Afferent control of locomotor CPG: insights from a simple neuromechanical model

Tapping into the human spinal locomotor centres with transspinal stimulation

Human locomotion is controlled by spinal neuronal networks of similar properties, function, and organization to those described in animals. Transspinal stimulation affects the spinal locomotor networks and is used to improve standing and walking ability in paralyzed people. However, the function of locomotor centers during transspinal stimulation at different frequencies and intensities is not known. Here, we document the 3D joint kinematics and spatiotemporal gait characteristics during transspinal stimulation at 15, 30, and 50 Hz at sub-threshold and supra-threshold stimulation intensities. We document the temporal structure of gait patterns, dynamic stability of joint movements over stride-to-stride fluctuations, and limb coordination during walking at a self-selected speed in healthy subjects. We found that transspinal stimulation (1) affects the kinematics of the hip, knee, and ankle joints, (2) promotes a more stable coordination at the left ankle, (3) affects interlimb coordination of the thighs, and (4) intralimb coordination between thigh and foot, (5) promotes greater dynamic stability of the hips, (6) increases the persistence of fluctuations in step length variability, and lastly (7) affects mechanical walking stability. These results support that transspinal stimulation is an important neuromodulatory strategy that directly affects gait symmetry and dynamic stability. The conservation of main effects at different frequencies and intensities calls for systematic investigation of stimulation protocols for clinical applications.

FARMS: Framework for Animal and Robot Modeling and Simulation

The study of animal locomotion and neuromechanical control offers valuable insights for advancing research in neuroscience, biomechanics, and robotics. We have developed FARMS (Framework for Animal and Robot Modeling and Simulation), an open-source, interdisciplinary framework, designed to facilitate access to neuromechanical simulations for modeling, simulation, and analysis of animal locomotion and bio-inspired robotic systems. By providing an accessible and user-friendly platform, FARMS aims to lower the barriers for researchers to explore the complex interactions between the nervous system, musculoskeletal structures, and their environment. Integrating the MuJoCo physics engine in a modular manner, FARMS enables realistic simulations and fosters collaboration among neuroscientists, biologists, and roboticists. FARMS has already been extensively used to study locomotion in animals such as mice, drosophila, fish, salamanders, and centipedes, serving as a platform to investigate the role of central pattern generators and sensory feedback. This article provides an overview of the FARMS framework, discusses its interdisciplinary approach, showcases its versatility through specific case studies, and highlights its effectiveness in advancing our understanding of locomotion. In particular, we show how we used FARMS to study amphibious locomotion by presenting experimental demonstrations across morphologies and environments based on neural controllers with central pattern generators and sensory feedback circuits models. Overall, the goal of FARMS is to contribute to a deeper understanding of animal locomotion, the development of innovative bio-inspired robotic systems, and promote accessibility in neuromechanical research.

Tapping Into the Human Spinal Locomotor Centres With Transspinal Stimulation

Human locomotion is controlled by spinal neuronal networks of similar properties, function, and organization to those described in animals. Transspinal stimulation affects the spinal locomotor networks and is used to improve standing and walking ability in paralyzed people. However, the function of locomotor centers during transspinal stimulation at different frequencies and intensities is not known. Here, we document the 3D joint kinematics and spatiotemporal gait characteristics during transspinal stimulation at 15, 30, and 50 Hz at sub-threshold and supra-threshold stimulation intensities. We document the temporal structure of gait patterns, dynamic stability of joint movements over stride-to-stride fluctuations, and limb coordination during walking at a self-selected speed in healthy subjects. We found that transspinal stimulation 1) affects the kinematics of the hip, knee, and ankle joints, 2) promotes a more stable coordination at the left ankle, 3) improves interlimb coordination of the thighs, 4) improves intralimb coordination between thigh and foot, 5) promotes greater dynamic stability of the hips, and lastly 6) affects the mechanical stability of the joints. These results support that transspinal stimulation is an important neuromodulatory strategy that directly affects gait symmetry and dynamic stability. The conservation of main effects at different frequencies and intensities calls for systematic investigation of stimulation protocols for clinical applications.

Sensory Feedback and Central Neuronal Interactions in Mouse Locomotion

Locomotion is a complex process involving specific interactions between the central neural controller and the mechanical components of the system. The basic rhythmic activity generated by locomotor circuits in the spinal cord defines rhythmic limb movements and their central coordination. The operation of these circuits is modulated by sensory feedback from the limbs providing information about the state of the limbs and the body. However, the specific role and contribution of central interactions and sensory feedback in the control of locomotor gait and posture remain poorly understood. We use biomechanical data on quadrupedal locomotion in mice and recent findings on the organization of neural interactions within the spinal locomotor circuitry to create and analyze a tractable mathematical model of mouse locomotion. The model includes a simplified mechanical model of the mouse body with four limbs and a central controller composed of four rhythm generators, each operating as a state machine controlling the state of one limb. Feedback signals characterize the load and extension of each limb as well as postural stability (balance). We systematically investigate and compare several model versions and compare their behavior to existing experimental data on mouse locomotion. Our results highlight the specific roles of sensory feedback and some central propriospinal interactions between circuits controlling fore and hind limbs for speed-dependent gait expression. Our models suggest that postural imbalance feedback may be critically involved in the control of swing-to-stance transitions in each limb and the stabilization of walking direction.

Central pattern generator network model for the alternating hind limb gait of rats based on the modified Van der Pol equation

Herein, we employed a central pattern generator (CPG), a spinal cord neural network that regulates lower-limb gait during intra-spinal micro-stimulation (ISMS). Particularly, ISMS was used to determine the spatial distribution pattern of CPG sites in the spinal cord and the signal regulation pattern that induced the CPG network to produce coordinated actions. Based on the oscillation phenomenon of the single CPG neurons of Van der Pol (VDP) oscillators, a double-cell CPG neural network model was constructed to realise double lower limbs, six-joint modelling, the simulation of 12 neural circuits, the CPG loci characterising stimuli-inducing alternating movements and changes in polarity stimulation signals in rat hindlimbs, and leg-state change movements. The feasibility and effectiveness of the CPG neural network were verified by recording the electromyographic burst-release mode of the flexor and extensor muscles of the knee joints during CPG electrical stimulation. The results revealed that the output pattern of the CPG presented stable rhythm and coordination characteristics. The 12-neuron CPG model based on the improved VDP equation realised single-point control while significantly reducing the number of stimulation electrodes in the gait training of spinal cord injury patients. We believe that this study advances motor function recovery in rehabilitation medicine.

In-silico neuro musculoskeletal model reproduces the movement types obtained by spinal micro stimulation

BACKGROUND AND OBJECTIVES

Virtual patients and physiologies allow experimentation, design, and early-stage clinical trials in-silico. Virtual patient technology for human movement systems that encompasses musculoskeleton and its neural control are few and far in between. Our major goal is to create a neuro- musculoskeletal upper limb in-silico model, which is modular in architecture and generates movement as an emergent phenomenon out of a multiscale co-simulation of spinal cord neural control and musculoskeletal dynamics.

