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Although the raison d'etre of the brain is the survival of the body, there are relatively few theoretical studies of closed-loop rhythmic motor control systems. In this paper we provide a unified framework, based on variational analysis, for investigating the dual goals of performance and robustness in powerstroke–recovery systems. To demonstrate our variational method, we augment two previously published closed-loop motor control models by equipping each model with a performance measure based on the rate of progress of the system relative to a spatially extended external substrate—such as a long strip of seaweed for a feeding task, or progress relative to the ground for a locomotor task. The sensitivity measure quantifies the ability of the system to maintain performance in response to external perturbations, such as an applied load. Motivated by a search for optimal design principles for feedback control achieving the complementary requirements of efficiency and robustness, we discuss the performance–sensitivity patterns of the systems featuring different sensory feedback architectures. In a paradigmatic half-center oscillator-motor system, we observe that the excitation–inhibition property of feedback mechanisms determines the sensitivity pattern while the activation–inactivation property determines the performance pattern. Moreover, we show that the nonlinearity of the sigmoid activation of feedback signals allows the existence of optimal combinations of performance and sensitivity. In a detailed hindlimb locomotor system, we find that a force-dependent feedback can simultaneously optimize both performance and robustness, while length-dependent feedback variations result in significant performance-versus-sensitivity tradeoffs. Thus, this work provides an analytical framework for studying feedback control of oscillations in nonlinear dynamical systems, leading to several insights that have the potential to inform the design of control or rehabilitation systems. [ABSTRACT FROM AUTHOR]

Patients with pseudobulbar palsy often present with velopharyngeal incompetence. Velopharyngeal incompetence is usually observed during expiratory activities such as speech and/or blowing during laryngoscopy. These patients typically exhibit good velopharyngeal closure during swallowing, which is dissociated from expiratory activities. We named this phenomenon "speech-swallow dissociation" (SSD). SSD on endoscopic findings can help in diagnosing the underlying disease causing dysphagia. This endoscopic finding is qualitative, and the quantitative characteristics of SSD are still unclear. Accordingly, the current study aimed to quantitatively evaluate SSD in patients with pseudobulbar palsy. We evaluated velopharyngeal pressure during swallowing and expiratory activity in 10 healthy subjects and 10 patients with pseudobulbar palsy using high-resolution manometry, and compared the results between the two groups. No significant differences in maximal velopharyngeal contraction pressure (V-Pmax) were observed during dry swallowing between the pseudobulbar palsy group and healthy subjects (190.5 mmHg vs. 173.6 mmHg; P = 0.583). V-Pmax during speech was significantly decreased in the pseudobulbar palsy group (85.4 mmHg vs. 34.5 mmHg; P < 0.001). The degree of dissociation of speech to swallowing in V-Pmax, when compared across groups, exhibited a larger difference in the pseudobulbar palsy group, at 52% versus 80% (P = 0.001). Velopharyngeal pressure during blowing was similar to that during speech. Velopharyngeal closure in patients with pseudobulbar palsy exhibited weaker pressure during speech and blowing compared with swallowing, quantitatively confirming the presence of SSD. Pseudobulbar palsy often presents with SSD, and this finding may be helpful in differentiating the etiology of dysphagia. [ABSTRACT FROM AUTHOR]

Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding. [ABSTRACT FROM AUTHOR]

Silent hypoxemia, or "happy hypoxia," is a puzzling phenomenon in which patients who have contracted COVID-19 exhibit very low oxygen saturation ( SaO 2 < 80%) but do not experience discomfort in breathing. The mechanism by which this blunted response to hypoxia occurs is unknown. We have previously shown that a computational model of the respiratory neural network (Diekman et al. in J Neurophysiol 118(4):2194–2215, 2017) can be used to test hypotheses focused on changes in chemosensory inputs to the central pattern generator (CPG). We hypothesize that altered chemosensory function at the level of the carotid bodies and/or the nucleus tractus solitarii are responsible for the blunted response to hypoxia. Here, we use our model to explore this hypothesis by altering the properties of the gain function representing oxygen sensing inputs to the CPG. We then vary other parameters in the model and show that oxygen carrying capacity is the most salient factor for producing silent hypoxemia. We call for clinicians to measure hematocrit as a clinical index of altered physiology in response to COVID-19 infection. [ABSTRACT FROM AUTHOR]

Gait planning is one of the main focuses in the research field of bipedal robotics. To enhance the stability and simplicity of gait planning for bipedal robots using central pattern generator (CPG) methods, this paper first refines the existing Kimura oscillator model. Subsequently, an improved oscillator model is employed to propose a novel configuration of CPG network for flat walking gait planning in bipedal robots. A particle swarm algorithm with variable structural parameters is utilized to optimize the parameters of the CPG network, with the optimization objective being the maximization of stability margin at zero moment points (ZMP) during the walking process of the bipedal robot. Finally, an ADAMS simulation experiment platform is established to validate the feasibility of this method through simulation experiments. The experimental results indicate that this approach enables bipedal robots to achieve stable walking motion on a flat surface. [ABSTRACT FROM AUTHOR]

Most conventional biped robots process leg movements and information from each sensor by numerical calculation using a CPU. However, to cope with diverse environments, the numerical calculations are enormous, so they must be processed at high speed using a high-performance CPU and high power consumption. On the other hand, focusing on human motor control, it is believed that basic motor patterns such as walking and running are generated by a neural network called the central pattern generator (CPG), which is localized in the spinal cord and is independent of calculation. We previously focused on pulse-type hardware neural networks (P-HNNs), in which the neural network was composed of analog electronic circuits, and developed a hardware CPG model for controlling a single leg of a musculoskeletal humanoid robot. However, to actually move a biped robot, a CPG model that takes into account both legs and sensory information is required. Therefore, this study aims to develop a hardware CPG model for controlling both legs of a musculoskeletal humanoid robot whose gait changes according to the higher center and sensory information. We report on a hardware CPG model configured by circuit simulation confirmed the generation of walking and running patterns. [ABSTRACT FROM AUTHOR]

Based on the central pattern generator (CPG) and fuzzy controller, this paper proposes a heading control method for the directional motion for a new type of blade legged hexapod robot (BLHR). First, the modified Hopf oscillator is used to construct the CPG model of BLHR based on the limit cycle. Second, the fuzzy controller is applied to adjust the support angles of legs to change the heading of BLHR, thereby correcting the error between the actual and desired heading angle in real-time. Finally, the feasibility and effectiveness of the proposed CPG-Fuzzy control method is verified in Gazebo simulations and real-world experiments. This is the first attempt to combine CPG and fuzzy control in the context of hexapod robot. In comparison to existing control methods, the proposed CPG-Fuzzy controller can implement heading control of BLHR with better performance and value of further investigation. [ABSTRACT FROM AUTHOR]

