CPU-less robotics: distributed control of biomorphs

Traditional robotics revolves around the microprocessor. All well-known demonstrations of sensory guided motor control, such as jugglers and mobile robots, require at least one CPU. Recently, the availability of fast CPUs have made real-time sensory-motor control possible, however, problems with high power consumption and lack of autonomy still remain. In fact, the best examples of real-time robotics are usually tethered or require large batteries. We present a new paradigm for robotics control that uses no explicit CPU. We use computational sensors that are directly interfaced with adaptive actuation units. The units perform motor control and have learning capabilities. This architecture distributes computation over the entire body of the robot, in every sensor and actuator. Clearly, this is similar to biological sensory- motor systems. Some researchers have tried to model the latter in software, again using CPUs. We demonstrate this idea in with an adaptive locomotion controller chip. The locomotory controller for walking, running, swimming and flying animals is based on a Central Pattern Generator (CPG). CPGs are modeled as systems of coupled non-linear oscillators that control muscles responsible for movement. Here we describe an adaptive CPG model, implemented in a custom VLSI chip, which is used to control an under-actuated and asymmetric robotic leg

CPU-less robotics: distributed control of biomorphs

Traditional robotics revolves around the microprocessor. All well-known demonstrations of sensory guided motor control, such as jugglers and mobile robots, require at least one CPU. Recently, the availability of fast CPUs have made real-time sensory-motor control possible, however, problems with high power consumption and lack of autonomy still remain. In fact, the best examples of real-time robotics are usually tethered or require large batteries. We present a new paradigm for robotics control that uses no explicit CPU. We use computational sensors that are directly interfaced with adaptive actuation units. The units perform motor control and have learning capabilities. This architecture distributes computation over the entire body of the robot, in every sensor and actuator. Clearly, this is similar to biological sensory- motor systems. Some researchers have tried to model the latter in software, again using CPUs. We demonstrate this idea in with an adaptive locomotion controller chip. The locomotory controller for walking, running, swimming and flying animals is based on a Central Pattern Generator (CPG). CPGs are modeled as systems of coupled non-linear oscillators that control muscles responsible for movement. Here we describe an adaptive CPG model, implemented in a custom VLSI chip, which is used to control an under-actuated and asymmetric robotic leg

Simulating Adaptive Human Bipedal Locomotion Based on Phase Resetting Using Foot-Contact Information

Humans generate bipedal walking by cooperatively manipulating their complicated and redundant musculoskeletal systems to produce adaptive behaviors in diverse environments. To elucidate the mechanisms that generate adaptive human bipedal locomotion, we conduct numerical simulations based on a musculoskeletal model and a locomotor controller constructed from anatomical and physiological findings. In particular, we focus on the adaptive mechanism using phase resetting based on the foot-contact information that modulates the walking behavior. For that purpose, we first reconstruct walking behavior from the measured kinematic data. Next, we examine the roles of phase resetting on the generation of stable locomotion by disturbing the walking model. Our results indicate that phase resetting increases the robustness of the walking behavior against perturbations, suggesting that this mechanism contributes to the generation of adaptive human bipedal locomotion

National Space Biomedical Research Institute

The National Space Biomedical Research Institute (NSBRI) sponsors and performs fundamental and applied space biomedical research with the mission of leading a world-class, national effort in integrated, critical path space biomedical research that supports NASA's Human Exploration and Development of Space (HEDS) Strategic Plan. It focuses on the enabling of long-term human presence in, development of, and exploration of space. This will be accomplished by: designing, implementing, and validating effective countermeasures to address the biological and environmental impediments to long-term human space flight; defining the molecular, cellular, organ-level, integrated responses and mechanistic relationships that ultimately determine these impediments, where such activity fosters the development of novel countermeasures; establishing biomedical support technologies to maximize human performance in space, reduce biomedical hazards to an acceptable level, and deliver quality medical care; transferring and disseminating the biomedical advances in knowledge and technology acquired through living and working in space to the benefit of mankind in space and on Earth, including the treatment of patients suffering from gravity- and radiation-related conditions on Earth; and ensuring open involvement of the scientific community, industry, and the public at large in the Institute's activities and fostering a robust collaboration with NASA, particularly through Johnson Space Center

