The neuromechanical tuning hypothesis

Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

The nervous system predicts and executes complex motion of body segments actuated by the coordinated action of muscles. When a stroke or other traumatic injury disrupts neural processing, the impeded behavior has not only kinematic but also kinetic attributes that require interpretation. Biomechanical models could allow medical specialists to observe these dynamic variables and instantaneously diagnose mobility issues that may otherwise remain unnoticed. However, the real-time and subject-specific dynamic computations necessitate the optimization these simulations. In this study, we explored the effects of intrinsic viscoelasticity, choice of numerical integration method, and decrease in sampling frequency on the accuracy and stability of the simulation. The bipedal model with 17 rotational degrees of freedom (DOF)-describing hip, knee, ankle, and standing foot contact-was instrumented with viscoelastic elements with a resting length in the middle of the DOF range of motion. The accumulation of numerical errors was evaluated in dynamic simulations using swing-phase experimental kinematics. The relationship between viscoelasticity, sampling rates, and the integrator type was evaluated. The optimal selection of these three factors resulted in an accurate reconstruction of joint kinematics (err < 1%) and kinetics (err < 5%) with increased simulation time steps. Notably, joint viscoelasticity reduced the integration errors of explicit methods and had minimal to no additional benefit for implicit methods. Gained insights have the potential to improve diagnostic tools and accurize real-time feedback simulations used in the functional recovery of neuromuscular diseases and intuitive control of modern prosthetic solutions.

Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

The nervous system predicts and executes complex motion of body segments actuated by the coordinated action of muscles. When a stroke or other traumatic injury disrupts neural processing, the impeded behavior has not only kinematic but also kinetic attributes that require interpretation. Biomechanical models could allow medical specialists to observe these dynamic variables and instantaneously diagnose mobility issues that may otherwise remain unnoticed. However, the real-time and subject-specific dynamic computations necessitate the optimization these simulations. In this study, we explored the effects of intrinsic viscoelasticity, choice of numerical integration method, and decrease in sampling frequency on the accuracy and stability of the simulation. The bipedal model with 17 rotational degrees of freedom (DOF)-describing hip, knee, ankle, and standing foot contact-was instrumented with viscoelastic elements with a resting length in the middle of the DOF range of motion. The accumulation of numerical errors was evaluated in dynamic simulations using swing-phase experimental kinematics. The relationship between viscoelasticity, sampling rates, and the integrator type was evaluated. The optimal selection of these three factors resulted in an accurate reconstruction of joint kinematics (err < 1%) and kinetics (err < 5%) with increased simulation time steps. Notably, joint viscoelasticity reduced the integration errors of explicit methods and had minimal to no additional benefit for implicit methods . Gained insights have the potential to improve diagnostic tools and accurize real-time feedback simulations used in the functional recovery of neuromuscular diseases and intuitive control of modern prosthetic solutions.

A neuromuscular model of human locomotion combines spinal reflex circuits with voluntary movements

Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.

A model for the transfer of control from the brain to the spinal cord through synaptic learning

The spinal cord is essential to the control of locomotion in legged animals and humans. However, the actual circuitry of the spinal controller remains only vaguely understood. Here we approach this problem from the viewpoint of learning. More precisely, we assume the circuitry evolves through the transfer of control from the brain to the spinal cord, propose a specific learning mechanism for this transfer based on the error between the cord and brain contributions to muscle control, and study the resulting structure of the spinal controller in a simplified neuromuscular model of human locomotion. The model focuses on the leg rebound behavior in stance and represents the spinal circuitry with 150 muscle reflexes. We find that after learning a spinal controller has evolved that produces leg rebound motions in the absence of a central brain input with only three structural reflex groups. These groups contain individual reflexes well known from physiological experiments but thought to serve separate purposes in the control of human locomotion. Our results suggest a more holistic interpretation of the role of individual sensory projections in spinal networks than is common. In addition, we discuss potential neural correlates for the proposed learning mechanism that may be probed in experiments. Together with such experiments, neuromuscular models of spinal learning likely will become effective tools for uncovering the structure and development of the spinal control circuitry.

