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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]

Rhythmic jaw muscle activities (RJMAs) occur during rapid eye movement (REM) sleep in humans and animals even though motoneurons are inhibited. The present study compared the characteristics of jaw muscle activities induced by electrical microstimulations of the corticobulbar tract (CT) during REM sleep with those during wakefulness and non-REM sleep. Eleven guinea pigs were surgically prepared for polygraphic recordings with the implantation of a stimulating electrode. Long- and short-train repetitive electrical microstimulations were applied to the CT under freely moving conditions. The response rate, latency, burst amplitude, and cycle length in the digastric muscle were calculated and cortical and cardiac activities were quantified. Long-train microstimulations induced RJMAs in the digastric muscle followed by masseter muscle activity during wakefulness and non-REM sleep and only induced rhythmic digastric muscle activity during REM sleep. The response rate of RJMAs and the burst amplitude of digastric muscles were significantly lower during REM sleep than during wakefulness and non-REM sleep. However, response latency did not significantly differ between REM sleep and wakefulness. Transient cortical and cardiac changes were associated with RJMAs induced during non-REM sleep, but not during REM sleep. Short-train microstimulations induced a short-latency digastric response, the amplitude of which was significantly lower during REM sleep than during non-REM sleep and wakefulness. These results suggest that the masticatory CPG was activated by electrical CT stimulations independently of the motoneuron inhibitory system during REM sleep. [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]

Short-term synaptic plasticity is found in many areas of the central nervous system. In the inhibitory half-center central pattern generators involved in locomotion, synaptic depression is believed to act as a burst termination mechanism, allowing networks to generate anti-phase bursting patterns of varying periods. To better understand burst generation in these central pattern generators, we study a minimal network of two neurons coupled through depressing synapses. Depending on the strength of the synaptic conductance between the two neurons, this network can produce symmetric n : n anti-phase bursts, where neurons fire n spikes in alternation, with the period of such solutions increasing with the strength of the synaptic conductance. Relying on the timescale disparity in the model, we reduce the eight-dimensional network equations to a fully-explicit scalar Poincaré burst map. This map tracks the state of synaptic depression from one burst to the next and captures the complex bursting dynamics of the network. Fixed points of this map are associated with stable burst solutions of the full network model, and are created through fold bifurcations of maps. We derive conditions that predict the bifurcations between n : n and (n + 1) : (n + 1) solutions, producing a full bifurcation diagram of the burst cycle period. Predictions of the Poincaré map fit excellently with numerical simulations of the full network model and allow the study of parameter sensitivity for rhythm generation. [ABSTRACT FROM AUTHOR]

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots. [ABSTRACT FROM AUTHOR]

Many neurons in the central nervous system produce a single primary cilium that serves as a specialized signaling organelle. Several neuromodulatory G-protein-coupled receptors (GPCRs) localize to primary cilia in neurons, although it is not understood how GPCR signaling from the cilium impacts circuit function and behavior. We find that the vertebrate ancient long opsin A (VALopA), a Gi-coupled GPCR extraretinal opsin, targets to cilia of zebrafish spinal neurons. In the developing 1-d-old zebrafish, brief light activation of VALopA in neurons of the central pattern generator circuit for locomotion leads to sustained inhibition of coiling, the earliest form of locomotion. We find that a related extraretinal opsin, VALopB, is also Gi-coupled, but is not targeted to cilia. Light-induced activation of VALopB also suppresses coiling, but with faster kinetics. We identify the ciliary targeting domains of VALopA. Retargeting of both opsins shows that the locomotory response is prolonged and amplified when signaling occurs in the cilium. We propose that ciliary localization provides a mechanism for enhancing GPCR signaling in central neurons.