METHODS

The model is developed on the NEUROiD movement simulation platform that enables a co-simulation of popular neural simulator NEURON and the musculoskeletal simulator OpenSim. We further characterized and demonstrated the use of this model in generating a range of commonly observed upper limb movements by means of a spatio-temporal stimulation pattern delivered to the cervical spinal cord.

RESULTS

We were able to characterize the model based on proprioception (Ia, Ib and II fibers), afferent conduction delay and inital postures of the musculoskeletal system. A smooth movement was achieved in all the considered experiments. The generated movements in all degrees of freedom were reproduced in accordance with the previous experimental studies.

CONCLUSION

In this work, design and development of the upper limb model was described in a modular fashion, while reusing existing models and modules. We believe this work enables a first and small step towards an in-silico paradigms for understanding upper limb movement, disease pathology, medication, and rehabilitation.

A review of motor neural system robotic modeling approaches and instruments

In this review, we are considering an actively developing tool in neuroscience-robotic modeling. The new perspective and existing application fields, tools, and methods are discussed. We try to determine starting positions and approaches that are useful at the beginning of new research in this field. Among multiple directions of the research is robotic modeling on the level of muscles fibers and their afferents, skin surface sensors, muscles, and joints proprioceptors. Some examples of technical implementation for physical modeling are reviewed. They are software and hardware tools like event-related modeling algorithms, reduced neuron models, robotic drives constructions. We observe existing drives technologies and prospective electric motor types: switched reluctance and transverse flux motors. Next, we look at the existing examples and approaches for robotic modeling of the cerebellum and spinal cord neural networks. These examples show practical methods for the model neural network architecture and adaptation. Those methods allow the use of cortical and spinal cord reflexes for the network training and apply additional artificial blocks for data processing in other brain structures that transmit and receive data from biologically realistic models.

EMG-driven control in lower limb prostheses: a topic-based systematic review

BACKGROUND

The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts.

OBJECTIVE AND METHODOLOGIES

This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers.

RESULTS AND CONCLUSIONS

The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees.

Optimization of immune receptor-related hypersensitive cell death response assay using agrobacterium-mediated transient expression in tobacco plants

BACKGROUND

The study of the regulatory mechanisms of evolutionarily conserved Nucleotide-binding leucine-rich repeat (NLR) resistance (R) proteins in animals and plants is of increasing importance due to understanding basic immunity and the value of various crop engineering applications of NLR immune receptors. The importance of temperature is also emerging when applying NLR to crops responding to global climate change. In particular, studies of pathogen effector recognition and autoimmune activity of NLRs in plants can quickly and easily determine their function in tobacco using agro-mediated transient assay. However, there are conditions that should not be overlooked in these cell death-related assays in tobacco.

RESULTS

Environmental conditions play an important role in the immune response of plants. The system used in this study was to establish conditions for optimal hypertensive response (HR) cell death analysis by using the paired NLR RPS4/RRS1 autoimmune and AvrRps4 effector recognition system. The most suitable greenhouse temperature for growing plants was fixed at 22 °C. In this study, RPS4/RRS1-mediated autoimmune activity, RPS4 TIR domain-dependent cell death, and RPS4/RRS1-mediated HR cell death upon AvrRps4 perception significantly inhibited under conditions of 65% humidity. The HR is strongly activated when the humidity is below 10%. Besides, the leaf position of tobacco is important for HR cell death. Position #4 of the leaf from the top in 4-5 weeks old tobacco plants showed the most effective HR cell death.

CONCLUSIONS

As whole genome sequencing (WGS) or resistance gene enrichment sequencing (RenSeq) of various crops continues, different types of NLRs and their functions will be studied. At this time, if we optimize the conditions for evaluating NLR-mediated HR cell death, it will help to more accurately identify the function of NLRs. In addition, it will be possible to contribute to crop development in response to global climate change through NLR engineering.

Pace Running of a Quadruped Robot Driven by Pneumatic Muscle Actuators: An Experimental Study

Our goal is to design a neuromorphic locomotion controller for a prospective bioinspired quadruped robot driven by artificial muscle actuators. In this paper, we focus on achieving a running gait called a pace, in which the ipsilateral pairs of legs move in phase, while the two pairs together move out of phase, by a quadruped robot with realistic legs driven by pneumatic muscle actuators. The robot is controlled by weakly coupled two-level central pattern generators to generate a pace gait with leg loading feedback. Each leg is moved through four sequential phases like an animal, i.e., touch-down, stance, lift-off, and swing phases. We find that leg loading feedback to the central pattern generator can contribute to stabilizing pace running with an appropriate cycle autonomously determined by synchronizing each leg’s oscillation with the roll body oscillation without a human specifying the cycle. The experimental results conclude that our proposed neuromorphic controller is beneficial for achieving pace running by a muscle-driven quadruped robot.

Contribution of Afferent Feedback to Adaptive Hindlimb Walking in Cats: A Neuromusculoskeletal Modeling Study

Mammalian locomotion is generated by central pattern generators (CPGs) in the spinal cord, which produce alternating flexor and extensor activities controlling the locomotor movements of each limb. Afferent feedback signals from the limbs are integrated by the CPGs to provide adaptive control of locomotion. Responses of CPG-generated neural activity to afferent feedback stimulation have been previously studied during fictive locomotion in immobilized cats. Yet, locomotion in awake, behaving animals involves dynamic interactions between central neuronal circuits, afferent feedback, musculoskeletal system, and environment. To study these complex interactions, we developed a model simulating interactions between a half-center CPG and the musculoskeletal system of a cat hindlimb. Then, we analyzed the role of afferent feedback in the locomotor adaptation from a dynamic viewpoint using the methods of dynamical systems theory and nullcline analysis. Our model reproduced limb movements during regular cat walking as well as adaptive changes of these movements when the foot steps into a hole. The model generates important insights into the mechanism for adaptive locomotion resulting from dynamic interactions between the CPG-based neural circuits, the musculoskeletal system, and the environment.