From standing balance to walking: A single control structure for a continuum of gaits

This article presents a control algorithm framework with which a bipedal robot can perform a variety of gaits by only modifying a small set of control parameters. The controller drives a number of variables, called non-emergent variables, to their desired trajectories resulting in a desired emergent walking gait. While the non-emergent variables remain the same independent of the gait, their desired trajectories are functions of a small set of control parameters that change as a function of the desired gait. This control algorithm has been tested on the humanoid robot COMAN, where different gaits including standing balance, stepping in place, periodic walking gaits with different velocities, as well as gait switching are demonstrated in experiments.

Analytical CPG model driven by limb velocity input generates accurate temporal locomotor dynamics

The ability of vertebrates to generate rhythm within their spinal neural networks is essential for walking, running, and other rhythmic behaviors. The central pattern generator (CPG) network responsible for these behaviors is well-characterized with experimental and theoretical studies, and it can be formulated as a nonlinear dynamical system. The underlying mechanism responsible for locomotor behavior can be expressed as the process of leaky integration with resetting states generating appropriate phases for changing body velocity. The low-dimensional input to the CPG model generates the bilateral pattern of swing and stance modulation for each limb and is consistent with the desired limb speed as the input command. To test the minimal configuration of required parameters for this model, we reduced the system of equations representing CPG for a single limb and provided the analytical solution with two complementary methods. The analytical and empirical cycle durations were similar (R 2 = 0.99) for the full range of walking speeds. The structure of solution is consistent with the use of limb speed as the input domain for the CPG network. Moreover, the reciprocal interaction between two leaky integration processes representing a CPG for two limbs was sufficient to capture fundamental experimental dynamics associated with the control of heading direction. This analysis provides further support for the embedded velocity or limb speed representation within spinal neural pathways involved in rhythm generation.

A Systematic Review on Muscle Synergies: From Building Blocks of Motor Behavior to a Neurorehabilitation Tool

The central nervous system (CNS) is believed to utilize specific predefined modules, called muscle synergies (MS), to accomplish a motor task. Yet questions persist about how the CNS combines these primitives in different ways to suit the task conditions. The MS hypothesis has been a subject of debate as to whether they originate from neural origins or nonneural constraints. In this review article, we present three aspects related to the MS hypothesis: (1) the experimental and computational evidence in support of the existence of MS, (2) algorithmic approaches for extracting them from surface electromyography (EMG) signals, and (3) the possible role of MS as a neurorehabilitation tool. We note that recent advances in computational neuroscience have utilized the MS hypothesis in motor control and learning. Prospective advances in clinical, medical, and engineering sciences and in fields such as robotics and rehabilitation stand to benefit from a more thorough understanding of MS.

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.

Neurophysiology and neural engineering: a review

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.

The role of stride frequency for walk-to-run transition in humans

It remains unclear why humans spontaneously shift from walking to running at a certain point during locomotion at gradually increasing velocity. We show that a calculated walk-to-run transition stride frequency (70.6 ± 3.2 strides min⁻¹) agrees with a transition stride frequency (70.8 ± 3.1 strides min⁻¹) predicted from the two stride frequencies applied during treadmill walking and running at freely chosen velocities and freely chosen stride frequencies. The agreement is based on Bland and Altman’s statistics. We found no essential mean relative difference between the two transition frequencies, i.e. −0.5% ± 4.2%, as well as limits of agreement of −8.7% and 7.7%. The particular two freely chosen stride frequencies used for prediction are considered behavioural attractors. Gait is predicted to be shifted from walking to running when the stride frequency starts getting closer to the running attractor than to the walking attractor. In particular, previous research has focussed on transition velocity and optimisation theories based on minimisation of, e.g., energy turnover or biomechanical loadings of the legs. Conversely, our data support that the central phenomenon of walk-to-run transition during human locomotion could be influenced by behavioural attractors in the form of stride frequencies spontaneously occurring during behaviourally unrestricted gait conditions of walking and running.