The design of motion control systems for legged robots has always been a challenge. This article first proposes a motion control method for legged robots based on the gradient central pattern generator (GD-CPG). The periodic signals output from the GD-CPG neural network are used as the drive signals of each thigh joint of the legged robots, which are then converted into the driving signal of the knee and ankle joints by the thigh–knee mapping function and the knee–ankle mapping function. The proposed control algorithm is adapted to quadruped and hexapod robots. To improve the ability of legged robots to cope with complex terrains, this article further proposes the responsive gradient-CPG motion control method for legged robots. From the perspective of bionics, a biological vestibular sensory feedback mechanism is established in the control system. The mechanism adjusts the robot's motion state in real time through the attitude angle of the body measured during the robot's motion, to keep the robot's body stable when it moves in rugged terrains. Compared with the traditional feedback model that only balances the body pitch, this article also adds the balancing functions of body roll and yaw to balance the legged robot's motion from more dimensions and improve the linear motion capability. This article also introduces a differential evolutionary algorithm and designs a fitness function to adaptively optimize vestibular sensory feedback parameters. The validity, robustness, and transferability of the method are verified through simulations and physical experiments. [ABSTRACT FROM AUTHOR]

Background: The states of the central nervous system (CNS) can be classified into subcritical, critical, and supercritical states that endow the system with information capacity, transmission capabilities, and dynamic range. A further investigation of the relationship between the CNS and the central pattern generators (CPG) is warranted to provide insight into the mechanisms that govern the locomotion system. Methods: In this study, we established a fractional-order CPG model based on an extended Hindmarsh-Rose model with time delay. A CNS model was further established using a recurrent excitation-inhibition neuronal network. Coupling between these CNS and CPG models was then explored, demonstrating a potential means by which oscillations generated by a neural network respond to periodic stimuli. Results and Conclusions: These simulations yielded two key sets of findings. First, frequency sliding was observed when the CPG was sent to the CNS in the subcritical, critical, and supercritical states with different external stimulus and fractional-order index values, indicating that frequency sliding regulates brain function on multiple spatiotemporal scales when the CPG and CNS are coupled together. The main frequency range for these simulations was observed in the gamma band. Second, with increasing external inputs the coherence index for the CNS decreases, demonstrating that strong external inputs introduce neuronal stochasticity. Neural network synchronization is then reduced, triggering irregular neuronal firing. Together these results provide novel insight into the potential mechanisms that may underlie the locomotion system. [ABSTRACT FROM AUTHOR]

A symmetric gait pattern in humans reflects near-identical movement in bilateral limbs during walking. However, little is known about how gait symmetry changes on different inclines. This study aimed to address this knowledge gap using the central pattern generator and internal model hypotheses. Eighteen healthy young adults underwent five 2-minute walking trials (inclines of +15%, +8%, 0%, −8%, and −15%). Dependent variables included step time, step length, step width, maximum heel clearance, time to peaks of maximum heel clearance, their corresponding coefficients of variation (CV), and respective symmetry indices (SI). Significant differences were observed in SI of step length (p =.022), step length variability (p <.001), step width variability (p =.001), maximum heel clearance (p <.001), and maximum heel clearance variability (p =.049). Compared to level walking, walking at −8% and −15% inclines increased SI of step length (p =.011, p =.039 respectively) but decreased SI of maximum heel clearance (p =.025, p =.019 respectively). These observations suggested that incline walking affected gait symmetry differently, possibly due to varied internal models used in locomotion. Downhill walking improved vertical gait symmetry but reduced anterior-posterior symmetry compared to level walking. Downhill walking may be a preferable rehabilitation protocol for enhancing gait symmetry, as it activates internal model controls. Even slight downhill inclines could increase active control loading, beneficial for the elderly and those with impaired gait. [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]