Single-cell and ensemble activity of lumbar intermediate and ventral horn interneurons in the spinal air-stepping cat

We explored the relationship between population interneuronal network activation and motor output in the adult, in vivo, air-stepping, spinal cat. By simultaneously measuring the activity of large numbers of spinal interneurons, we explored ensembles of coherently firing interneurons and their relation to motor output. In addition, the networks were analyzed in relation to their spatial distribution along the lumbar enlargement for evidence of localized groups driving particular phases of the locomotor step cycle. We simultaneously recorded hindlimb EMG activity during stepping and extracellular signals from 128 channels across two polytrodes inserted within lamina V-VII of two separate lumbar segments. Results indicated that spinal interneurons participate in one of two ensembles that are highly correlated with the flexor or the extensor muscle bursts during stepping. Interestingly, less than half of the isolated single units were significantly unimodally tuned during the step cycle whereas >97% of the single units of the ensembles were significantly correlated with muscle activity. These results show the importance of population scale analysis in neural studies of behavior as there is a much greater correlation between muscle activity and ensemble firing than between muscle activity and individual neurons. Finally, we show that there is no correlation between interneurons' rostrocaudal locations within the lumbar enlargement and their preferred phase of firing or ensemble participation. These findings indicate that spinal interneurons of lamina V-VII encoding for different phases of the locomotor cycle are spread throughout the lumbar enlargement in the adult spinal cord.NEW & NOTEWORTHY We report on the ensemble organization of interneuronal activity in the spinal cord during locomotor movements and show that lumbar intermediate zone interneurons organize in two groups related to the two major phases of walking: stance and swing. Ensemble organization is also shown to better correlate with muscular output than single-cell activity, although ensemble membership does not appear to be somatotopically organized within the spinal cord.

Autonomous speed adaptation by a muscle-driven hind leg robot modeled on a cat without intervention from brain

This study aims to design a nervous system model to drive the realistic muscle-driven legs for the locomotion of a quadruped robot. We evaluate our proposed nervous system model with a hind leg simulated model and robot. We apply a two-level central pattern generator for each leg, which generates locomotion rhythms and reproduces cat-like leg trajectories by driving different sets of the muscles at any timing during one cycle of moving the leg. The central pattern generator receives sensory feedback from leg loading. A cat simulated model and a robot with two hind legs, each with three joints driven by six muscle models, are controlled by our nervous system model. Even though their hind legs are forced backward at a wide range of speeds, they can adapt to the speed variation by autonomously adjusting its stride and cyclic duration without changing any parameters or receiving any descending inputs. In addition to the autonomous speed adaptation, the cat hind leg robot switched from a trot-like gait to a gallop-like gait while speeding up. These features can be observed in existing animal locomotion tests. These results demonstrate that our nervous system is useful as a valid and practical legged locomotion controller.

Computational Modeling of Spinal Locomotor Circuitry in the Age of Molecular Genetics

Neuronal circuits in the spinal cord are essential for the control of locomotion. They integrate supraspinal commands and afferent feedback signals to produce coordinated rhythmic muscle activations necessary for stable locomotion. For several decades, computational modeling has complemented experimental studies by providing a mechanistic rationale for experimental observations and by deriving experimentally testable predictions. This symbiotic relationship between experimental and computational approaches has resulted in numerous fundamental insights. With recent advances in molecular and genetic methods, it has become possible to manipulate specific constituent elements of the spinal circuitry and relate them to locomotor behavior. This has led to computational modeling studies investigating mechanisms at the level of genetically defined neuronal populations and their interactions. We review literature on the spinal locomotor circuitry from a computational perspective. By reviewing examples leading up to and in the age of molecular genetics, we demonstrate the importance of computational modeling and its interactions with experiments. Moving forward, neuromechanical models with neuronal circuitry modeled at the level of genetically defined neuronal populations will be required to further unravel the mechanisms by which neuronal interactions lead to locomotor behavior.

Integrating Non-spiking Interneurons in Spiking Neural Networks

Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

A Conceptual Blueprint for Making Neuromusculoskeletal Models Clinically Useful

The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson’s disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author’s personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility.

Intrathecal Delivery of BDNF Into the Lumbar Cistern Re-Engages Locomotor Stepping After Spinal Cord Injury

Delivery of neurotrophins to the spinal injury site via cellular transplants or viral vectors administration has been shown to promote recovery of locomotion in the absence of locomotor training in adult spinalized animals. These delivery methods involved risks of secondary injury to the cord and do not allow for precise and controlled dosing making them unsuitable for clinical applications. The present study was aimed at evaluating the locomotor recovery efficacy and safety of the neurotrophin BDNF delivered intrathecally to the lumbar locomotor centers using an implantable and programmable infusion mini-pump. Results showed that BDNF treated spinal cats recovered weight-bearing plantar stepping at all velocities tested (0.3-0.8 m/s). Spinal cats treated with saline did not recover stepping ability, especially at higher velocities, and dragged their hind paws on the treadmill. Histological evaluation showed minimal catheter associated trauma and tissue inflammation, underlining that intrathecal delivery by an implantable/programmable pump is a safe and effective method for delivery of a controlled BDNF dosage; it poses minimal risks to the cord and is clinically translational.

On the Organization of the Locomotor CPG: Insights From Split-Belt Locomotion and Mathematical Modeling

Rhythmic limb movements during locomotion are controlled by central pattern generator (CPG) circuits located in the spinal cord. It is considered that these circuits are composed of individual rhythm generators (RGs) for each limb interacting with each other through multiple commissural and long propriospinal circuits. The organization and operation of each RG are not fully understood, and different competing theories exist about interactions between its flexor and extensor components, as well as about left-right commissural interactions between the RGs. The central idea of circuit organization proposed in this study is that with an increase of excitatory input to each RG (or an increase in locomotor speed) the rhythmogenic mechanism of the RGs changes from "flexor-driven" rhythmicity to a "classical half-center" mechanism. We test this hypothesis using our experimental data on changes in duration of stance and swing phases in the intact and spinal cats walking on the ground or tied-belt treadmills (symmetric conditions) or split-belt treadmills with different left and right belt speeds (asymmetric conditions). We compare these experimental data with the results of mathematical modeling, in which simulated CPG circuits operate in similar symmetric and asymmetric conditions with matching or differing control drives to the left and right RGs. The obtained results support the proposed concept of state-dependent changes in RG operation and specific commissural interactions between the RGs. The performed simulations and mathematical analysis of model operation under different conditions provide new insights into CPG network organization and limb coordination during locomotion.

Modeling of the neural mechanism underlying the terrestrial turning of the salamander

In order to explore the neural mechanism underlying salamander terrestrial turning, an improved biomechanical model is proposed by modifying the forelimb structure of the existing biomechanical model. Based on the proposed improved biomechanical model, a new spinal locomotor network model is constructed which contains the interneuron networks and motoneuron pool. Control methods are also developed for the new model which increase its transient response speed, control the initial swing order of the forelimbs, and generate different walking turning gait and turning on the spot (turning without moving forward). The simulation results show that the biomechanical model controlled by the new spinal locomotor network model can generate different walking turning and turning on the spot, and can control posture and the initial swing order of the forelimbs. Moreover, the transient response speed of the proposed model is very rapid. This paper thus provides a useful tool for exploring the operational mechanism of the spinal circuitry of the salamander. In addition, the research results presented here may inspire the construction of artificial spinal control networks for bionic robots.