Currently, it is accepted that animal locomotion is controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current central pattern generator models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the Bienenstock–Cooper–Munro learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to central pattern generator models, because rhythm generating neurons and genetically defined connectivity are not required in our model. Though our results are not in contradiction to current models, as existing neural mechanism and structures, not used in our model, can be expected to facilitate the kind of learning demonstrated here. Therefore, our model could be used to augment existing models. [ABSTRACT FROM AUTHOR]

The decisive role of sensory input in the initiation and modulation of locomotion has been demonstrated repeatedly. One approach to studying this input is provided by the split treadmill paradigm. We report here a comparative analysis of the walking of decerebrated cats on a split treadmill with belts moving not only at different speeds (differing by a factor of three), but also in different directions (forward and backward). The two limbs were shown to operate reciprocally in this mode of locomotion, as did the flexor and extensor muscles of each limb. Two main stepping strategies were identified: for each step of the limb walking on the slow treadmill belt, the limb walking on the fast belt carried out either one step (the 1:1 strategy) or two steps (the 1:2 strategy); strategies could alternate. The results obtained here suggest persistence of the integration of the locomotor networks of the two limbs despite significant mismatch of their sensory inputs. [ABSTRACT FROM AUTHOR]

Human locomotion may result from monotonic shifts in the referent position, R, of the body in the environment. R is also the spatial threshold at which muscles can be quiescent but are activated depending on the deflection of the current body configuration Q from R. Shifts in R are presumably accomplished with the participation of proprioceptive and visual feedback and responsible for transferring stable body balance (equilibrium) from one place in the environment to another, resulting in rhythmic activity of multiple muscles by a central pattern generator (CPG). We tested predictions of this two-level control scheme. In particular, in response to a transient block of vision during locomotion, the system can temporarily slow shifts in R. As a result, the phase of rhythmical movements of all four limbs will be changed for some time, even though the rhythm and other characteristics of locomotion will be fully restored after perturbation, a phenomenon called long-lasting phase resetting. Another prediction of the control scheme is that the activity of multiple muscles of each leg can be minimized reciprocally at specific phases of the gait cycle both in the presence and absence of vision. Speed of locomotion is related to the rate of shifts in the referent body position in the environment. Results confirmed that human locomotion is likely guided by feedforward shifts in the referent body location, with subsequent changes in the activity of multiple muscles by the CPG. Neural structures responsible for shifts in the referent body configuration causing locomotion are suggested. [ABSTRACT FROM AUTHOR]

This paper proposes a novel stride management strategy for rehabilitation exoskeletons. This method incorporates a Central Pattern Generator (CPG) into an Assist-As-Needed (AAN) controller to induce an optimal stride length for the wearers and optimize the energy consumption. Most AAN controllers rely on a predefined and fixed trajectory to measure the required amount of assistance. However, for a stride management approach, the trajectory should be updated regularly according to the wearer’s performance to induce the optimal stride length eventually. The proposed stride management strategy deals with this challenge by integrating a CPG into the control loop. The CPG updates the desired trajectory right after each swing phase. The AAN controller uses a recently introduced Strength Index (SI) for continuous measurement of the wearer’s ability in tracking the desired trajectory. A virtual tunnel around the desired trajectory is defined, and the tunnel boundaries are adjusted according to the SI. Then, an adaptive impedance controller determines the assistive force according to the distance between the actual trajectory of the user and the tunnel boundaries. The performance of the proposed method is evaluated in OpenSim software, and reductions in the metabolic cost and muscles’ activity are observed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A significant component of the repetitive dynamics during locomotion in vertebrates is generated within the spinal cord. The legged locomotion of mammals is most likely controled by a hierarchical, multi-layer spinal network structure, while the axial circuitry generating the undulatory swimming motion of animals like lamprey is thought to have only a single layer in each segment. Recent experiments have suggested a hybrid network structure in zebrafish larvae in which two types of excitatory interneurons (V2a-I and V2a-II) both make first-order connections to the brain and last-order connections to the motor pool. These neurons are connected by electrical and chemical synapses across segments. Through computational modeling and an asymptotic perturbation approach we show that this interleaved interaction between the two neuron populations allows the spinal network to quickly establish the correct activation sequence of the segments when starting from random initial conditions, as needed for a swimming spurt, and to reduce the dependence of the intersegmental phase difference (ISPD) of the oscillations on the swimming frequency. The latter reduces the frequency dependence of the waveform of the swimming motion. In the model the reduced frequency dependence is largely due to the different impact of chemical and electrical synapses on the ISPD and to the significant spike-frequency adaptation that has been observed experimentally in V2a-II neurons, but not in V2a-I neurons. Our model makes experimentally testable predictions and points to a benefit of the hybrid structure for undulatory locomotion that may not be relevant for legged locomotion. [ABSTRACT FROM AUTHOR]

Central pattern generators are characterized by a heterogeneous cellular composition, with different cell types playing distinct roles in the production and transmission of rhythmic signals. However, little is known about the functional implications of individual variation in the relative distributions of cells and their connectivity patterns. Here, we addressed this question through a combination of morphological data analysis and computational modeling, using the pacemaker nucleus of the weakly electric fish Apteronotus leptorhynchus as case study. A neural network comprised of 60–110 interconnected pacemaker cells and 15–30 relay cells conveying its output to electromotoneurons in the spinal cord, this nucleus continuously generates neural signals at frequencies of up to 1 kHz with high temporal precision. We systematically explored the impact of network size and density on oscillation frequencies and their variation within and across cells. To accurately determine effect sizes, we minimized the likelihood of complex dynamics using a simplified setup precluding differential delays. To identify natural constraints, parameter ranges were extended beyond experimentally recorded numbers of cells and connections. Simulations revealed that pacemaker cells have higher frequencies and lower within-population variability than relay cells. Within-cell precision and between-cells frequency synchronization increased with the number of pacemaker cells and of connections of either type, and decreased with relay cell count in both populations. Network-level frequency-synchronized oscillations occurred in roughly half of simulations, with maximized likelihood and firing precision within biologically observed parameter ranges. These findings suggest the structure of the biological pacemaker nucleus is optimized for generating synchronized sustained oscillations. [ABSTRACT FROM AUTHOR]