Biomechanical Constraints Underlying Motor Primitives Derived from the Musculoskeletal Anatomy of the Human Arm

Neural control of movement can only be realized though the interaction between the mechanical properties of the limb and the environment. Thus, a fundamental question is whether anatomy has evolved to simplify neural control by shaping these interactions in a beneficial way. This inductive data-driven study analyzed the patterns of muscle actions across multiple joints using the musculoskeletal model of the human upper limb. This model was used to calculate muscle lengths across the full range of motion of the arm and examined the correlations between these values between all pairs of muscles. Musculoskeletal coupling was quantified using hierarchical clustering analysis. Muscle lengths between multiple pairs of muscles across multiple postures were highly correlated. These correlations broadly formed two proximal and distal groups, where proximal muscles of the arm were correlated with each other and distal muscles of the arm and hand were correlated with each other, but not between groups. Using hierarchical clustering, between 11 and 14 reliable muscle groups were identified. This shows that musculoskeletal anatomy does indeed shape the mechanical interactions by grouping muscles into functional clusters that generally match the functional repertoire of the human arm. Together, these results support the idea that the structure of the musculoskeletal system is tuned to solve movement complexity problem by reducing the dimensionality of available solutions.

A Perturbation Mechanism for Investigations of Phase-Dependent Behavior in Human Locomotion

Bipedal locomotion is a popular area of study across multiple fields (e.g., biomechanics, neuroscience and robotics). Different hypotheses and models have tried explaining how humans achieve stable locomotion. Perturbations that produce shifts in the nominal periodic orbit of the joint kinematics during locomotion could inform about the manner in which the human neuromechanics represent the phase of gait. Ideally, this type of perturbation would modify the progression of the human subject through the gait cycle without deviating from the nominal kinematic orbits of the leg joints. However, there is a lack of publicly available experimental data with this type of perturbation. This paper presents the design and validation of a perturbation mechanism and an experimental protocol capable of producing phase-shifting perturbations of the gait cycle. The effects of this type of perturbation on the gait cycle are statistically quantified and analyzed in order to show that a clean phase shift in the gait cycle was achieved. The data collected during these experiments will be publicly available for the scientific community to test different hypotheses and models of human locomotion.

Motor programme activating therapy influences adaptive brain functions in multiple sclerosis: clinical and MRI study

There is still little scientific evidence for the efficacy of neurofacilitation approaches and their possible influence on brain plasticity and adaptability. In this study, the outcome of a new kind of neurofacilitation approach, motor programme activating therapy (MPAT), was evaluated on the basis of a set of clinical functions and with MRI. Eighteen patients were examined four times with standardized clinical tests and diffusion tensor imaging to monitor changes without therapy, immediately after therapy and 1 month after therapy. Moreover, the strength of effective connectivity was analysed before and after therapy. Patients underwent a 1-h session of MPAT twice a week for 2 months. The data were analysed by nonparametric tests of association and were subsequently statistically evaluated. The therapy led to significant improvement in clinical functions, significant increment of fractional anisotropy and significant decrement of mean diffusivity, and decrement of effective connectivity at supplementary motor areas was observed immediately after the therapy. Changes in clinical functions and diffusion tensor images persisted 1 month after completing the programme. No statistically significant changes in clinical functions and no differences in MRI-diffusion tensor images were observed without physiotherapy. Positive immediate and long-term effects of MPAT on clinical and brain functions, as well as brain microstructure, were confirmed.

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.