Introduction: Aspiration pneumonia, a leading cause of mortality, poses an urgent challenge in contemporary society. Neuromuscular electrical stimulation (NMES) has been commonly used in dysphagia rehabilitation. However, given that NMES at motor threshold targets only specific muscles, it carries a potential risk of further compromising functions related to swallowing, respiration, and airway protection. Considering that the swallow motor pattern is orchestrated by the entire swallow pattern generator (the neural mechanism governing a sequence of swallow actions), a rehabilitation approach that centrally facilitates the entire circuit through sensory nerve stimulation is desirable. In this context, we propose a novel stimulation method using surface electrodes placed on the back to promote swallowing. Methods: The efficacy of the proposed method in promoting swallowing was evaluated by electrically stimulating sensory nerves in the back or neck. Probabilistic stimulus was applied to either the back or neck of male and female rats. Swallows were evoked by an oral water stimulus, and electromyographic (EMG) activity of the mylohyoid, thyroarytenoid, and thyropharyngeus muscles served as the primary outcome measure. Results: Gaussian frequency stimulation applied to the skin surface of the thoracic back elicited significant increases in EMG amplitude of all three swallow-related muscles. Neck stimulation elicited a significant increase in EMG amplitude of the thyroarytenoid during swallow, but not the mylohyoid or thyropharyngeus muscles. Discussion: While the targeted thoracic spinal segments T9-T10 have been investigated for enhancing respiration, the promotion of swallowing through back stimulation has not been previously studied. Furthermore, this study introduces a new probabilistic stimulus based on Gaussian distribution. Probabilistic stimuli have been reported to excel in nerve stimulation in previous research. The results demonstrate that back stimulation effectively facilitated swallow more than neck stimulation and suggest potential applications for swallowing rehabilitation. [ABSTRACT FROM AUTHOR]

Oscillatory networks underlying rhythmic motor behaviors, and sensory and complex neural processing, are flexible, even in their neuronal composition. Neuromodulatory inputs enable neurons to switch participation between networks or participate in multiple networks simultaneously. Neuromodulation of internetwork synapses can both recruit and coordinate a switching neuron in a second network. We previously identified an example in which a neuron is recruited into dual-network activity via peptidergic modulation of intrinsic properties. We now ask whether the same neuropeptide also modulates internetwork synapses for internetwork coordination. The crab (Cancer borealis) stomatogastric nervous system contains two well-defined feeding-related networks (pyloric, food filtering, ∼1 Hz; gastric mill, food chewing, ∼0.1 Hz). The projection neuron MCN5 uses the neuropeptide Gly1-SIFamide to recruit the pyloric-only lateral posterior gastric (LPG) neuron into dual pyloric- plus gastric mill-timed bursting via modulation of LPG's intrinsic properties. Descending input is not required for a coordinated rhythm, thus intranetwork synapses between LPG and its second network must underlie coordination among these neurons. However, synapses between LPG and gastric mill neurons have not been documented. Using two-electrode voltage-clamp recordings, we found that graded synaptic currents between LPG and gastric mill neurons (lateral gastric, inferior cardiac, and dorsal gastric) were primarily negligible in saline, but were enhanced by Gly1-SIFamide. Furthermore, LPG and gastric mill neurons entrain each other during Gly1-SIFamide application, indicating bidirectional, functional connectivity. Thus, a neuropeptide mediates neuronal switching through parallel actions, modulating intrinsic properties for recruitment into a second network and as shown here, also modulating bidirectional internetwork synapses for coordination. NEW & NOTEWORTHY: Neuromodulation can enable neurons to simultaneously coordinate with separate networks. Both recruitment into, and coordination with, a second network can occur via modulation of internetwork synapses. Alternatively, recruitment can occur via modulation of intrinsic ionic currents. We find that the same neuropeptide previously determined to modulate intrinsic currents also modulates bidirectional internetwork synapses that are typically ineffective. Thus, complementary modulatory peptide actions enable recruitment and coordination of a neuron into a second network. [ABSTRACT FROM AUTHOR]