We report here an experimental-theoretical study of the features of the EMG activity of the iliopsoas (IP), tibialis anterior (TA), and gastrocnemius medialis (GM) muscles of the hindlimbs of decerebrate cats during walking induced by epidural spinal cord stimulation in various locomotor modes determined by the direction of movement of the treadmill belts: unidirectional walking in the forward (FW) and backward (BW) directions and bidirectional walking (BDW); an attempt was made to explain the data obtained in these experiments from the point of view of various central pattern generator (CPG) models. While maintaining the reciprocity of the operation of antagonist muscles during BDW, the activity of the TA and GM in BDW in oppositely directed limbs differed from each other: the activity of the TA and GM for the limb going backwards in BDW was similar to the activity of these muscles in unidirectional BW, and the activity of the TA and GM for limb moving forward during BDW was similar to the activity of these muscles during unidirectional FW. The patterns of IP activity of both limbs in BDW were similar, but IP activity was lower than for unidirectional FW and higher than for unidirectional BW. The stability of IP muscle activity was more variable than that of TA and GM. These data can be explained from the point of view of the operation of two-level CPG models, as well as a scheme for coordinating the CPG of different limbs, including reciprocal relationships between flexion half-centers and the influences of excitatory and inhibitory afferent signals on each component in the system in different phases of the locomotor cycle. [ABSTRACT FROM AUTHOR]

Most animals are capable of efficient locomotion owing to a special neural network—the central pattern generator (CPG) located in the central nervous system. The half-center oscillator (HCO) is the basic and pivotal motif of the CPG, which consists of two neurons or two neural populations connected by excitatory and inhibitory neurotransmitters. In this study, we present a simple delayed HCO neural system consisting of two inertial neurons and study the rhythm activities of the system and their coexistence. The periodic orbit, quasi-periodic activity, and chaotic attractor with self- and mutual-symmetric patterns are investigated through their time history, phase diagram, frequency spectra, and Poincaré maps. The one-dimensional bifurcation diagram presents different routes to chaos. A pair of mutual-symmetric periodic orbits surrounding nontrivial equilibria enters a pair of mutual-symmetric chaotic attractors by the period-doubling bifurcation. A self-symmetric periodic orbit evolves into a self-symmetric chaotic attractor by the quasi-periodic bifurcation. Finally, the chaotic attractor translates into a pair of mutual-symmetric multi-periodic orbits and enters a pair of mutual-symmetric chaotic attractors by the period-doubling bifurcation. The HCO neural system illustrates multiple coexistences, such as self-symmetric quasi-periodic orbit with a pair of mutual-symmetric chaotic attractors, a pair of multi-period orbits with chaotic attractors, and two pairs of mutual-symmetric chaotic attractors. [ABSTRACT FROM AUTHOR]

Legged locomotion is a desirable ability for robotic systems thanks to its agile mobility and wide range of motions that it provides. In this paper, the use of neural network-based nonlinear controller structures which consist of recurrent and feedforward layers have been examined in the dynamically stable walking problem of two-legged robots. In detail, hybrid neural controllers, in which long short-term memory type of neuron models employed at recurrent layers, are utilized in the feedback and feedforward paths. To train these neural networks, supervised learning data sets are created by using a biped robot platform which is controlled by a central pattern generator. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is examined under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the inclusion of recurrent layer provides smooth transition and control between stance and flight motion phases and L 2 regularization is beneficial for walking performance. Finally, the proposed hybrid neural network models are found to be more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training. [ABSTRACT FROM AUTHOR]

The central pattern generator (CPG) is a key neural-circuit component of the locomotion control system. Recently, numerous molecular and genetic approaches have been proposed for investigating the CPG mechanisms. The rhythm in the CPG locomotor circuits comes from the activity in the ipsilateral excitatory neurons whose output is controlled by inter-neuron inhibitory connections. Conventional models for simulating the CPG mechanism are complex Hodgkin-Huxley-type models. Inspired by biological investigations and the continuous-time Matsuoka model, we propose new integral-order and fractional-order CPG models, which consider time delays and synaptic interfaces. The phase diagrams exhibit limit cycles and periodic solutions, in agreement with the CPG biological characteristics. As well, the fractional-order model shows state transitions with order variations. In addition, we investigate the relationship between the CPG and the motor units through the construction of integral-order and fractional-order coupling models. Simulations of these coupling models show that the states generated by the three motor units are in accordance with the experimentally-obtained states in previous studies. The proposed models reveal that the CPG can regulate limb locomotion patterns through connection weights and synaptic interfaces. Moreover, in comparison to the integral-order models, the fractional-order ones appear to be more effective, and hence more suitable for describing the dynamics of the CPG biological system. [ABSTRACT FROM AUTHOR]

A model is proposed to consider sexual climax in men, women, and animals as a unitary phenomenon. Sexual climax is a stereotyped rhythmic pattern of spinally generated neural activity in the autonomic and somatic nerves innervating pelvic organs. A column of neurons in the spinal cord of the male rat is strongly activated by ejaculation (sexual climax in the male). These neurons project to the thalamus and are therefore called lumbar spinothalamic cells (LSt cells). Comprehensive studies have demonstrated that the LSt cells constitute a central pattern generator of ejaculation. These findings have been extended to female animals. Further studies identified LSt cells in the lumbar spinal cord of men and women. Strong evidence indicates that the LSt cells mediate ejaculation in men. The climax model generalizes and extends these studies. It postulates that LSt cells in the lumbar spinal cord of humans and animals of both sexes generate climax. The LSt cells generate the neural activity driving the pelvic contractions and other responses of climax. The activity is transmitted to supraspinal sites to activate orgasm. The LSt cells receive excitatory and inhibitory projections from supraspinal sites. The descending projections reflect subjective arousal and inhibitions. Spinal sensory neurons from the genitals provide excitatory and inhibitory innervation to the LSt cells. These represent pleasurable and noxious sensations. The supraspinal and spinal excitatory and inhibitory inputs are integrated by the LSt. When the sum of the excitatory inputs, minus the sum of the inhibitory inputs reaches a threshold, the LSt cells generate sexual climax. [ABSTRACT FROM AUTHOR]