Doctor of Philosophy

dissertationHigh-count microelectrode arrays implanted in peripheral nerves could restore motor function after spinal cord injury or sensory function after limb loss via electrical stimulation. The same device could also help restore volitional control to a prosthesis-using amputee, or sensation to a Spinal cord Injury (SCI) patient, via recordings from the still-viable peripheral nerves. The overall objective of these dissertations studies is to improve the usefulness of intrafascicular electrodes, such as the Utah Slanted Electrode Array (USEA), for neuroprosthetic devices for limb loss or spinal cord injury patients. Previous work in cat sciatic nerve has shown that stimulation through the USEA can remain viable for months after implant. However, stimulation parameters were not stable, and recordings were lost rapidly and were subject to strong contamination by myoelectrical activity from adjacent muscles. Recent research has shown that even when mobility is restored to a patient, either through prosthesis or functional electrical stimulation, difficulties in using the affected limbs arise from the lack of sensory input. In the absence of the usual proprioceptive and cutaneous inputs from the limb, planning and executing motions can be challenging and sometimes lead to the user's abandonment of prostheses. To begin to address this need, I examined the ability of USEAs in cat hindlimb nerves to activate primary sensory fibers by monitoring evoked potentials in somatosensory cortex via skull-screw electrodes. I iv also monitored evoked EMG responses, and determined that it is possible to recruit sensory or motor responses independently of one another. In the second study of this dissertation, I sought to improve the long-term stability of USEAs in the PNS by physically and electrically stabilizing and protecting the array. To demonstrate the efficacy of the stabilization and shielding technique, I examined the recording capabilities of USEA electrodes and their selectivity of muscle activation over the long term in cat sciatic nerve. In addition to long-term viability, clinically useful neuroprosthetic devices will have to be capable of interfacing with complex motor systems such as the human hand. To extend previous results of USEAs in cat hindlimb nerves and to examine selectivity when interfacing with a complex sensorimotor system, I characterized EMG and cortical somatosensory responses to acute USEA stimulation in monkey arm nerves. Then, to demonstrate the functional usefulness of stimulation through the USEA. I used multi-array, multi-electrode stimulation to generate a natural, coordinated grasp

Emergent Central Pattern Generator Behavior in Gap-Junction-Coupled Hodgkin-Huxley Style Neuron Model

Most models of central pattern generators (CPGs) involve two distinct nuclei mutually inhibiting one another via synapses. Here, we present a single-nucleus model of biologically realistic Hodgkin-Huxley neurons with random gap junction coupling. Despite no explicit division of neurons into two groups, we observe a spontaneous division of neurons into two distinct firing groups. In addition, we also demonstrate this phenomenon in a simplified version of the model, highlighting the importance of afterhyperpolarization currents (I AHP ) to CPGs utilizing gap junction coupling. The properties of these CPGs also appear sensitive to gap junction conductance, probability of gap junction coupling between cells, topology of gap junction coupling, and, to a lesser extent, input current into our simulated nucleus

Plasticity of the neuromuscular system in response to changes in dactyly

Changes in limb morphology enabled the terrestrial lifestyle of tetrapods, as they adapted to different types of locomotion. The distal part of the limb – called the autopod, and encompassing the carpals/tarsals, metacarpals/metatarsals, and digits (fingers and toes) – in particular, went through drastic morphological changes during tetrapod evolution. Both digit patterns, as well as digit numbers, were modified during this process. While fossil data of stem-group tetrapods show that some ancestors possessed up to eight digits on one extremity, this number was reduced to five at the basis of the crown tetrapod group. Today, there is no known natural tetrapod population that surpasses the so-called ‘pentadactyl state’, and having more than five fingers is considered a pathology referred to as ‘polydactyly’. In the limb, not only the skeletal structure has to be modified during digit gains or losses, but also the soft tissues – like, e.g., the neuromuscular system - has to follow the skeletal changes, to form a fully functional unit. While polydactyly and the molecular alterations leading to it have been extensively studied at the skeletal level, little is known about the accompanying changes in the neuromuscular system. The present work aims to bridge this gap, by studying the plasticity of the neuromuscular system in response to changes in digit numbers. First, we evaluated muscle and nerve patterns in the periphery of polydactyl limbs. Then, on a molecular and cellular level, we attempted to understand how muscle-specific motor neuron pools are modified in polydactyl individuals. We used chicken embryos, where in-ovo manipulation of the limb can result in mirror-image duplication of the digits, thus providing an ideal and well-established model for our study. Using wholemount immunostaining of nerves and muscles followed by light sheet microscopy, we first reconstructed a 3D developmental time series of control wings and legs. There, our main finding uncovered a rotated pattern of the main nerve branches between wings and legs. Moreover, challenging the system with additional digits demonstrated a differential response of muscles and nerves in polydactyl limbs. Namely, while muscles seemed able to perfectly follow the pattern of skeletal mirror-image duplications, only two of the three main nerve branches responded and split to innervate the duplicated muscles. These intriguing results motivated us to turn our attention toward the central nervous system. The cell bodies of limb-innervating motor neurons reside in the spinal cord and are organized into small subsets, so-called motor neuron pools. Each pool innervates one specific muscle, and the survival and maturation of these pools largely depend on target-muscle derived factors. The observed changes in innervation patterns pointed toward a potential modification of motor neuron survival and pool identity, a rationale we set out to explore in the second half of this thesis. First, we showed that motor neuron numbers in the rostral LMC are decreased in the spinal cord of polydactyl individuals, due to increased cell death. This phenomenon was correlated to changes in the muscle patterns of the forearm, and the modification of fast versus slow muscle fibre composition in the limb, which might impair efficient innervation. In order to gain insights into the motor neuron pool compositions and transcriptomes, we performed, for the first time in chicken embryos, single-cell RNA-sequencing of spinal cord tissue. The resulting data identified putative digit-specific motoneuron pool markers but indicated only minor differences in cell-type distribution between polydactyl and control neural tubes. However, the traditional emulsion-based method might have failed to capture high enough numbers of motor neurons. Accordingly, we focused on a different, more direct approach, for an in-depth study of motor neuron pools innervating native and duplicated muscles in polydactyl embryos. To this aim, we developed a method combining ex-ovo retrograde axonal labelling, manual purification of neurons, and the highly sensitive Smart-seq2 single-cell RNA-sequencing technique. Our results showed that our method is efficient in extracting embryonic motor neurons, that it captures a high number of genes per cellular transcriptome, and therefore represents a valuable tool for studying motor circuit formation. Preliminary comparative transcriptomic analysis of neurons coming from two motor neuron pools – innervating the EMR and FDQ wing muscles - revealed unique pool-specific signatures and further validated our technique. Collectively, in the present thesis work, we describe the plasticity of the neuromuscular system in response to polydactyly, and how muscles and nerves integrate changes in the skeletal patterning. Furthermore, we show that changes in muscle identity and shifts in muscle fibre composition can affect motor neuron survival. Also, we developed a technique to study in great detail the transcriptomes of muscle-specific motoneurons, and we intend to use this methodology to better understand neuronal wiring and molecular muscle-neuron cross-talk during circuit formation. Our data provide the basis for further developmental studies and offer a framework for medical research, in order to better understand the etiologies of human polydactyl phenotypes