Phase-Dependent Response to Afferent Stimulation During Fictive Locomotion: A Computational Modeling Study

Central pattern generators (CPGs) in the spinal cord generate rhythmic neural activity and control locomotion in vertebrates. These CPGs operate under the control of sensory feedback that affects the generated locomotor pattern and adapt it to the animal's biomechanics and environment. Studies of the effects of afferent stimulation on fictive locomotion in immobilized cats have shown that brief stimulation of peripheral nerves can reset the ongoing locomotor rhythm. Depending on the phase of stimulation and the stimulated nerve, the applied stimulation can either shorten or prolong the current locomotor phase and the locomotor cycle. Here, we used a mathematical model of a half-center CPG to investigate the phase-dependent effects of brief stimulation applied to CPG on the CPG-generated locomotor oscillations. The CPG in the model consisted of two half-centers mutually inhibiting each other. The rhythmic activity in each half-center was based on a slowly inactivating, persistent sodium current. Brief stimulation was applied to CPG half-centers in different phases of the locomotor cycle to produce phase-dependent changes in CPG activity. The model reproduced several results from experiments on the effect of afferent stimulation of fictive locomotion in cats. The mechanisms of locomotor rhythm resetting under different conditions were analyzed using dynamic systems theory methods.

Three-dimensional walking of a simulated muscle-driven quadruped robot with neuromorphic two-level central pattern generators

We aim to design a neuromorphic controller for the locomotion of a quadruped robot with muscle-driven leg mechanisms. To this end, we use a simulated cat model; each leg of the model is equipped with three joints driven by six muscle models incorporating two-joint muscles. For each leg, we use a two-level central pattern generator consisting of a rhythm generation part to produce basic rhythms and a pattern formation part to synergistically activate a different set of muscles in each of the four sequential phases (swing, touchdown, stance, and liftoff). Conventionally, it was difficult for a quadruped model with such realistic neural systems and muscle-driven leg mechanisms to walk even on flat terrain, but because of our improved neural and mechanical components, our quadruped model succeeds in reproducing motoneuron activations and leg trajectories similar to those in cats and achieves stable three-dimensional locomotion at a variety of speeds. Moreover, the quadruped is capable of walking upslope and over irregular terrains and adapting to perturbations, even without adjusting the parameters.

Kinematics associated with treadmill walking in Rett syndrome

BACKGROUND AND PURPOSE

Individuals with Rett syndrome suffer from severely impaired cognitive and motor performance. Current movement-related therapeutic programs often include traditional physical therapy activities and assisted treadmill walking routines for those individuals who are ambulatory. However, there are no quantitative reports of kinematic gait parameters obtained during treadmill walking. The purpose of this research was to characterize the kinematic patterns of the lower limbs during treadmill walking as speed was slowly increased.

METHODS

Seventeen independently ambulatory females diagnosed with a methyl-CpG-binding protein 2 gene mutation walked on a motorized treadmill while joint kinematics were obtained by a camera-based motion capture system and analysis software.

RESULTS

Stride times progressively decreased as treadmill speeds increased. There were significant main effects of speed on sagittal knee and hip ranges of motion and hip velocity. There were large joint asymmetries and variance values relative to other ambulatory patient populations, although variance values decreased as walking speed increased.

CONCLUSIONS

The results indicate that individuals with Rett syndrome can adapt their kinematic gait patterns in response to increasing treadmill speed, but only within a narrow range of speeds. We suggest that treadmill training for ambulatory individuals with Rett syndrome may promote improved walking kinematics and possibly provide overall health benefits.Implications for rehabilitationWalking is an activity that can counter the negative impacts of the sedentary lifestyle of many individuals with disabilities, including those individuals with Rett syndrome.Documentation of the lower limb kinematic patterns displayed during walking by ambulatory females with Rett syndrome can be used by clinicians to evaluate their patients' gait performance in response to therapeutic and pharmacological interventions designed to promote walking.The ability to adapt to increases in treadmill speed suggests that a training program of treadmill walking may be effective in promoting improved gait performance in individuals with Rett syndrome.

Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.

A Poincare map based analysis of stroke patients' walking after a rehabilitation by a robot

Since the past decade, rehabilitation robots have become common technologies for recovering gait ability after a stroke. Nevertheless, it is believed that these robots can be further enhanced. Hence, several researches are making progress in optimizing gait rehabilitation robots. However, most of these researches have only assessed the robots and their controllers in improving spatiotemporal and kinetic features of walking. There are not many researchers have focused on the robots' controllers' effects on the central nervous or neuromuscular systems. On the other hand, recently computational methods have been utilized to investigate the rehabilitations of neural disorders, through developing neuromechanical models. However, these methods have neither studied the robot-assisted gait rehabilitation, nor have they theoretically proved why rehabilitation exercises enhance patients' walking ability. Therefore, this paper merged a theoretical approach into a computational method to investigate the effects of gait rehabilitation robots on post-stroke neuromuscular system. To this end, a neuromechanical model of gait has been developed and thereby, the Poincare maps of intact and stroke people have been obtained. Comparison of these maps revealed why a stroke reduces the stability of walking. Then, the effect of an impedance controller, which is used in a rehabilitative robot, is scrutinized in stabilizing a walking motion. Obtaining the Poincare map of this close-loop system, proved that this controller improves motion stability. Finally, the effect of this controller is investigated by simulations and experiments. The experimental tests are performed by Arman rehabilitative robot. Clinical Reference Number: IR.TMU.REC.1394.254.

Model of a bilateral Brown-type central pattern generator for symmetric and asymmetric locomotion

The coordinated activity of muscles is produced in part by spinal rhythmogenic neural circuits, termed central pattern generators (CPGs). A classical CPG model is a system of coupled oscillators that transform locomotor drive into coordinated and gait-specific patterns of muscle recruitment. The network properties of this conceptual model can be simulated by a system of ordinary differential equations with a physiologically inspired coupling locus of interactions capturing the timing relationship for bilateral coordination of limbs in locomotion. Whereas most similar models are solved numerically, it is intriguing to have a full analytical description of this plausible CPG architecture to illuminate the functionality within this structure and to expand it to include steering control. Here, we provided a closed-form analytical solution contrasted against the previous numerical method. The evaluation time of the analytical solution was decreased by an order of magnitude when compared with the numerical approach (relative errors, <0.01%). The analytical solution tested and supported the previous finding that the input to the model can be expressed in units of the desired limb locomotor speed. Furthermore, we performed parametric sensitivity analysis in the context of controlling steering and documented two possible mechanisms associated with either an external drive or intrinsic CPG parameters. The results identify specific propriospinal pathways that may be associated with adaptations within the CPG structure. The model offered several network configurations that may generate the same behavioral outcomes. NEW & NOTEWORTHY Using a simple process of leaky integration, we developed an analytical solution to a robust model of spinal pattern generation. We analyzed the ability of this neural element to exert locomotor control of the signal associated with limb speeds and tested the ability of this simple structure to embed steering control using the velocity signal in the model's inputs or within the internal connectivity of its elements.