Intrinsic oscillators in the central nervous system play a preeminent role in the neural control of rhythmic behaviors, yet little is known about how the ionic milieu regulates their output patterns. A powerful system to address this question is the pacemaker nucleus of the weakly electric fish Apteronotus leptorhynchus. A neural network comprised of an average of 87 pacemaker cells and 20 relay cells produces tonic oscillations, with higher frequencies in males compared to females. Previous empirical studies have suggested that this sexual dimorphism develops and is maintained through modulation of buffering of extracellular K+ by a massive meshwork of astrocytes enveloping the pacemaker and relay cells. Here, we constructed a model of this neural network that can generate sustained spontaneous oscillations. Sensitivity analysis revealed the potassium equilibrium potential, EK (as a proxy of extracellular K+ concentration), and corresponding somatic channel conductances as critical determinants of oscillation frequency and amplitude. In models of both the pacemaker nucleus network and isolated pacemaker and relay cells, the frequency increased almost linearly with EK, whereas the amplitude decreased nonlinearly with increasing EK. Our simulations predict that this frequency increase is largely caused by a shift in the minimum K+ conductance over one oscillation period. This minimum is close to zero at more negative EK, converging to the corresponding maximum at less negative EK. This brings the resting membrane potential closer to the threshold potential at which voltage-gated Na+ channels become active, increasing the excitability, and thus the frequency, of pacemaker and relay cells. [ABSTRACT FROM AUTHOR]

It has been hypothesized that sensory feedback is a critical component in determining the functionality of a central pattern generator. To test this, Yu and Thomas's recent work Yu and Thomas (Biol Cybern 115(2):135–160, 2021) built a model of a half-center oscillator coupled to a simple muscular model with sensory feedback. They showed that sensory feedback increases robustness against external noise, while simultaneously expanding the potential repertoire of functions the half-center oscillator can perform. However, they show that this comes at the cost of robustness against internal noise. [ABSTRACT FROM AUTHOR]

Previous authors have proposed two basic hypotheses about the factors that form the basis of locomotor rhythms in walking insects: sensory feedback only or sensory feedback together with rhythmic activity of small neural circuits called central pattern generators (CPGs). Here we focus on the latter. Following this concept, to generate functional outputs, locomotor control must feature both rhythm generation by CPGs at the level of individual joints and coordination of their rhythmic activities, so that all muscles are activated in an appropriate pattern. This work provides an in-depth analysis of an aspect of this coordination process based on an existing network model of stick insect locomotion. Specifically, we consider how the control system for a single joint in the stick insect leg may produce rhythmic output when subjected to ascending sensory signals from other joints in the leg. In this work, the core rhythm generating CPG component of the joint under study is represented by a classical half-center oscillator constrained by a basic set of experimental observations. While the dynamical features of this CPG, including phase transitions by escape and release, are well understood, we provide novel insights about how these transition mechanisms yield entrainment to the incoming sensory signal, how entrainment can be lost under variation of signal strength and period or other perturbations, how entrainment can be restored by modulation of tonic top-down drive levels, and how these factors impact the duty cycle of the motor output. [ABSTRACT FROM AUTHOR]

Chirping male crickets combine a 30 Hz pulse pattern with a 3 Hz chirp pattern to drive the rhythmic opening-closing movements of the front wings for sound production. Lesion experiments suggest two coupled modular timer-networks located along the chain of abdominal ganglia, a network in A3 and A4 generating the pulse pattern, and a network organized along with ganglia A4–A6 controlling the generation of the chirp rhythm. We analyzed neurons of the timer-networks and their synaptic connections by intracellular recordings and staining. We identified neurons spiking in phase with the chirps and pulses, or that are inhibited during the chirps. Neurons share a similar "gestalt", regarding the position of the cell body, the dendritic arborizations and the contralateral ascending axon. Activating neurons of the pulse-timer network elicits ongoing motor activity driving the generation of pulses; this activity is not structured in the chirp pattern. Activating neurons of the chirp-timer network excites pulse-timer neurons; it drives the generation of chirps and during the chirps the pulse pattern is produced. Our results support the hypothesis that two modular networks along the abdominal ganglion chain control the cricket calling song, a pattern generating network in the mesothoracic ganglion may not be required. [ABSTRACT FROM AUTHOR]

A common neural control mechanism coordinates various types of rhythmic locomotion performed in the sagittal plane, but it is unclear whether frontal plane movements show similar neural patterning in adult humans. The purpose of this study was to compare cutaneous reflex modulation patterns evoked during sagittal and frontal plane rhythmic movements. Eight healthy, neurologically intact adults (three males, five females) walked and sidestepped on a treadmill at approximately 1 Hz. The sural nerve of the dominant (and lead) limb was stimulated randomly every 3–7 steps at eight phases of each gait cycle. Ipsilateral electromyographic recordings from four lower leg muscles and kinematic data from the ankle were collected continuously throughout both tasks. Data from unstimulated gait cycles were used as control trials to calculate middle-latency reflex responses (80–120 ms) and kinematic changes (140–220 ms) following electrical stimulation. Results show that the cutaneous reflex modulation patterns were similar across both tasks despite significant differences in background EMG activity. However, increased reflex amplitudes were observed during the late swing and early stance phases of sidestepping, which directly altered ankle kinematics. These results suggest that the neural control mechanisms responsible for coordinating sagittal locomotion are flexibly modified to coordinate frontal plane activities even with very different foot landing mechanics. [ABSTRACT FROM AUTHOR]

Simulations of neural activity are commonly based on differential equations. We address the question what can be achieved with a simplified discrete model. The proposed model resembles artificial neural networks enriched with additional biologically inspired features. A neuron has several states, and the state transitions follow endogenous patterns which roughly correspond to firing behavior observed in biological neurons: oscillatory, tonic, plateauing, etc. Neural interactions consist of two components: synaptic connections and extrasynaptic emission of neurotransmitters. The dynamics is asynchronous and event-based; the events correspond to the changes in neurons activity. This model is innovative in introducing discrete framework for modeling neurotransmitter interactions which play the important role in neuromodulation. We simulate rhythmic activity of small neural ensembles like central pattern generators (CPG). The modeled examples include: the biphasic rhythm generated by the half-center mechanism with the post-inhibitory rebound (like the leech heartbeat CPG), the triphasic rhythm (like in pond snail feeding CPG) and the pattern switch in the system of several neurons (like the switch between ingestion and egestion in Aplysia feeding CPG). The asynchronous dynamics allows to obtain multi-phasic rhythms with phase durations close to their biological prototypes. The perspectives of discrete modeling in biological research are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Gap junctions are physical channels that connect adjacent cells, permitting the flow of small molecules/ions between the cytoplasms of the coupled units. Innexin/innexin-like proteins are responsible for the formation of invertebrate gap junctions. Within the nervous system, gap junctions often function as electrical synapses, providing a means for coordinating activity among electrically coupled neurons. While some gap junctions allow the bidirectional flow of small molecules/ions between coupled cells, others permit flow in one direction only or preferentially. The complement of innexins present in a gap junction determines its specific properties. Thus, understanding innexin diversity is key for understanding the full potential of electrical coupling in a species/system. The decapod crustacean cardiac ganglion (CG), which controls cardiac muscle contractions, is a simple pattern-generating neural network with extensive electrical coupling among its circuit elements. In the lobster, Homarus americanus, prior work suggested that the adult neuronal innexin complement consists of six innexins (Homam-Inx1-4 and Homam-Inx6-7). Here, using a H. americanus CG-specific transcriptome, we explored innexin complement in this portion of the lobster nervous system. With the exception of Homam-Inx4, all of the previously described innexins appear to be expressed in the H. americanus CG. In addition, transcripts encoding seven novel putative innexins (Homam-Inx8-14) were identified, four (Homam-Inx8-11) having multiple splice variants, e.g., six for Homam-Inx8. Collectively, these data indicate that the innexin complement of the lobster nervous system in general, and the CG specifically, is likely significantly greater than previously reported, suggesting the possibility of expanded gap junction diversity and function in H. americanus. [ABSTRACT FROM AUTHOR]