Reorganization of a Spinal Motoneuron Nucleus following Autologous Nerve Graft in the Rat

Autologous nerve grafts are steadily regarded as the method of choice for bridging the nerve gaps resulting after peripheral nerve lesions with substance defects. Microsurgical techniques and the perineurial suture of corresponding fascicles have improved the functional results following peripheral nerve graft. However, regeneration success is often disappointing, despite the most thorough technique and expertise. The loss of spinal motoneurons associated with a nerve lesion and the growth of axon sprouts in inadequate endoneurial sheaths were held responsible as the reason for the lowered muscular strength, limited movement coordination and fine motor skills, poor differentiation and localization of sensory stimuli and for the lack of tactile gnosis. In this experimental study, it is assumed that the central effects at the level of the spinal motoneuron nuclei show an image of the peripheral misinnervation in topographical-morphological terms, and can supply an explanatory model for the functional motor deficits after peripheral nerve graft. On the other hand, the plastic changes of a motor cell column in the reinnervation process influence the structural-functional relationships of the motor units in a variety of clinically relevant ways

Exploration of biological neural wiring using self-organizing agents

Cette thèse présente un nouveau modèle computationnel capable de détecter les configurations temporelles d'une voie neuronale donnée afin d'en construire sa copie artificielle. Cette construction représente un véritable défi puisqu'il est impossible de faire des mesures directes sur des neurones individuels dans le système nerveux central humain et que la voie neuronale sous-jacente doit être considérée comme une boîte noire. La théorie des Systèmes Multi-Agents Adaptatifs (AMAS) est utilisée pour relever ce défi. Dans ces systèmes auto-organisateurs, un grand nombre d'agents logiciels coopératifs interagissent localement pour donner naissance à un comportement collectif ascendant. Le résultat est un modèle émergent dans lequel chaque entité logicielle représente un neurone " intègre-et-tire ". Ce modèle est appliqué aux réponses réflexes d'unités motrices isolées obtenues sur des sujets humains conscients. Les résultats expérimentaux, comparés à des données obtenues expérimentalement, montrent que le modèle découvre la fonctionnalité de voies neuronales humaines. Ce qui rend le modèle prometteur est le fait que c'est, à notre connaissance, le premier modèle réaliste capable d'auto-construire un réseau neuronal artificiel en combinant efficacement les neurosciences et des systèmes multi-agents adaptatifs. Bien qu'aucune preuve n'existe encore sur la correspondance exacte entre connectivité du modèle et connectivité du système humain, tout laisse à penser que ce modèle peut aider les neuroscientifiques à améliorer leur compréhension des réseaux neuronaux humains et qu'il peut être utilisé pour établir des hypothèses afin de conduire de futures expérimentations.In this thesis, a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication is presented. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, the Adaptive Multi-Agent Systems (AMAS) theory in which large sets of cooperative software agents interacting locally give rise to collective behavior bottom-up is used. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model uncovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with self-adaptive multi-agent systems. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments