responses without cocontraction. Cocontraction appears to reshape the solution space, virtually eliminating the probability of entrapment in poor local minima. The local minima that are entered during learning with cocontraction are favorable starting points for learning to perform the task when cocontraction is abruptly removed. Given the control system's ability to learn effectively and rapidly, we hypothesize that it will generalize more readily to the wider range of tasks that subjects must learn to perform, as opposed to BMIs mapped to outputs of the musculoskeletal system.

MSMS software for VR simulations of neural prostheses and patient training and rehabilitation

In the increasingly complex prosthetic limbs for upper extremity amputees, more mechanical degrees of freedom are combined with various neural commands to produce versatile human-like movements. Development, testing, and fitting of such neural prosthetic systems and training patients to control them effectively are complex processes that cannot be performed efficiently or safely by ad hoc and trial-and-error approaches. We have developed a software tool known as MSMS to enable researchers and engineers to simulate the movement of these neural prostheses and evaluate their performance before they are built and to train the patients in virtual simulation environments to operate their prostheses before receiving them. Further, MSMS facilitates development of interactive virtual reality applications for training, rehabilitation, and treatment of patients suffering from movement disorders.