The aim of the present work was to study the dynamics of changes in the sensorimotor control of gait in conditions of unweighting of the hindlimbs. Experiments were performed on rats in which the kinematics of locomotor movements were evaluated before, during, and after keeping the hindlimbs in the Morey-Holton antiorthostatic position. Locomotion on the treadmill after unweighting showed increases in extension at the ankle and knee joints. Analysis of the dynamics of changes in the kinematic parameters of locomotor movements in the unsupported position revealed progressive increases in extension of the knee and ankle joints, though there was a decrease in extension at the hip joint during the seven days of unweighting. This corresponded to the forced position of the hindlimbs on passive hanging, which was characterized by hyperextension of the knee and ankle joints, though with hyperflexion of the hips. These data lead to the conclusion that stable dynamics of changes in gait kinematics characterizing the operation of spinal neural networks in the central locomotor generator responsible for forming the motor pattern can be produced by progressive reorganization of the system of proprioceptive connections in conditions of the altered configuration of body posture and limb joints during unweighting. [ABSTRACT FROM AUTHOR]

In this paper, the tracking of the zero moment point (ZMP) trajectory for humanoid locomotion is addressed. For that, a combination of a repetitive controller (RC) and a linear quadratic regulator (LQR) is proposed. The RC controller achieves a zero steady state tracking error for any periodic desired output with a fixed period. Here, the robot model is simplified as a cart table model. In the first stage, a walking pattern with constant gait is considered to allow the use of a repetitive controller. The results are then compared to a classical PD control. In the second part, the proposed controller is applied to track a desired ZMP profile generated using a central pattern generator (CPG). Here, the speed of the oscillator is adjusted via a proper selection of the parameters of the CPG and the RC design is simultaneously updated to assure the perfect tracking of the ZMP. [ABSTRACT FROM AUTHOR]

Using a simulation approach, we propose a biophysically feasible mechanism describing how a multifunctional central pattern generator (CPG) can produce co-existing slow and fast rhythms of the patterned motor output. This mechanism suggests that the same core elemental CPG can produce either a locomotion or a paw shaking rhythm in mammals (cat). We built a Hodgkin–Huxley-style biophysical model that generates multistability of a locomotion-like regime and a paw shake-like regime. This model is constructed as a half-center oscillator (HCO), in which two inhibitory neurons (representing the respective neuronal populations in real CPGs) reciprocally inhibit each other. We propose that the locomotion rhythm and the paw shaking rhythm are controlled by two different slowly inactivating intrinsic ion currents. In our model, slowly inactivating low voltage-activated calcium current (ICaS) drives the locomotion-like regime, while slowly inactivating sodium current (INaS) drives the paw shakelike regime. We investigated whether asymmetric characteristics of these regimes could be reliably and separately controlled by asymmetric variation of the conductances of these two currents. We found that variation of the conductance of ICaS in only one neuron, while holding this conductance constant in the other neuron, produces asymmetric changes in bursting characteristics, including the burst duration and interburst interval of the locomotion-like regime without producing notable changes of the paw shake-like regime. We also found that similar variation of the conductance of INaS affects the bursting characteristics of both regimes, although the paw shake-like regime is affected more remarkably than the locomotion-like one. [ABSTRACT FROM AUTHOR]

In this paper a procedural animation framework is developed and discussed, which consists of a oscillator based Central Pattern Generator (CPG) system. This CPG based animation model is able to produce coupled leg oscillation derived through user-controlled parameters, producing in-phase and out-of-phase leg swing motion curves. Each leg has a separate CPG unit that is able to generate and control the swing and stance phases of each gait cycle with couple oscillation, having a time shift in correspondence to each leg, in a permuted symmetry. The dynamic motion is calculated independently for each body part with user interaction and control over the speed, frequency and oscillation of body parts individually, during runtime, for high divergence control of the simulation. The user can manipulate the simulation parameters for leg impact phases and duration at runtime and the system will automatically adjust the motion gaits and transitions between each gait at runtime. This procedural model for animating quadrupeds can generate various locomotion gaits with varying speed and footfall patterns dynamically. The various gaits produced by the CPG system are, walk, trot, gallop, canter, pace, pronk and rack. Various gait and footfall timing test are performed to test and validate the motion, along with a study of user's perception test to determine a Visual Mean Opinion Score (VMOS) of the believability and accuracy of the generated animation with statistical significance. [ABSTRACT FROM AUTHOR]

Dynamic walking fulfill agility and stability simultaneously is one of the most difficulty for biped robot control. The traditional zero moment point (ZMP) is the most commonly used reference point for biped robot static and quasi dynamic walking control. However, human walking experimental results indicate that during walking process of human beings, the ZMP trajectory is not always conformed to the requirement of stability, such as giant strides, acceleration walking or fast walking. In order to reveal the mechanism of the biped dynamic walking, this paper proposed a novel stability criterion for the biped walking by tuning the conventional fixed support polygon area to an adjustable one. This method includes the tiptoe underactuated phase of the support foot during the biped walking. A new algorithm for the real-time biped walking generation by combining central pattern generation (CPG) with foot rotation indicator (FRI) is presented. The FRI monitor establishes the mapping function between the center of mass of the biped robot with the boundary of the elastic support polygon. By introducing FRI information, the CPG parameters can be adjusted in real time to generate a rhythmic and stable walking pattern. Numerical simulation results show that the proposed algorithm extends the application area of the ZMP criterion and improves the walking velocity of the biped robot. Moreover, the algorithm builds a bridge for the dynamic biped walking from the robot agility to motor parameters. This means that the agility of the biped robot can be quantitative controlled by modulating the motor parameters. [ABSTRACT FROM AUTHOR]