Inhibitory neurons embedded within mammalian neural circuits shape breathing, walking, and other rhythmic motor behaviors. At the core of the neural circuit controlling breathing is the preBötzinger Complex (preBötC), where GABAergic (GAD1/2+) and glycinergic (GlyT2+) neurons are functionally and anatomically intercalated among glutamatergic Dbx1-derived (Dbx1+) neurons that generate rhythmic inspiratory drive. The roles of these preBötC inhibitory neurons in breathing remain unclear. We first characterized the spatial distribution of molecularly defined preBötC inhibitory subpopulations in male and female neonatal double reporter mice expressing either tdTomato or EGFP in GlyT2+, GAD1+, or GAD2+ neurons. We found that the majority of preBötC inhibitory neurons expressed both GlyT2 and GAD2 while a much smaller subpopulation also expressed GAD1. To determine the functional role of these subpopulations, we used holographic photostimulation, a patterned illumination technique, in rhythmically active medullary slices from neonatal Dbx1tdTomato;GlyT2EGFP and Dbx1tdTomato;GAD1EGFP double reporter mice of either sex. Stimulation of 4 or 8 preBötC GlyT2+ neurons during endogenous rhythm prolonged the interburst interval in a phase-dependent manner and increased the latency to burst initiation when bursts were evoked by stimulation of Dbx1+ neurons. In contrast, stimulation of 4 or 8 preBötC GAD1+ neurons did not affect interburst interval or latency to burst initiation. Instead, photoactivation of GAD1+ neurons during the inspiratory burst prolonged endogenous and evoked burst duration and decreased evoked burst amplitude. We conclude that GlyT2+/GAD2+ neurons modulate breathing rhythm by delaying burst initiation while a smaller GAD1+ subpopulation shapes inspiratory patterning by altering burst duration and amplitude. [ABSTRACT FROM AUTHOR]

Biological experiments have confirmed that the locomotion system includes the nervous and musculoskeletal systems, and central pattern generator (CPG). The motor unit (MU) is the core part of the musculoskeletal system. Based on the biological experiments, it is confirmed that the CPG can generate and distribute the motor rhythms and patterns to different MUs through flexible weights, and the MUs feedback can affect the output of CPG. Then, the MU model, the CPG model, and their coupling model are investigated to explain the biological phenomena. Within this work, it was found that there is consistency between the shape of states generated by the three MUs in this work and the experimental results in previous studies. When the MUs correspond to the parts in limbs, and the CPG connects to the MUs via synapses, the locomotion pattern can be adjusted by CPG to use the variable synaptic weights. Besides, the model with fractional order (FO) has better efficiency than the model with integer order. Therefore, the model investigates the relationship between the CPG and the MU and provides a novel technique to introduce the FO coupling model for controlling the locomotion state. [ABSTRACT FROM AUTHOR]

Locomotion allows us to move and interact with our surroundings. Spinal networks that control locomotion produce rhythm and left–right and flexor–extensor coordination. Several glutamatergic populations, Shox2 non-V2a, Hb9-derived interneurons, and, recently, spinocerebellar neurons have been proposed to be involved in the mouse rhythm generating networks. These cells make up only a smaller fraction of the excitatory cells in the ventral spinal cord. Here, we set out to identify additional populations of excitatory spinal neurons that may be involved in rhythm generation or other functions in the locomotor network. We use RNA sequencing from glutamatergic, non-glutamatergic, and Shox2 cells in the neonatal mice from both sexes followed by differential gene expression analyses. These analyses identified transcription factors that are highly expressed by glutamatergic spinal neurons and differentially expressed between Shox2 neurons and glutamatergic neurons. From this latter category, we identified the Lhx9-derived neurons as having a restricted spinal expression pattern with no Shox2 neuron overlap. They are purely glutamatergic and ipsilaterally projecting. Ablation of the glutamatergic transmission or acute inactivation of the neuronal activity of Lhx9- derived neurons leads to a decrease in the frequency of locomotor-like activity without change in coordination pattern. Optogenetic activation of Lhx9-derived neurons promotes locomotor-like activity and modulates the frequency of the locomotor activity. Calcium activities of Lhx9-derived neurons show strong left–right out-of-phase rhythmicity during locomotor-like activity. Our study identifies a distinct population of spinal excitatory neurons that regulates the frequency of locomotor output with a suggested role in rhythm-generation in the mouse alongside other spinal populations. [ABSTRACT FROM AUTHOR]