State-dependent rhythmogenesis and frequency control in a half-center locomotor CPG

The spinal locomotor central pattern generator (CPG) generates rhythmic activity with alternating flexion and extension phases. This rhythmic pattern is likely to result from inhibitory interactions between neural populations representing flexor and extensor half-centers. However, it is unclear whether the flexor-extensor CPG has a quasi-symmetric organization with both half-centers critically involved in rhythm generation, features an asymmetric organization with flexor-driven rhythmogenesis, or comprises a pair of intrinsically rhythmic half-centers. There are experimental data that support each of the above concepts but appear to be inconsistent with the others. In this theoretical/modeling study, we present and analyze a CPG model architecture that can operate in different regimes consistent with the above three concepts depending on conditions, which are defined by external excitatory drives to CPG half-centers. We show that control of frequency and phase durations within each regime depends on network dynamics, defined by the regime-dependent expression of the half-centers' intrinsic rhythmic capabilities and the operating phase transition mechanisms (escape vs. release). Our study suggests state dependency in locomotor CPG operation and proposes explanations for seemingly contradictory experimental data. NEW & NOTEWORTHY Our theoretical/modeling study focuses on the analysis of locomotor central pattern generators (CPGs) composed of conditionally bursting half-centers coupled with reciprocal inhibition and receiving independent external drives. We show that this CPG framework can operate in several regimes consistent with seemingly contradictory experimental data. In each regime, we study how intrinsic dynamics and phase-switching mechanisms control oscillation frequency and phase durations. Our results provide insights into the organization of spinal circuits controlling locomotion.

The Bobath concept - a model to illustrate clinical practice

Background and purpose: The model of Bobath clinical practice provides a framework identifying the unique aspects of the Bobath concept in terms of contemporary neurological rehabilitation. The utilisation of a framework to illustrate the clinical application of the Bobath concept provides the basis for a common understanding with respect to Bobath clinical practice, education, and research. The development process culminating in the model of Bobath clinical practice is described. Case description: The use of the model in clinical practice is illustrated using two cases: a client with a chronic incomplete spinal cord injury and a client with a stroke. Discussion: This article describes the clinical application of the Bobath concept in terms of the integration of posture and movement with respect to the quality of task performance, applying the Model of Bobath Clinical Practice. Facilitation, a key aspect of Bobath clinical practice, was utilised to positively affect motor control and perception in two clients with impairment-related movement problems due to neurological pathology and associated activity limitations and participation restrictions - the outcome measures used to reflect the individual clinical presentation. Implications for Rehabilitation The model of Bobath clinical practice provides a framework identifying the unique aspects of the Bobath-concept. The model of Bobath clinical practice provides the basis for a common understanding with respect to Bobath clinical practice, education, and research. The clinical application of the Bobath-concept highlights the integration of posture and movement with respect to the quality of task performance. Facilitation, a key aspect of Bobath clinical practice, positively affects motor control, and perception.

Transplants of Neurotrophin-Producing Autologous Fibroblasts Promote Recovery of Treadmill Stepping in the Acute, Sub-Chronic, and Chronic Spinal Cat

Adult cats show limited spontaneous locomotor capabilities following spinal transection, but recover treadmill stepping with body-weight-supported training. Delivery of neurotrophic factors such as brain-derived neurotrophic factor (BDNF) and neurotrophic factor 3 (NT-3) can substitute for body-weight-supported training, and promotes a similar recovery in a shorter period of time. Autologous cell grafts would negate the need for the immunosuppressive agents currently used with most grafts, but have not shown functional benefits in incomplete spinal cord injury models and have never been tested in complete transection or chronic injury models. In this study, we explored the effects of autologous fibroblasts, prepared from the individual cats and modified to produce BDNF and NT-3, on the recovery of locomotion in acute, sub-chronic and chronic full-transection models of spinal injury. Fourteen female cats underwent complete spinal transection at T11/T12. Cats were separated into four groups: sham graft at the time of injury, and BDNF and NT-3 producing autologous fibroblasts grafted at the time of injury, 2 weeks after injury, or 6 weeks after injury. Kinematics were recorded 3 and 5 weeks after cell graft. Additional kinematic recordings were taken for some cats until 12 weeks post-graft. Eleven of 12 cats with neurotrophin-producing grafts recovered plantar weight-bearing stepping at treadmill speeds from 0.3 to 0.8 m/sec within 5 weeks of grafting, whereas control cats recovered poor quality stepping at low speeds only (≤ 0.4 m/sec). Further, kinematic measures in cats with grafts were closer to pre-transection values than those for controls, and recovery was maintained up to 12 weeks post-grafting. Our results show that not only are autologous neurotrophin-producing grafts effective at promoting recovery of locomotion, but that delayed delivery of neurotrophins does not diminish the therapeutic effect, and may improve outcome.

Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems.

Design process and tools for dynamic neuromechanical models and robot controllers

We present a serial design process with associated tools to select parameter values for a posture and locomotion controller for simulation of a robot. The controller is constructed from dynamic neuron and synapse models and simulated with the open-source neuromechanical simulator AnimatLab 2. Each joint has a central pattern generator (CPG), whose neurons possess persistent sodium channels. The CPG rhythmically inhibits motor neurons that control the servomotor's velocity. Sensory information coordinates the joints in the leg into a cohesive stepping motion. The parameter value design process is intended to run on a desktop computer, and has three steps. First, our tool FEEDBACKDESIGN uses classical control methods to find neural and synaptic parameter values that stably and robustly control servomotor output. This method is fast, testing over 100 parameter value variations per minute. Next, our tool CPGDESIGN generates bifurcation diagrams and phase response curves for the CPG model. This reveals neural and synaptic parameter values that produce robust oscillation cycles, whose phase can be rapidly entrained to sensory feedback. It also designs the synaptic conductance of inter-joint pathways. Finally, to understand sensitivity to parameters and how descending commands affect a leg's stepping motion, our tool SIMSCAN runs batches of neuromechanical simulations with specified parameter values, which is useful for searching the parameter space of a complicated simulation. These design tools are demonstrated on a simulation of a robot, but may be applied to neuromechanical animal models or physical robots as well.

Modular neuromuscular control of human locomotion by central pattern generator

The central pattern generators (CPG) in the spinal cord are thought to be responsible for producing the rhythmic motor patterns during rhythmic activities. For locomotor tasks, this involves much complexity, due to a redundant system of muscle actuators with a large number of highly nonlinear muscles. This study proposes a reduced neural control strategy for the CPG, based on modular organization of the co-active muscles, i.e., muscle synergies. Four synergies were extracted from the EMG data of the major leg muscles of two subjects, during two gait trials each, using non-negative matrix factorization algorithm. A Matsuoka׳s four-neuron CPG model with mutual inhibition, was utilized to generate the rhythmic activation patterns of the muscle synergies, using the hip flexion angle and foot contact force information from the sensory afferents as inputs. The model parameters were tuned using the experimental data of one gait trial, which resulted in a good fitting accuracy (RMSEs between 0.0491 and 0.1399) between the simulation and experimental synergy activations. The model׳s performance was then assessed by comparing its predictions for the activation patterns of the individual leg muscles during locomotion with the relevant EMG data. Results indicated that the characteristic features of the complex activation patterns of the muscles were well reproduced by the model for different gait trials and subjects. In general, the CPG- and muscle synergy-based model was promising in view of its simple architecture, yet extensive potentials for neuromuscular control, e.g., resolving redundancies, distributed and fast control, and modulation of locomotion by simple control signals.