On voluntary rhythmic leg movement behaviour and control during pedalling

The overall purpose of the present dissertation was to contribute to the understanding of voluntary human rhythmic leg movement behaviour and control. This was achieved by applying pedalling as a movement model and exposing healthy and recreationally active individuals as well as trained cyclists to for example cardiopulmonary and mechanical loading, fatiguing exercise, and heavy strength training. As a part of the background, the effect of pedalling frequency on diverse relevant biomechanical, physiological, and psychophysiological variables as well as on performance was initially explored. Freely chosen pedalling frequency is considerably higher than the energetically optimal pedalling frequency. This has been shown by others and was confirmed in the present work. As a result, pedal force is relatively low while rates of VO2 and energy turnover are relatively high during freely chosen pedalling as compared to a condition where a lower and more efficient pedalling frequency is imposed. The freely chosen pedalling frequency was in the present work, and by others, found to most likely be less advantageous than the lower energetically optimal pedalling frequency with respect to performance during intensive cycling following prolonged submaximal cycling. This stimulates the motivation to understand the behaviour and control of the freely chosen pedalling frequency during cycling. Freely chosen pedalling frequency was in the present work shown to be highly individual. In addition, the pedalling frequency was shown to be steady in a longitudinal perspective across 12 weeks. Further, it was shown to be unaffected by both fatiguing hip extension exercise and hip flexion exercise as well as by increased loading on the cardiopulmonary system at constant mechanical loading, and vice versa. Based on this, the freely chosen pedalling frequency is considered to be characterised as a highly individual, steady, and robust innate voluntary motor rhythm under primary influence of central pattern generators. The last part of the characterisation is largely based on, and supported by, work of other researchers in the field. Despite the robustness of the freely chosen pedalling frequency, it may be affected by some particular factors. As an example from the present work, freely chosen pedalling frequency during treadmill cycling increased by on average 15 to 17 rpm when power output was increased from a value corresponding to 86% and up to 165% of Wmax . This phenomenon is supported by other studies. As another example from the present work, freely chosen pedalling frequency decreased by on average 9 to 14 rpm following heavy strength training that involved both hip extension and hip flexion. Further, the present work suggested that the latter phenomenon occurred within the first week of training and was caused by in particular the hip extension strength training rather than the hip flexion strength training. The fast response to the strength training indicated that neural adaptations presumably caused the observed changes in movement behaviour. The internal organisation of the central pattern generator is by some other researchers in the field considered to be functionally separated into two components, in which, one is responsible for movement frequency and another is responsible for movement pattern. For the present dissertation, the freely chosen pedalling frequency was considered to reflect the rhythmic movement frequency of the voluntary rhythmic leg movement of pedalling. The tangential pedal force profile was considered to reflect the rhythmic movement pattern. The present work showed that fatiguing hip flexion exercise in healthy and recreationally active individuals modified the tangential pedal force profile during cycling at a pre-set target pedalling frequency in a way that the minimum tangential pedal force became more negative, the maximum tangential pedal force increased, and the phase with negative tangential pedal force increased. In other words, the legs were "actively lifted" to a lesser extent in the upstroke phase. Fatiguing hip extension exercise did not have that effect. And none of the fatiguing exercises affected the freely chosen pedalling frequency. The present work furthermore showed that the primary effect of hip extension strength training was that it decreased the freely chosen pedalling frequency. An interpretation of this could be that the hip extension strength training, in particular, influenced the output from the component of the central pattern generator that may be responsible for rhythmic movement frequency.

Locomotor patterns in cerebellar ataxia

Several studies have demonstrated how cerebellar ataxia (CA) affects gait, resulting in deficits in multijoint coordination and stability. Nevertheless, how lesions of cerebellum influence the locomotor muscle pattern generation is still unclear. To better understand the effects of CA on locomotor output, here we investigated the idiosyncratic features of the spatiotemporal structure of leg muscle activity and impairments in the biomechanics of CA gait. To this end, we recorded the electromyographic (EMG) activity of 12 unilateral lower limb muscles and analyzed kinematic and kinetic parameters of 19 ataxic patients and 20 age-matched healthy subjects during overground walking. Neuromuscular control of gait in CA was characterized by a considerable widening of EMG bursts and significant temporal shifts in the center of activity due to overall enhanced muscle activation between late swing and mid-stance. Patients also demonstrated significant changes in the intersegmental coordination, an abnormal transient in the vertical ground reaction force and instability of limb loading at heel strike. The observed abnormalities in EMG patterns and foot loading correlated with the severity of pathology [International Cooperative Ataxia Rating Scale (ICARS), a clinical ataxia scale] and the changes in the biomechanical output. The findings provide new insights into the physiological role of cerebellum in optimizing the duration of muscle activity bursts and the control of appropriate foot loading during locomotion.