Starting from the piecewise-linear framework applied to networked FitzHugh-Nagumo oscillators, this paper aims at presenting a control strategy relying on phase portrait reshaping through the manipulation of their nullclines in order to fulfil both phase and time requirements. This has been achieved by relating the slopes of piecewise linearly approximated model's nullclines in its limit cycle to its oscillation period. Additionally, the targeted issue addressed by this paper has been assessed by combining the former framework with an event-driven control strategy aimed at reducing its effects to specific time instants instead of continuous, instantaneous phases, which is much more computationally expensive. The strategy is therefore motivated by its simplicity and is supported by key applications to bioinspired locomotion control in legged robots, which suggest how a dynamics-preserving approximation of the phase portrait combined with a sampled control action can produce pre-defined phase topologies in directed, non-diffusive tree graphs. [ABSTRACT FROM AUTHOR]

Background: Body-weight-supported treadmill training (BWSTT) is a treatment method to help patients with incomplete spinal cord injuries to restore the capability to walk again. Objective: This study aimed to assess the effects of different percentages of body-weight-support locomotor training on the enhancement of gait for persons with incomplete spinal cord injuries. Subjects and methods: Twenty male paraplegic persons, with the statistical parameters [mean ± SD] age 32.53 ± 1. 793 years, height 175.71 ± 1.658 cm, weight 71.59 ± 2.442 kg, and body mass index 23.18 ± 0.828 kg/m2) were picked by convenience sampling. Ten patients were randomly allocated to the body-weight treadmill training 30% group (Group A); and ten patients to the body-weight treadmill training 40% group (Group B). The two groups were exercised for 1h every set, at two sets weekly for 1.5 months. Body-weight-supported treadmill training was divided into a 15-min warm-up on a stationary bicycle, 45-min body-weight-supported treadmill training with 30 and 40% body weight and finally, a 10-min cool down as we started loading with 10% of body weight of each patient until reach 30% or 40% according to his group. Results: The resultant data revealed that there are significant distinctions between the body-weight treadmill training 30% and the with body-weight treadmill training 40% groups in regards to alterations in the walking speed, step length, and stride length (p < 0.001), cadence (p = 0.009). Conclusion: Body-weight-supported treadmill training 40% in comparison with body-weight-supported treadmill training 30% can enhance both the quantity and quality of gait in paraplegic patients (ASIA B, C, and D classification). [ABSTRACT FROM AUTHOR]

This study aims to generate 3-D dynamic bipedal walking using two oscillators of a CPG approach based on its body dynamics. First, a 3-D biped, which inherited the aspect of the previous passive dynamic walker, was developed that contained flat feet with pressure sensors, ankle joints composed of ball joints surrounded by coil springs, telescopic knees, and actuated hip joints using a direct drive (DD) motor. The torso was controlled by the DD motors using a PD control scheme. Next, the Owaki–Ishiguro method was used with ground contact information in the phase oscillator to the knee joint. Finally, experimental tests were conducted to elucidate the effectiveness of the biped. A 3-D dynamic bipedal gait of RW06 was developed using knee oscillation based on the phase oscillator. Bipedal walking was excited only by the knee oscillation; therefore, the gait was strongly affected by the body dynamics. [ABSTRACT FROM AUTHOR]

We propose a biomimetic approach for steering motion control of a snake robot. Inspired by a vertebrate biological motor system paradigm, a hierarchical control scheme is adopted. In the control scheme, an artificial central pattern generator (CPG) is employed to generate serpentine locomotion in the robot. This generator outputs the coordinated desired joint angle commands to each lower-level effector controller, while the locomotion can be controlled through CPG modulation by a higher-level motion controller. The motion controller consists of a cerebellar model articulation controller (CMAC) and a proportional-derivative (PD) controller. Because of the fast learning ability of the CMAC, the proposed motion controller can drive the robot to track the desired orientation and adapt to unexpected perturbations. The PD controller is employed to expedite the convergence speed of the motion controller. Finally, both numerical studies and experiments proved that the proposed approach can help the snake robot achieve good tracking performance and adaptability in a varying environment. [ABSTRACT FROM AUTHOR]

During forward swimming, crayfish and other long-tailed crustaceans rhythmically move four pairs of limbs called swimmerets to propel themselves through the water. This behavior is characterized by a particular stroke pattern in which the most posterior limb pair leads the rhythmic cycle and adjacent swimmerets paddle sequentially with a delay of roughly 25% of the period. The neural circuit underlying limb coordination consists of a chain of local modules, each of which controls a pair of limbs. All modules are directly coupled to one another, but the inter-module coupling strengths decrease with the distance of the connection. Prior modeling studies of the swimmeret neural circuit have included only the dominant nearest-neighbor coupling. Here, we investigate the potential modulatory role of long-range connections between modules. Numerical simulations and analytical arguments show that these connections cause decreases in the phase-differences between neighboring modules. Combined with previous results from a computational fluid dynamics model, we posit that this phenomenon might ensure that the resultant limb coordination lies within a range where propulsion is optimal. To further assess the effects of long-range coupling, we modify the model to reflect an experimental preparation where synaptic transmission from a middle module is blocked, and we generate predictions for the phase-locking properties in this system. [ABSTRACT FROM AUTHOR]

Locomotion of mammals, including humans, is based on the rhythmic activity of spinal cord circuitries. The functioning of these circuitries depends on multimodal afferent information and on supraspinal influences from the motor cortex. Using the method of transcranial magnetic stimulation (TMS) of arm muscle areas in the motor cortex, we studied the motor evoked potentials (MEP) in the upper arm muscles in stationary conditions and during voluntary and vibration-evoked arm movements. The study included 13 healthy subjects under arm and leg unloading conditions. In the first series of experiments, with motionless limbs, the effect of vibration of left upper arm muscles on motor responses in these muscles was evaluated. In the second series of experiments, MEP were compared in the same muscles during voluntary and rhythmic movements generated by left arm m. triceps brachii vibration (the right arm was stationary). Motionless left arm vibration led to an increase in MEP values in both vibrated muscle and in most of the non-vibrated muscles. For most target muscles, MEP was greater with voluntary arm movements than with vibration-evoked movements. At the same time, a similar MEP modulation in the cycle of arm movements was observed in the same upper arm muscles during both types of arm movements. TMS of the motor cortex significantly potentiated arm movements generated by vibration, but its effect on voluntary movements was weaker. These results indicate significant differences in the degree of motor cortex involvement in voluntary and evoked arm movements. We suppose that evoked arm movements are largely due to spinal rather than central mechanisms of generation of rhythmic movements. [ABSTRACT FROM AUTHOR]