The Motor Ganglion (MG) is a small collection of neurons that control the swimming movements of the tunicate tadpole larva. Situated at the base of the tail, molecular and functional comparisons suggest that may be a homolog of the spinal cord and/or hindbrain ("rhombospinal" region) of vertebrates. Here we review the most current knowledge of the development, connectivity, functions, and unique identities of the neurons that comprise the MG, drawn mostly from studies in Ciona spp. The simple cell lineages, minimal cellular composition, and comprehensively mapped "connectome" of the Ciona MG all make this an excellent model for studying the development and physiology of motor control in aquatic larvae. Research Highlights: We review the latest research on the development, circuitry, and function of the Motor Ganglion of tunicate larvae, focused primarily on the most well‐studied species in the Ciona genus. [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]

Objective: The sequential generation of swallowing pressure (SP) from the nasopharynx to the proximal esophagus is important for the bolus to pass from the oral cavity to the esophagus. The purpose of this study was to investigate the correlation of the SP sequence mode on high‐resolution manometry (HRM) with oral intake difficulty and aspiration pneumonia. Methods: Consecutive patients with dysphagia who were admitted to our dysphagia clinic between November 2016 and November 2020 were enrolled in this cross‐sectional study. We classified the HRM pressure topography data according to the SP sequence mode into type A, normal; B, partially decreased; C, totally decreased; and D, sequence disappeared, and according to the upper esophageal sphincter (UES) during pharyngeal swallowing into type 1, flattening and 2, non‐flattening. Clinical dysphagia severity was determined based on oral intake difficulty and aspiration pneumonia. Results: In total, 202 patients with dysphagia (mean [standard deviation] age, 68.3 [14.5] years; 140 [69.8%] male) were enrolled. Type C (odds ratio [OR], 10.48; 95% confidence interval [CI], 2.89–51.45), type D (OR, 19.90; 95% CI, 4.18–122.35), and type 2 (OR, 6.36; 95% CI, 2.88–14.57) were significantly related to oral intake difficulty. Type C (OR, 3.23; 95% CI, 1.08–11.12) and type 2 (OR, 4.18; 95% CI, 1.95–9.15) were significantly associated with aspiration pneumonia. Conclusion: The failure of sequential generation of SP was associated with higher risk of oral intake difficulty and aspiration pneumonia. These assessments are useful in understanding the pathophysiology and severity of dysphagia and in selecting safety nutritional management methods. Level of Evidence: 4 Laryngoscope, 134:2127–2135, 2024 [ABSTRACT FROM AUTHOR]

A symmetric gait pattern in humans reflects near-identical movement in bilateral limbs during walking. However, little is known about how gait symmetry changes on different inclines. This study aimed to address this knowledge gap using the central pattern generator and internal model hypotheses. Eighteen healthy young adults underwent five 2-minute walking trials (inclines of +15%, +8%, 0%, −8%, and −15%). Dependent variables included step time, step length, step width, maximum heel clearance, time to peaks of maximum heel clearance, their corresponding coefficients of variation (CV), and respective symmetry indices (SI). Significant differences were observed in SI of step length (p = .022), step length variability (p

Intonation in speech is the control of vocal pitch to layer expressive meaning to communication, like increasing pitch to indicate a question. Also, stereotyped patterns of pitch are used to create distinct sounds with different denotations, like in tonal languages and, perhaps, the 10 sounds in the murine lexicon. A basic tone is created by exhalation through a constricted laryngeal voice box, and it is thought that more complex utterances are produced solely by dynamic changes in laryngeal tension. But perhaps, the shifting pitch also results from altering the swiftness of exhalation. Consistent with the latter model, we describe that intonation in most vocalization types follows deviations in exhalation that appear to be generated by the re-activation of the cardinal breathing muscle for inspiration. We also show that the brainstem vocalization central pattern generator, the iRO, can create this breath pattern. Consequently, ectopic activation of the iRO not only induces phonation, but also the pitch patterns that compose most of the vocalizations in the murine lexicon. These results reveal a novel brainstem mechanism for intonation.