Evidence for a time-invariant phase variable in human ankle control

Human locomotion is a rhythmic task in which patterns of muscle activity are modulated by state-dependent feedback to accommodate perturbations. Two popular theories have been proposed for the underlying embodiment of phase in the human pattern generator: a time-dependent internal representation or a time-invariant feedback representation (i.e., reflex mechanisms). In either case the neuromuscular system must update or represent the phase of locomotor patterns based on the system state, which can include measurements of hundreds of variables. However, a much simpler representation of phase has emerged in recent designs for legged robots, which control joint patterns as functions of a single monotonic mechanical variable, termed a phase variable. We propose that human joint patterns may similarly depend on a physical phase variable, specifically the heel-to-toe movement of the Center of Pressure under the foot. We found that when the ankle is unexpectedly rotated to a position it would have encountered later in the step, the Center of Pressure also shifts forward to the corresponding later position, and the remaining portion of the gait pattern ensues. This phase shift suggests that the progression of the stance ankle is controlled by a biomechanical phase variable, motivating future investigations of phase variables in human locomotor control.

Sensory systems in the control of movement

Animal movement is immensely varied, from the simplest reflexive responses to the most complex, dexterous voluntary tasks. Here, we focus on the control of movement in mammals, including humans. First, the sensory inputs most closely implicated in controlling movement are reviewed, with a focus on somatosensory receptors. The response properties of the large muscle receptors are examined in detail. The role of sensory input in the control of movement is then discussed, with an emphasis on the control of locomotion. The interaction between central pattern generators and sensory input, in particular in relation to stretch reflexes, timing, and pattern forming neuronal networks is examined. It is proposed that neural signals related to bodily velocity form the basic descending command that controls locomotion through specific and well-characterized relationships between muscle activation, step cycle phase durations, and biomechanical outcomes. Sensory input is crucial in modulating both the timing and pattern forming parts of this mechanism.

Motor circuit-specific burst patterns drive different muscle and behavior patterns

In the isolated CNS, different modulatory inputs can enable one motor network to generate multiple output patterns. Thus far, however, few studies have established whether different modulatory inputs also enable a defined network to drive distinct muscle and movement patterns in vivo, much as they enable these distinctions in behavioral studies. This possibility is not a foregone conclusion, because additional influences present in vivo (e.g., sensory feedback, hormonal modulation) could alter the motor patterns. Additionally, rhythmic neuronal activity can be transformed into sustained muscle contractions, particularly in systems with slow muscle dynamics, as in the crab (Cancer borealis) stomatogastric system used here. We assessed whether two different versions of the biphasic (protraction, retraction) gastric mill (chewing) rhythm, triggered in the isolated stomatogastric system by the modulatory ventral cardiac neurons (VCNs) and postoesophageal commissure (POC) neurons, drive different muscle and movement patterns. One distinction between these rhythms is that the lateral gastric (LG) protractor motor neuron generates tonic bursts during the VCN rhythm, whereas its POC-rhythm bursts are divided into fast, rhythmic burstlets. Intracellular muscle fiber recordings and tension measurements show that the LG-innervated muscles retain the distinct VCN-LG and POC-LG neuron burst structures. Moreover, endoscope video recordings in vivo, during VCN-triggered and POC-triggered chewing, show that the lateral teeth protraction movements exhibit the same, distinct protraction patterns generated by LG in the isolated nervous system. Thus, the multifunctional nature of an identified motor network in the isolated CNS can be preserved in vivo, where it drives different muscle activity and movement patterns.

The flexion synergy, mother of all synergies and father of new models of gait

Recently there has been a growing interest in the modular organization of leg movements, in particular those related to locomotion. One of the basic modules involves the flexion of the leg during swing and it was shown that this module is already present in neonates (Dominici et al., 2011). In this paper, we question how these finding build upon the original work by Sherrington, who proposed that the flexor reflex is the basic building block of flexion during swing phase. Similarly, the relation between the flexor reflex and the withdrawal reflex modules of Schouenborg and Weng (1994) will be discussed. It will be argued that there is large overlap between these notions on modules and the older concepts of reflexes. In addition, it will be shown that there is a great flexibility in the expression of some of these modules during gait, thereby allowing for a phase-dependent modulation of the appropriate responses. In particular, the end of the stance phase is a period when the flexor synergy is facilitated. It is proposed that this is linked to the activation of circuitry that is responsible for the generation of locomotor patterns (CPG, “central pattern generator”). More specifically, it is suggested that the responses in that period relate to the activation of a flexor burst generator. The latter structure forms the core of a new asymmetric model of the CPG. This activation is controlled by afferent input (facilitation by a broad range of afferents, suppression by load afferent input). Meanwhile, many of these physiologic features have found their way in the control of very flexible walking bipedal robots.