Adapting motor output based on environmental forces is critical for successful locomotion in the real world. Arthropods use at least two neural mechanisms to adjust muscle activation while walking based on detected forces. Mechanism 1 uses negative feedback of leg depressor force to ensure that each stance leg supports an appropriate amount of the body’s weight. Mechanism 2 encourages searching for ground contact if the leg supports no body weight. We expand the neural controller for MantisBot, a robot based upon a praying mantis, to include these mechanisms by incorporating leg-local memory and command neurons, as observed in arthropods. We present results from MantisBot transitioning between searching and stepping, mimicking data from animals as reported in the literature. [ABSTRACT FROM AUTHOR]

In the present study, we tested a hypothesis that the rhythm generator in humans keeps the rhythm of periodic motor output during brief inactivation of the pattern generator. This investigation was made through testing whether the step rhythm was reset after an interruptive event. A reset of the step rhythm was defined as an observation that the step re-emerges at random timing after an interruptive event regardless of the step rhythm before the interruption. This observation reflects an intermission of rhythm-keeping activity. Healthy participants stepped on a platform that could translate forward or backward. They continued stepping after the platform translation (non-stop session) or stopped briefly after the translation before resuming step with their own timing (stop session). In the non-stop session, the second step after the platform translation appeared at the integer multiple of the pre-existing step period in most participants, indicating that step rhythm was not reset. This finding indicates that postural perturbation does not interfere the rhythm-keeping activity. In the stop session, the step immediately after the intermission of stepping appeared at random time regardless of the step rhythm before the intermission in most participants. The actual side of the first step after the intermission was consistent with the predicted first step side at a 0.5 probability. Those findings indicate that step rhythm is reset after brief intermission of stepping, and contradict with the hypothesis that the activity of the rhythm generator is maintained, while the pattern generator is temporally inactive during a brief intermission of periodic motor output. This analysis could help to determine whether rhythm-keeping activity is inactivated by an interruptive event during periodic motor activity. [ABSTRACT FROM AUTHOR]

As the engine behind many life phenomena, motor information generated by the central nervous system plays a critical role in the activities of all animals. In this work, a novel, macroscopic and model-independent approach is presented for creating different patterns of coupled neural oscillations observed in biological central pattern generators (CPG) during the control of legged locomotion. Based on a simple distributed state machine, which consists of two nodes sharing pre-defined number of resources, the concept of oscillatory building blocks (OBBs) is summarised for the production of elaborated rhythmic patterns. Various types of OBBs can be designed to construct a motion joint of one degree of freedom with adjustable oscillatory frequencies and duty cycles. An OBB network can thus be potentially built to generate a full range of locomotion patterns of a legged animal with controlled transitions between different rhythmic patterns. It is shown that gait pattern transition can be achieved by simply changing a single parameter of an OBB module. Essentially, this simple mechanism allows for the consolidation of a methodology for the construction of artificial CPG architectures behaving as an asymmetric Hopfield neural network. Moreover, the proposed CPG model introduced here is amenable to analogue and/or digital circuit integration. [ABSTRACT FROM AUTHOR]

Many neuronal circuits driving coordinated locomotion are composed of chains of half-center oscillators (HCOs) of various lengths. The HCO is a common motif in central pattern generating circuits (CPGs); an HCO consists of two neurons, or two neuronal populations, connected by reciprocal inhibition. To maintain appropriate motor coordination for effective locomotion over a broad range of frequencies, chains of CPGs must produce approximately constant phase-differences in a robust manner. In this article, we study phase-locking in chains of nearest-neighbor coupled HCOs and examine how the circuit architecture can promote phase-constancy, i.e., inter-HCO phase-differences that are frequency-invariant. We use two models with different levels of abstraction: (1) a conductance-based model in which each neuron is modeled by the Morris-Lecar equations (the ML-HCO model); and (2) a coupled phase model in which the state of each HCO is captured by its phase (the phase-HCO model). We show that one of four phase-waves with inter-HCO phase-differences at approximately 0, 25, 50 or 75 % arises robustly as a result of the inter-HCO connection topology, and its robust existence is not affected by the number of HCOs in the chain, the difference in strength between the ascending and descending nearest-neighbor connections, or the number of nearest-neighbor connections. Our results show that the internal anti-phase structure of the HCO and an appropriate inter-HCO connection topology together can provide a mechanism for robust (i.e., frequency-independent) limb coordination in segmented animals, such as the 50 % interlimb phase-differences in the tripod gate of stick insects and cockroaches, and the 25 % interlimb phase-differences in crayfish and other long-tailed crustaceans during forward swimming. [ABSTRACT FROM AUTHOR]

The crustacean stomatogastric nervous system is a classic model for understanding the effects of modulating ionic currents and synapses at both the cell and network levels. The stomatogastric ganglion in this system contains two distinct central pattern generators: a slow gastric mill network that generates flexible rhythmic outputs (8-20 s) and is often silent, and a fast pyloric network that generates more consistent rhythmic outputs (0.5-2 s) and is always active in vitro. Different ionic conductances contribute to the properties of individual neurons and therefore to the overall dynamics of the pyloric and gastric mill networks. However, the contributions of ionic currents to different dynamics between the pyloric and gastric mill networks are not well understood. The goal of this study is to evaluate how changes in outward potassium current ( I ) in the stomatogastric ganglion affect the dynamics of the pyloric and gastric mill rhythms by interfering with normal I activity. We bath-applied the specific I blocker 4-aminopyridine to reduce I 's effect in the stomatogastric ganglion in vitro and evaluated quantitatively the changes in both rhythms. We found that blocking I in the stomatogastric ganglion alters the synchronization between pyloric neurons, and consistently activates the gastric mill rhythm in quiescent preparations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motor systems must adapt to perturbations and changing conditions both within and outside the body. We refer to the ability of a system to maintain performance despite perturbations as 'robustness,' and the ability of a system to deploy alternative strategies that improve fitness as 'flexibility.' Different classes of pattern-generating circuits yield dynamics with differential sensitivities to perturbations and parameter variation. Depending on the task and the type of perturbation, high sensitivity can either facilitate or hinder robustness and flexibility. Here we explore the role of multiple coexisting oscillatory modes and sensory feedback in allowing multiphasic motor pattern generation to be both robust and flexible. As a concrete example, we focus on a nominal neuromechanical model of triphasic motor patterns in the feeding apparatus of the marine mollusk Aplysia californica. We find that the model can operate within two distinct oscillatory modes and that the system exhibits bistability between the two. In the 'heteroclinic mode,' higher sensitivity makes the system more robust to changing mechanical loads, but less robust to internal parameter variations. In the 'limit cycle mode,' lower sensitivity makes the system more robust to changes in internal parameter values, but less robust to changes in mechanical load. Finally, we show that overall performance on a variable feeding task is improved when the system can flexibly transition between oscillatory modes in response to the changing demands of the task. Thus, our results suggest that the interplay of sensory feedback and multiple oscillatory modes can allow motor systems to be both robust and flexible in a variable environment. [ABSTRACT FROM AUTHOR]