Central pattern generators (CPGs) are biological neural circuits that allow vertebrates to control rhythmic and motor behavior by generating rhythmic output without rhythmic input. The synthetic generation of CPGs has attracted attention to control robots for locomotion tasks. The above has led to the design of CPGs using spiking neural networks (SNNs) or systems of coupled oscillators. Nevertheless, using SNNs for CPG design has drawbacks, such as a lack of interpretability and difficult training. On the other hand, coupled oscillators require trial and error tuning of many parameters and may face stability and synchronization issues between oscillators. This study proposed utilizing Phase-Locked Loops (PLLs) combined with adjustable amplitude van der Pol (VDP) oscillators for synthetic CPG design used for locomotion tasks. Each VDP-PLL unit acts as an analog for a neuron within a biological CPG, forming a synchronized network that resembles rhythmic, coordinated movements. Furthermore, the proposed VDP-PLL was compared to classical coupled oscillators such as the Kuramoto and Hopf. The results demonstrate that the VDP-PLL method effectively overcomes the typical challenges of conventional coupled oscillator implementations in CPGs, since it benefits from a robust mathematical model utilizing four non-heuristic variables, allowing system parameterization using classical control system methodologies. The system can self-correct within an adequate time frame in response to variations in oscillation parameters. Furthermore, the VDP-PLL system has the potential to be implemented using analog electronics, thereby mitigating processing limitations in embedded systems. Our findings indicate that this method offers a promising pathway for achieving adaptable and transparent bioinspired motion.

In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity.

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots.

Breathing behaviour involves the generation of normal breaths (eupnoea) on a timescale of seconds and sigh breaths on the order of minutes. Both rhythms emerge in tandem from a single brainstem site, but whether and how a single cell population can generate two disparate rhythms remains unclear. We posit that recurrent synaptic excitation in concert with synaptic depression and cellular refractoriness gives rise to the eupnoea rhythm, whereas an intracellular calcium oscillation that is slower by orders of magnitude gives rise to the sigh rhythm. A mathematical model capturing these dynamics simultaneously generates eupnoea and sigh rhythms with disparate frequencies, which can be separately regulated by physiological parameters. We experimentally validated key model predictions regarding intracellular calcium signalling. All vertebrate brains feature a network oscillator that drives the breathing pump for regular respiration. However, in air‐breathing mammals with compliant lungs susceptible to collapse, the breathing rhythmogenic network may have refashioned ubiquitous intracellular signalling systems to produce a second slower rhythm (for sighs) that prevents atelectasis without impeding eupnoea. Key points: A simplified activity‐based model of the preBötC generates inspiratory and sigh rhythms from a single neuron population.Inspiration is attributable to a canonical excitatory network oscillator mechanism.Sigh emerges from intracellular calcium signalling.The model predicts that perturbations of calcium uptake and release across the endoplasmic reticulum counterintuitively accelerate and decelerate sigh rhythmicity, respectively, which was experimentally validated.Vertebrate evolution may have adapted existing intracellular signalling mechanisms to produce slow oscillations needed to optimize pulmonary function in mammals. [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]

Network flexibility is important for adaptable behaviors. This includes neuronal switching, where neurons alter their network participation, including changing from single- to dual-network activity. Understanding the implications of neuronal switching requires determining how a switching neuron interacts with each of its networks. Here, we tested 1) whether "home" and second networks, operating via divergent rhythm generation mechanisms, regulate a switching neuron and 2) if a switching neuron, recruited via modulation of intrinsic properties, contributes to rhythm or pattern generation in a new network. Small, well-characterized feeding-related networks (pyloric, ~1 Hz; gastric mill, ~0.1 Hz) and identified modulatory inputs make the isolated crab (Cancer borealis) stomatogastric nervous system (STNS) a useful model to study neuronal switching. In particular, the neuropeptide Gly1-SIFamide switches the lateral posterior gastric (LPG) neuron (2 copies) from pyloric-only to dual-frequency pyloric/gastric mill (fast/slow) activity via modulation of LPG-intrinsic properties. Using current injections to manipulate neuronal activity, we found that gastric mill, but not pyloric, network neurons regulated the intrinsically generated LPG slow bursting. Conversely, selective elimination of LPG from both networks using photoinactivation revealed that LPG regulated gastric mill neuron-firing frequencies but was not necessary for gastric mill rhythm generation or coordination. However, LPG alone was sufficient to produce a distinct pattern of network coordination. Thus, modulated intrinsic properties underlying dual-network participation may constrain which networks can regulate switching neuron activity. Furthermore, recruitment via intrinsic properties may occur in modulatory states where it is important for the switching neuron to actively contribute to network output. [ABSTRACT FROM AUTHOR]