Smooth changes in the EMG patterns during gait transitions under body weight unloading

During gradual speed changes, humans exhibit a sudden discontinuous switch from walking to running at a specific speed, and it has been suggested that different gaits may be associated with different functioning of neuronal networks. In this study we recorded the EMG activity of leg muscles at slow increments and decrements in treadmill belt speed and at different levels of body weight unloading. In contrast to normal walking at 1 g, at lower levels of simulated gravity (<0.4 g) the transition between walking and running was generally gradual, without systematic abrupt changes in either intensity or timing of EMG patterns. This phenomenon depended to a limited extent on the gravity simulation technique, although the exact level of the appearance of smooth transitions (0.4-0.6 g) tended to be lower for the vertical than for the tilted body weight support system. Furthermore, simulations performed with a half-center oscillator neuromechanical model showed that the abruptness of motor patterns at gait transitions at 1 g could be predicted from the distinct parameters anchored already in the normal range of walking and running speeds, whereas at low gravity levels the parameters of the model were similar for the two human gaits. A lack of discontinuous changes in the pattern of speed-dependent locomotor characteristics in a hypogravity environment is consistent with the idea of a continuous shift in the state of a given set of central pattern generators, rather than the activation of a separate set of central pattern generators for each distinct gait.

Chapter 10--a hierarchical perspective on rhythm generation for locomotor control

The control of locomotion is a complex dynamic task solved with apparent ease by our body. How this is accomplished still remains an intriguing mystery. This chapter first describes classical and recent findings relevant to understanding the complexity of the question on the verge of several fields of neurophysiology, biomechanics, and computational neuroscience. Then, control of locomotion is analyzed with numerical simulations to reveal some basic characteristics responsible for modulation of the locomotor rhythm and high-level control of steering in the whole animal. In this study, the concept of a central pattern generator (CPG) for controlling locomotor rhythm first proposed by Brown was implemented in a "simple" model with bilateral half-center oscillators consisting of reciprocally organized integrators. The parameters of the CPG were determined by the process of optimization of its phase-duration characteristic that satisfies biomechanical requirements of the overground locomotion. The general finding of this study is that the modality of the control signal that drives CPGs for each limb corresponds to the desired speed of forward progression. This supports the idea that the descending and sensory feedback inputs to the spinal CPG are combined to produce a high-level control signal that sets forward velocity. The same mechanism may be responsible for the control of steering by generating a differential input of speed commands to different limbs.

Neuromechanical simulation

The importance of the interaction between the body and the brain for the control of behavior has been recognized in recent years with the advent of neuromechanics, a field in which the coupling between neural and biomechanical processes is an explicit focus. A major tool used in neuromechanics is simulation, which connects computational models of neural circuits to models of an animal's body situated in a virtual physical world. This connection closes the feedback loop that links the brain, the body, and the world through sensory stimuli, muscle contractions, and body movement. Neuromechanical simulations enable investigators to explore the dynamical relationships between the brain, the body, and the world in ways that are difficult or impossible through experiment alone. Studies in a variety of animals have permitted the analysis of extremely complex and dynamic neuromechanical systems, they have demonstrated that the nervous system functions synergistically with the mechanical properties of the body, they have examined hypotheses that are difficult to test experimentally, and they have explored the role of sensory feedback in controlling complex mechanical systems with many degrees of freedom. Each of these studies confronts a common set of questions: (i) how to abstract key features of the body, the world and the CNS in a useful model, (ii) how to ground model parameters in experimental reality, (iii) how to optimize the model and identify points of sensitivity and insensitivity, and (iv) how to share neuromechanical models for examination, testing, and extension by others.