Effect of Treadmill Training Protocols on Locomotion Recovery in Spinalized Rats

Both treadmill training and epidural stimulation can help to reactivate the central pattern generator (CPG) in the spinal cord after a spinal cord injury. However, designing an appropriate training approach and a stimulation profile is still a controversial issue. Since the spinal afferent signals are the input signals of CPG in the spinal cord, it can be concluded that the number of input afferent signals can affect the quality of movement recovery, such a phenomenon is in accordance with Hebbian theory. Therefore, at first in this paper, through some simulation studies on a model of CPGs, the effective influence of increasing the afferent input weight on activating CPG model was certified. Then, the performance of two different types of treadmill training along with epidural stimulation was compared. The numbers of spinal afferents involved during each designed training approach were different. Experiments were conducted on two groups of spinalized rats. Three quantized integer qualitative measures, with 0-2 scales, were envisioned to evaluate the performance of training protocols. According to the experimental results, the assigned scales to the rats using the training approach involving more afferents, the rats have been creeping on a treadmill, was 2. Also, the assigned scales to the rats using the training approach involving less afferents, the rats have been performing bipedal locomotion, was 0 or 1. Such experimental results coincide with achieved simulation results elucidating the effect of increasing the afferent input weights on activating CPG model.

Rehabilitation Strategies after Spinal Cord Injury: Inquiry into the Mechanisms of Success and Failure

Body-weight supported locomotor training (BWST) promotes recovery of load-bearing stepping in lower mammals, but its efficacy in individuals with a spinal cord injury (SCI) is limited and highly dependent on injury severity. While animal models with complete spinal transections recover stepping with step-training, motor complete SCI individuals do not, despite similarly intensive training. In this review, we examine the significant differences between humans and animal models that may explain this discrepancy in the results obtained with BWST. We also summarize the known effects of SCI and locomotor training on the muscular, motoneuronal, interneuronal, and supraspinal systems in human and non-human models of SCI and address the potential causes for failure to translate to the clinic. The evidence points to a deficiency in neuronal activation as the mechanism of failure, rather than muscular insufficiency. While motoneuronal and interneuronal systems cannot be directly probed in humans, the changes brought upon by step-training in SCI animal models suggest a beneficial re-organization of the systems' responsiveness to descending and afferent feedback that support locomotor recovery. The literature on partial lesions in humans and animal models clearly demonstrate a greater dependency on supraspinal input to the lumbar cord in humans than in non-human mammals for locomotion. Recent results with epidural stimulation that activates the lumbar interneuronal networks and/or increases the overall excitability of the locomotor centers suggest that these centers are much more dependent on the supraspinal tonic drive in humans. Sensory feedback shapes the locomotor output in animal models but does not appear to be sufficient to drive it in humans.

Central control of interlimb coordination and speed-dependent gait expression in quadrupeds

KEY POINTS

Quadrupeds express different gaits depending on speed of locomotion. Central pattern generators (one per limb) within the spinal cord generate locomotor oscillations and control limb movements. Neural interactions between these generators define interlimb coordination and gait. We present a computational model of spinal circuits representing four rhythm generators with left-right excitatory and inhibitory commissural and fore-hind inhibitory interactions within the cord. Increasing brainstem drive to all rhythm generators and excitatory commissural interneurons induces an increasing frequency of locomotor oscillations accompanied by speed-dependent gait changes from walk to trot and to gallop and bound. The model closely reproduces and suggests explanations for multiple experimental data, including speed-dependent gait transitions in intact mice and changes in gait expression in mutants lacking certain types of commissural interneurons. The model suggests the possible circuit organization in the spinal cord and proposes predictions that can be tested experimentally.

ABSTRACT

As speed of locomotion is increasing, most quadrupeds, including mice, demonstrate sequential gait transitions from walk to trot and to gallop and bound. The neural mechanisms underlying these transitions are poorly understood. We propose that the speed-dependent expression of different gaits results from speed-dependent changes in the interactions between spinal circuits controlling different limbs and interlimb coordination. As a result, the expression of each gait depends on (1) left-right interactions within the spinal cord mediated by different commissural interneurons (CINs), (2) fore-hind interactions on each side of the spinal cord and (3) brainstem drives to rhythm-generating circuits and CIN pathways. We developed a computational model of spinal circuits consisting of four rhythm generators (RGs) with bilateral left-right interactions mediated by V0 CINs (V0_D and V0_V sub-types) providing left-right alternation, and conditional V3 CINs promoting left-right synchronization. Fore and hind RGs mutually inhibited each other. We demonstrate that linearly increasing excitatory drives to the RGs and V3 CINs can produce a progressive increase in the locomotor speed accompanied by sequential changes of gaits from walk to trot and to gallop and bound. The model closely reproduces and suggests explanations for the speed-dependent gait expression observed in vivo in intact mice and in mutants lacking V0_V or all V0 CINs. Specifically, trot is not expressed after removal of V0_V CINs, and only bound is expressed after removal of all V0 CINs. The model provides important insights into the organization of spinal circuits and neural control of locomotion.

Left-right coordination from simple to extreme conditions during split-belt locomotion in the chronic spinal adult cat

KEY POINTS

Coordination between the left and right sides is essential for dynamic stability during locomotion. The immature or neonatal mammalian spinal cord can adjust to differences in speed between the left and right sides during split-belt locomotion by taking more steps on the fast side. We show that the adult mammalian spinal cord can also adjust its output so that the fast side can take more steps. During split-belt locomotion, only certain parts of the cycle are modified to adjust left-right coordination, primarily those associated with swing onset. When the fast limb takes more steps than the slow limb, strong left-right interactions persist. Therefore, the adult mammalian spinal cord has a remarkable adaptive capacity for left-right coordination, from simple to extreme conditions.