The article focuses on locomotion control of a snake-like robot with cardan joints using a central pattern generator (CPG) approach. A double chain structure of a CPG model is developed based on nonlinear oscillators connected with diffusive couplings. The proposed CPG model has the ability to produce stable rhythmic patterns applied both in the serpentine locomotion and sidewinding locomotion of snake robots. The global exponential stability of the model is also presented using the partial contraction theory. An important point addressed in this paper is that the proposed CPG model has explicit control parameters including not only frequencies of oscillation and amplitudes of oscillation but also phase differences between the neighbor oscillators. The method to adjust the speed and direction of the snake robot during the locomotion is discussed by modulating the control parameters in the proposed CPG model directly. Simulation results together with the experiments on a real snake robot show that the proposed CPG approach can be used to control snake robots successfully. [ABSTRACT FROM AUTHOR]

The effective and accurate walking is the biggest challenge for humanoid robot locomotion. In dynamic environments, such as RoboCup competition, not only must the speed of actions be high, but action switching must also be done almost immediately. This paper concentrates on two major behavior actions of humanoid soccer robot; straight walking and turning actions. Matsuoka central pattern generator model is used to generate trajectory actions. Both actions have their own parameters, which are obtained by comprehensive learning particle swarm optimization. By using these two actions and proper switching between them, robot can reach every point in the environment within a reasonable time. This paper tries to highlight the importance of action switching in movement maneuverability and proposes an effective solution for it. As transition from one action to another cannot be done in every posture of robot, the switching is done when robot is in double support phase. In this situation, membrane potential of each neuron has its minimum value and switching does not create big torque. The maximum time required for change action in this model is less than one step of robot. This method has been successfully implemented in rcssserver3d simulation server on NAO humanoid robot. [ABSTRACT FROM AUTHOR]

Locomotion is produced by a central pattern generator. Its spinal cord organization is generally considered to be distributed, with more rhythmogenic rostral lumbar segments. While this produces a rostrocaudally traveling wave in undulating species, this is not thought to occur in limbed vertebrates, with the exception of the interneuronal traveling wave demonstrated in fictive cat scratching (Cuellar et al. J Neurosci 29:798-810, 2009). Here, we reexamine this hypothesis in the frog, using the seven muscle synergies A to G previously identified with intraspinal NMDA (Saltiel et al. J Neurophysiol 85:605-619, 2001). We find that locomotion consists of a sequence of synergy activations (A-B-G-A-F-E-G). The same sequence is observed when focal NMDA iontophoresis in the spinal cord elicits a caudal extension-lateral force-flexion cycle (flexion onset without the C synergy). Examining the early NMDA-evoked motor output at 110 sites reveals a rostrocaudal topographic organization of synergy encoding by the lumbar cord. Each synergy is preferentially activated from distinct regions, which may be multiple, and partially overlap between different synergies. Comparing the sequence of synergy activation in locomotion with their spinal cord topography suggests that the locomotor output is achieved by a rostrocaudally traveling wave of activation in the swing-stance cycle. A two-layer circuitry model, based on this topography and a traveling wave reproduces this output and explores its possible modifications under different afferent inputs. Our results and simulations suggest that a rostrocaudally traveling wave of excitation takes advantage of the topography of interneuronal regions encoding synergies, to activate them in the proper sequence for locomotion. [ABSTRACT FROM AUTHOR]

Insects, like dung beetles, show fascinating locomotor abilities. They can use their legs to walk on complex terrains (e.g., rocky and curved surfaces) and to manipulate objects. They also exploit their compliant tarsi, increasing the contact area between the legs and surface, to enhance locomotion, and object manipulation efficiency. Besides these biomechanical components, their neural control allows them to move at a proper frequency with respect to their biomechanical properties and to quickly adapt their movements to deal with environmental changes. Realizing these complex achievements on artificial systems remains a grand challenge. As a step towards this direction, we present here our first prototype of an artificial dung beetle-like leg with compliant tarsus by analyzing real dung beetle legs through $$\mu$$ CT scans. Compliant tarsus was designed according to the so-called fin ray effect. Real robot experiments show that the leg with compliant tarsus can efficiently move on rocky and curved surfaces. We also apply neural control, based on a central pattern generator (CPG) circuit and synaptic plasticity, to autonomously generate a proper moving frequency of the leg. The controller can also adapt the leg movement to deal with environmental changes, like different treadmill speeds, within a few steps. [ABSTRACT FROM AUTHOR]

Central pattern generators consist of sets of interconnected neurons able to generate a basic motor output pattern underlying automatic movements (respiration, locomotion, chewing, swallowing, etc.) without any afferent signals from the executive motor apparatus. They are divided into constitutive pattern generators, which are active throughout life (the respiratory generator), and conditional pattern generators, which control episodic movements (locomotion, chewing, swallowing, etc.). As the motor output of a pattern generator is defined by its internal organization, the activity of conditional pattern generators is initiated by a simple command arriving from the higher centers. The structural-functional organization of the locomotor pattern generators in the marine mollusk Clione, the lamprey, the frog embryo, and laboratory mammals (cats, mice, and rats) are described, along with pattern generators controlling respiratory and swallowing movements in mammals and pattern generators for discharges of the electric organs of gymnotiform fish. Generation of the rhythmic motor output is shown in all cases to be based on the endogenous (pacemaker) activity of specific groups of interneurons and interneural interactions. These two interacting mechanisms supplement each other, ensuring the reliable operation of pattern generators. The question of whether experience gained from studies of central pattern generators can be used for understanding the mechanisms of more complex brain functions, including cognitive functions, is discussed. [ABSTRACT FROM AUTHOR]