Fast muscle responses to an unexpected foot-in-hole scenario, evoked in the context of prior knowledge of the potential perturbation

This study investigated the effect of prior knowledge of the potential loss of support during walking on muscle responses to the potential perturbation. Four conditions were tested; non-instructed control (NC), non-instructed perturbed (NP), instructed control (IC) and instructed perturbed (IP). Participants were perturbed by having them step into a hidden hole (8.5 cm) in a walkway during the NP and IP trials. Participants had no prior knowledge of the potential perturbation under the NC and NP conditions, but under the instructed conditions, participants were informed that there might be a hole in the walkway. A cautious landing strategy was observed in the IC trials. The participants exhibited flat-footed landings (plantar angle: NC: 13.7 +/- 2.8 degrees; IC: 8.5 +/- 5.2 degrees) and a prolonged double support phase (NC: 138 +/- 18 ms; IC: 161 +/- 17 ms) when they had prior knowledge of the possible hole. When the participants encountered a hole, we saw triggering of fast muscle responses in the ipsilateral plantarflexors and knee extensor, as well as in the contralateral dorsiflexors and knee flexors. This pattern was interpreted as a stop walking synergy. The opposite muscle activation pattern, which was thought of as a resume walking synergy, was induced when no hole was presented and actual foot contact occurred at the expected instant. The latencies between the onsets of muscle responses and the expected heel contact were shorter under the IP condition than under the NP condition (ipsilateral soleus: NP: 78 +/- 13 ms, IP: 64 +/- 14 ms; contralateral biceps femoris: NP: 94 +/- 25 ms; IP: 76 +/- 17 ms). Our results demonstrate that reactive muscle responses to perturbations depend on the anticipatory state with respect to potential perturbations.

A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities

While neuroscientists identify increasingly complex neural circuits that control animal and human gait, biomechanists find that locomotion requires little control if principles of legged mechanics are heeded that shape and exploit the dynamics of legged systems. Here, we show that muscle reflexes could be vital to link these two observations. We develop a model of human locomotion that is controlled by muscle reflexes which encode principles of legged mechanics. Equipped with this reflex control, we find this model to stabilize into a walking gait from its dynamic interplay with the ground, reproduce human walking dynamics and leg kinematics, tolerate ground disturbances, and adapt to slopes without parameter interventions. In addition, we find this model to predict some individual muscle activation patterns known from walking experiments. The results suggest not only that the interplay between mechanics and motor control is essential to human locomotion, but also that human motor output could for some muscles be dominated by neural circuits that encode principles of legged mechanics.

Feedback from peripheral musculature to central pattern generator in the neurogenic heart of the crab Callinectes sapidus: role of mechanosensitive dendrites

The neurogenic heart of decapod crustaceans is a very simple, self-contained, model central pattern generator (CPG)-effector system. The CPG, the nine-neuron cardiac ganglion (CG), is embedded in the myocardium itself; it generates bursts of spikes that are transmitted by the CG's five motor neurons to the periphery of the system, the myocardium, to produce its contractions. Considerable evidence suggests that a CPG-peripheral loop is completed by a return feedback pathway through which the contractions modify, in turn, the CG motor pattern. One likely pathway is provided by dendrites, presumably mechanosensitive, that the CG neurons project into the adjacent myocardial muscle. Here we have tested the role of this pathway in the heart of the blue crab, Callinectes sapidus. We performed "de-efferentation" experiments in which we cut the motor neuron axons to the myocardium and "de-afferentation" experiments in which we cut or ligated the dendrites. In the isolated CG, these manipulations had no effect on the CG motor pattern. When the CG remained embedded in the myocardium, however, these manipulations, interrupting either the efferent or afferent limb of the CPG-peripheral loop, decreased contraction amplitude, increased the frequency of the CG motor neuron spike bursts, and decreased the number of spikes per burst and burst duration. Finally, passive stretches of the myocardium likewise modulated the spike bursts, an effect that disappeared when the dendrites were cut. We conclude that feedback through the dendrites indeed operates in this system and suggest that it completes a loop through which the system self-regulates its activity.