ABSTRACT

Although left-right coordination is essential for locomotion, its control is poorly understood, particularly in adult mammals. To investigate the spinal control of left-right coordination, a spinal transection was performed in six adult cats that were then trained to recover hindlimb locomotion. Spinal cats performed tied-belt locomotion from 0.1 to 1.0 m s^-1 and split-belt locomotion with low to high (1:1.25-10) slow/fast speed ratios. With the left hindlimb stepping at 0.1 m s^-1 and the right hindlimb stepping from 0.2 to 1.0 m s^-1 , 1:1, 1:2, 1:3, 1:4 and 1:5 left-right step relationships could appear. The appearance of 1:2+ relationships was not linearly dependent on the difference in speed between the slow and fast belts. The last step taken by the fast hindlimb displayed longer cycle, stance and swing durations and increased extensor activity, as the slow limb transitioned to swing. During split-belt locomotion with 1:1, 1:2 and 1:3 relationships, the timing of stance onset of the fast limb relative to the slow limb and placement of both limbs at contact were invariant with increasing slow/fast speed ratios. In contrast, the timing of stance onset of the slow limb relative to the fast limb and the placement of both limbs at swing onset were modulated with slow/fast speed ratios. Thus, left-right coordination is adjusted by modifying specific parts of the cycle. Results highlight the remarkable adaptive capacity of the adult mammalian spinal cord, providing insight into spinal mechanisms and sensory signals regulating left-right coordination.

A Review on Locomotor Training after Spinal Cord Injury: Reorganization of Spinal Neuronal Circuits and Recovery of Motor Function

Locomotor training is a classic rehabilitation approach utilized with the aim of improving sensorimotor function and walking ability in people with spinal cord injury (SCI). Recent studies have provided strong evidence that locomotor training of persons with clinically complete, motor complete, or motor incomplete SCI induces functional reorganization of spinal neuronal networks at multisegmental levels at rest and during assisted stepping. This neuronal reorganization coincides with improvements in motor function and decreased muscle cocontractions. In this review, we will discuss the manner in which spinal neuronal circuits are impaired and the evidence surrounding plasticity of neuronal activity after locomotor training in people with SCI. We conclude that we need to better understand the physiological changes underlying locomotor training, use physiological signals to probe recovery over the course of training, and utilize established and contemporary interventions simultaneously in larger scale research studies. Furthermore, the focus of our research questions needs to change from feasibility and efficacy to the following: what are the physiological mechanisms that make it work and for whom? The aforementioned will enable the scientific and clinical community to develop more effective rehabilitation protocols maximizing sensorimotor function recovery in people with SCI.

A biologically based neural system coordinates the joints and legs of a tetrapod

A biologically inspired neural control system has been developed that coordinates a tetrapod trotting gait in the sagittal plane. The developed neuromechanical system is used to explore properties of connections in inter-leg and intra-leg coordination. The neural controller is built with biologically based neurons and synapses, and connections are based on data from literature where available. It is applied to a planar biomechanical model of a rat with 14 joints, each actuated by a pair of antagonistic Hill muscle models. The controller generates tension in the muscles through activation of simulated motoneurons. The hind leg and inter-leg control networks are based on pathways discovered in cat research tuned to the kinematic motions of a rat. The foreleg network was developed by extrapolating analogous pathways from the hind legs. The formulated intra-leg and inter-leg networks properly coordinate the joints and produce motions similar to those of a walking rat. Changing the strength of a single inter-leg connection is sufficient to account for differences in phase timing in different trotting rats.

Sensory control of normal movement and of movement aided by neural prostheses

Signals from sensory receptors in muscles and skin enter the central nervous system (CNS), where they contribute to kinaesthesia and the generation of motor commands. Many lines of evidence indicate that sensory input from skin receptors, muscle spindles and Golgi tendon organs play the predominant role in this regard. Yet in spite of over 100 years of research on this topic, some quite fundamental questions remain unresolved. How does the CNS choose to use the ability to control muscle spindle sensitivity during voluntary movements? Do spinal reflexes contribute usefully to load compensation, given that the feedback gain must be quite low to avoid instability? To what extent do signals from skin stretch receptors contribute? This article provides a brief review of various theories, past and present, that address these questions. To what extent has the knowledge gained resulted in clinical applications? Muscles paralyzed as a result of spinal cord injury or stroke can be activated by electrical stimulation delivered by neuroprostheses. In practice, at most two or three sensors can be deployed on the human body, providing only a small fraction of the information supplied by the tens of thousands of sensory receptors in animals. Most of the neuroprostheses developed so far do not provide continuous feedback control. Instead, they switch from one state to another when signals from their one or two sensors meet pre-set thresholds (finite state control). The inherent springiness of electrically activated muscle provides a crucial form of feedback control that helps smooth the resulting movements. In spite of the dissimilarities, parallels can be found between feedback control in neuroprostheses and in animals and this can provide surprising insights in both directions.

A simple rule for quadrupedal gait generation determined by leg loading feedback: a modeling study

We discovered a specific rule for generating typical quadrupedal gaits (the order of the movement of four legs) through a simulated quadrupedal locomotion, in which unprogrammed gaits (diagonal/lateral sequence walks, left/right-lead canters, and left/right-lead transverse gallops) spontaneously emerged because of leg loading feedbacks to the CPGs hard-wired to produce a default trot. Additionally, all gaits transitioned according to speed, as seen in animals. We have therefore hypothesized that various gaits derive from a trot because of posture control through leg loading feedback. The body tilt on the two support legs of each diagonal pair during trotting was classified into three types (level, tilted up, or tilted down) according to speed. The load difference between the two legs led to the phase difference between their CPGs via the loading feedbacks, resulting in nine gaits (3²: three tilts to the power of two diagonal pairs) including the aforementioned.

Multi-layered multi-pattern CPG for adaptive locomotion of humanoid robots

In this paper, we present an extended mathematical model of the central pattern generator (CPG) in the spinal cord. The proposed CPG model is used as the underlying low-level controller of a humanoid robot to generate various walking patterns. Such biological mechanisms have been demonstrated to be robust in locomotion of animal. Our model is supported by two neurophysiological studies. The first study identified a neural circuitry consisting of a two-layered CPG, in which pattern formation and rhythm generation are produced at different levels. The second study focused on a specific neural model that can generate different patterns, including oscillation. This neural model was employed in the pattern generation layer of our CPG, which enables it to produce different motion patterns-rhythmic as well as non-rhythmic motions. Due to the pattern-formation layer, the CPG is able to produce behaviors related to the dominating rhythm (extension/flexion) and rhythm deletion without rhythm resetting. The proposed multi-layered multi-pattern CPG model (MLMP-CPG) has been deployed in a 3D humanoid robot (NAO) while it performs locomotion tasks. The effectiveness of our model is demonstrated in simulations and through experimental results.