Neuromodulation lends flexibility to neural circuit operation but the general notion that different neuromodulators sculpt neural circuit activity into distinct and characteristic patterns is complicated by interindividual variability. In addition, some neuromodulators converge onto the same signaling pathways, with similar effects on neurons and synapses. We compared the effects of three neuropeptides on the rhythmic pyloric circuit in the stomatogastric ganglion of male crabs, Cancer borealis. Proctolin (PROC), crustacean cardioactive peptide (CCAP), and red pigment concentrating hormone (RPCH) activate the same modulatory inward current, IMI, and have convergent actions on synapses. However, while PROC targets all four neuron types in the core pyloric circuit, CCAP and RPCH target the same subset of only two neurons. After removal of spontaneous neuromodulator release, none of the neuropeptides restored the control cycle frequency, but all restored the relative timing between neuron types. Consequently, differences between neuropeptide effects were mainly found in the spiking activity of different neuron types. We performed statistical comparisons using the Euclidean distance in the multidimensional space of normalized output attributes to obtain a single measure of difference between modulatory states. Across preparations, the circuit output in PROC was distinguishable from CCAP and RPCH, but CCAP and RPCH were not distinguishable from each other. However, we argue that even between PROC and the other two neuropeptides, population data overlapped enough to prevent reliable identification of individual output patterns as characteristic for a specific neuropeptide. We confirmed this notion by showing that blind classifications by machine learning algorithms were only moderately successful. [ABSTRACT FROM AUTHOR]

In this paper, we explore the mechanisms of central pattern generators (CPGs), circuits that can generate rhythmic patterns of motor activity without external input. We study the half-center oscillator, a simple form of CPG circuit consisting of neurons connected by reciprocally inhibitory synapses. We examine the role of asymmetric coupling factors in shaping rhythm activity and how different network topologies contribute to network efficiency. We have discovered that neurons with lower synaptic strength are more susceptible to noise that affects rhythm changes. Our research highlights the importance of asymmetric coupling factors, noise, and other synaptic parameters in shaping the broad regimes of CPG rhythm. Finally, we compare three topology types' regular regimes and provide insights on how to locate the rhythm activity. [ABSTRACT FROM AUTHOR]

Mammals walk in different directions, such as forward and backward. In human infants/adults and decerebrate cats, one leg can walk forward and the other backward simultaneously on a split‐belt treadmill, termed hybrid or bidirectional locomotion. The purpose of the present study was to determine if spinal sensorimotor circuits generate hybrid locomotion and if so, how the limbs remain coordinated. We tested hybrid locomotion in 11 intact cats and in five following complete spinal thoracic transection (spinal cats) at three treadmill speeds with the hindlimbs moving forward, backward or bidirectionally. All intact cats generated hybrid locomotion with the forelimbs on a stationary platform. Four of five spinal cats generated hybrid locomotion, also with the forelimbs on a stationary platform, but required perineal stimulation. During hybrid locomotion, intact and spinal cats positioned their forward and backward moving hindlimbs caudal and rostral to the hip, respectively. The hindlimbs maintained consistent left–right out‐of‐phase alternation in the different stepping directions. Our results suggest that spinal locomotor networks generate hybrid locomotion by following certain rules at phase transitions. We also found that stance duration determined cycle duration in the different locomotor directions/conditions, consistent with a common rhythm‐generating mechanism for different locomotor directions. Our findings provide additional insight on how left–right spinal networks and sensory feedback from the limbs interact to coordinate the hindlimbs and provide stability during locomotion in different directions. Key points: Terrestrial mammals can walk forward and backward, which is controlled in part by spinal sensorimotor circuits.Humans and cats also perform bidirectional or hybrid locomotion on a split‐belt treadmill with one leg going forward and the other going backward.We show that cats with a spinal transection can perform hybrid locomotion and maintain left–right out‐of‐phase coordination, indicating that spinal sensorimotor circuits can perform simultaneous forward and backward locomotion.We also show that the regulation of cycle duration and phase duration is conserved across stepping direction, consistent with a common rhythm‐generating mechanism for different stepping directions.The results help us better understand how spinal networks controlling the left and right legs enable locomotion in different directions. [ABSTRACT FROM AUTHOR]