Either brain-derived neurotrophic factor or neurotrophin-3 only neurotrophin-producing grafts promote locomotor recovery in untrained spinalized cats

Background. Transplants of cellular grafts expressing a combination of 2 neurotrophic factors, brain-derived neurotrophic factor (BDNF) and neurotrophin-3 (NT-3) have been shown to promote and enhance locomotor recovery in untrained spinalized cats. Based on the time course of recovery and the absence of axonal growth through the transplants, we hypothesized that recovery was due to neurotrophin-mediated plasticity within the existing locomotor circuitry of the lumbar cord. Since BDNF and NT-3 have different effects on axonal sprouting and synaptic connectivity/strengthening, it becomes important to ascertain the contribution of each individual neurotrophins to recovery. Objective. We studied whether BDNF or NT-3 only producing cellular grafts would be equally effective at restoring locomotion in untrained spinal cats. Methods. Rat fibroblasts secreting one of the 2 neurotrophins were grafted into the T12 spinal transection site of adult cats. Four cats in each group (BDNF alone or NT-3 alone) were evaluated. Locomotor recovery was tested on a treadmill at 3 and 5 weeks post-transection/grafting. Results. Animals in both groups were capable of plantar weight-bearing stepping at speed up to 0.8 m/s as early as 3 weeks and locomotor capabilities were similar at 3 and 5 weeks for both types of graft. Conclusions. Even without locomotor training, either BDNF or NT-3 only producing grafts promote locomotor recovery in complete spinal animals. More clinically applicable delivery methods need to be developed.

Speed-dependent modulation of phase variations on a step-by-step basis and its impact on the consistency of interlimb coordination during quadrupedal locomotion in intact adult cats

It is well established that stance duration changes more than swing duration for a given change in cycle duration. Small variations in cycle duration are also observed at any given speed on a step-by-step basis. To evaluate the step-by-step effect of speed on phase variations, we measured the slopes of the linear regressions between the phases (i.e., stance, swing) and cycle duration during individual episodes at different treadmill speeds in five adult cats. We also determined the pattern of dominance, defined as the phase that varies most with cycle duration. We found a significant effect of speed on hindlimb phase variations, with significant differences observed between the slowest speed of 0.3 m/s compared with faster speeds. Moreover, although patterns of phase dominance were primarily stance/extensor dominated at the slowest speeds, as speed increased the patterns were increasingly categorized as covarying, whereby both stance/extensor and swing/flexor phases changed in approximately equal proportion with cycle duration. Speed significantly affected the relative duration of support periods as well as interlimb phasing between homolateral and diagonal pairs of limbs but not between homologous pairs of limbs. Speed also significantly affected the consistency of interlimb coordination on a step-by-step basis, being less consistent at the slowest speed of 0.3 m/s compared with faster speeds. We found a strong linear relationship between hindlimb phase variations and the consistency of interlimb coordination. Therefore, results show that phase variations on a step-by-step basis are modulated by speed, which appears to influence the consistency of interlimb coordination.

Contributions of phase resetting and interlimb coordination to the adaptive control of hindlimb obstacle avoidance during locomotion in rats: a simulation study

Obstacle avoidance during locomotion is essential for safe, smooth locomotion. Physiological studies regarding muscle synergy have shown that the combination of a small number of basic patterns produces the large part of muscle activities during locomotion and the addition of another pattern explains muscle activities for obstacle avoidance. Furthermore, central pattern generators in the spinal cord are thought to manage the timing to produce such basic patterns. In the present study, we investigated sensory-motor coordination for obstacle avoidance by the hindlimbs of the rat using a neuromusculoskeletal model. We constructed the musculoskeletal part of the model based on empirical anatomical data of the rat and the nervous system model based on the aforementioned physiological findings of central pattern generators and muscle synergy. To verify the dynamic simulation by the constructed model, we compared the simulation results with kinematic and electromyographic data measured during actual locomotion in rats. In addition, we incorporated sensory regulation models based on physiological evidence of phase resetting and interlimb coordination and examined their functional roles in stepping over an obstacle during locomotion. Our results show that the phase regulation based on interlimb coordination contributes to stepping over a higher obstacle and that based on phase resetting contributes to quick recovery after stepping over the obstacle. These results suggest the importance of sensory regulation in generating successful obstacle avoidance during locomotion.

Pursuit of food versus pursuit of information in a Markovian perception-action loop model of foraging

Efficient coding, redundancy reduction, and other information theoretic optimization principles have successfully explained the organization of many biological phenomena, from the physiology of sensory receptive fields to the variability of certain DNA sequence ensembles. Here we examine the hypothesis that behavioral strategies that are optimal for survival must necessarily involve efficient information processing, and ask whether there can be circumstances in which deliberately sacrificing some information can lead to higher utility? To this end, we present an analytically tractable model for a particular instance of a perception-action loop: a creature searching for a randomly moving food source confined to a 1D ring world. The model incorporates the statistical structure of the creature's world, the effects of the creature's actions on that structure, and the creature's strategic decision process. The underlying model takes the form of a Markov process on an infinite dimensional state space. To analyze it we construct an exact coarse graining that reduces the model to a Markov process on a finite number of "information states". This mathematical technique allows us to make quantitative comparisons between the performance of an information-theoretically optimal strategy with other candidate search strategies on a food gathering task. We find that 1. Information optimal search does not necessarily optimize utility (expected food gain). 2. The rank ordering of search strategies by information performance does not predict their ordering by expected food obtained. 3. The relative advantage of different strategies depends on the statistical structure of the environment, in particular the variability of motion of the source. We conclude that there is no simple relationship between information and utility. Even in the absence of information processing costs or bandwidth constraints, behavioral optimality does not imply information efficiency, nor is there a simple tradeoff between the two objectives of gaining information about a food source versus obtaining the food itself. For a wide range of values of the food source's movement parameter, the strategy of collecting the most information possible about the unknown source location carries an ineliminable structural cost, leading to a situation in which a foraging creature could actually choose to be less well-informed while simultaneously being, on average, better fed.

Motoneuronal and muscle synergies involved in cat hindlimb control during fictive and real locomotion: a comparison study

We compared the activity profiles and synergies of spinal motoneurons recorded during fictive locomotion evoked in immobilized decerebrate cat preparations by midbrain stimulation to the activity profiles and synergies of the corresponding hindlimb muscles obtained during forward level walking in cats. The fictive locomotion data were collected in the Spinal Cord Research Centre, University of Manitoba, and provided by Dr. David McCrea; the real locomotion data were obtained in the laboratories of M. A. Lemay and B. I. Prilutsky. Scatterplot representation and minimum spanning tree clustering algorithm were used to identify the possible motoneuronal and muscle synergies operating during both fictive and real locomotion. We found a close similarity between the activity profiles and synergies of motoneurons innervating one-joint muscles during fictive locomotion and the profiles and synergies of the corresponding muscles during real locomotion. However, the activity patterns of proximal nerves controlling two-joint muscles, such as posterior biceps and semitendinosus (PBSt) and rectus femoris (RF), were not uniform in fictive locomotion preparations and differed from the activity profiles of the corresponding two-joint muscles recorded during forward level walking. Moreover, the activity profiles of these nerves and the corresponding muscles were unique and could not be included in the synergies identified in fictive and real locomotion. We suggest that afferent feedback is involved in the regulation of locomotion via motoneuronal synergies controlled by the spinal central pattern generator (CPG) but may also directly affect the activity of motoneuronal pools serving two-joint muscles (e.g., PBSt and RF). These findings provide important insights into the organization of the spinal CPG in mammals, the motoneuronal and muscle synergies engaged during locomotion, and their afferent control.