The aim of modeling musculoskeletal systems is to understand the mechanisms of locomotion control in terms of neurophysiology and neuroanatomy. The complexity and unique nature of neuromuscular systems, however, make control problems in these systems very challenging due to several characteristics including speed and precision. Thus, their investigation requires the use of simple and analyzable methods. Consequently, taking into account the central pattern generator's (CPG) function, we attempted to create a structured chaotic model of how human joints and muscles interact for the purpose of facilitating gait and rehabilitation in patients with incomplete spinal cord injury. The four muscle groups used in this model are gluteus, and hip flexor groups for flexion and extension of the hip joints as well as hamstring muscles and vasti muscles for flexion and extension of the knee joint. The results indicate that the output of the chaotic model of muscle and joint interactions in a healthy state would be chaotic, while in the incomplete spinal cord injury state, it would remain a fixed point. For model rehabilitation, afferent nerve stimulation is used in a CPG model; based on the modeling results, by applying coefficients of 1.98, 2.21, and 3.1 to the values of Ia, II, and Ib afferent nerves, the incomplete spinal cord injury model state is changed from a fix-point to periodic in a permanent fashion, suggesting locomotion with rehabilitation in our model. Based on the results obtained from the chaotic model of muscle and joint interactions as well as the comparisons made with reference papers, it can be stated that this model has acceptable output while enjoying simple computations and can predict different norms. [ABSTRACT FROM AUTHOR]

The control of movement in living organisms represents a fundamental task that the brain has evolved to solve. One crucial aspect is how the nervous system organizes the transformation of sensory information into motor commands. These commands lead to muscle activation and subsequent animal movement, which can exhibit complex patterns. One example of such movement is locomotion, which involves the translation of the entire body through space. Central Pattern Generators (CPGs) are neuronal circuits that provide control signals for these movements. Compared to the intricate circuits found in the brain, CPGs can be simplified into networks of neurons that generate rhythmic activation, coordinating muscle movements. Since the 1990s, researchers have developed numerous models of locomotive circuits to simulate different types of animal movement, including walking, flying, and swimming. Initially, the primary goal of these studies was to construct biomimetic robots. However, it became apparent that simplified CPGs alone were not sufficient to replicate the diverse range of adaptive locomotive movements observed in living organisms. Factors such as sensory modulation, higher-level control, and cognitive components related to learning and memory needed to be considered. This necessitated the use of more complex, high-dimensional circuits, as well as novel materials and hardware, in both modeling and robotics. With advancements in high-power computing, artificial intelligence, big data processing, smart materials, and electronics, the possibility of designing a new generation of true bio-mimetic robots has emerged. These robots have the capability to imitate not only simple locomotion but also exhibit adaptive motor behavior and decision-making. This motivation serves as the foundation for the current review, which aims to analyze existing concepts and models of movement control systems. As an illustrative example, we focus on underwater movement and explore the fundamental biological concepts, as well as the mathematical and physical models that underlie locomotion and its various modulations. • We review state-of-the-art of research on swimming locomotion control in animals, biomechanical simulations and robots. • We focus on the organization of swimming control in animals and biomimetic robots using a central pattern generator (CPG). • We delve into key aspects of motor control physiology and the underlying CPG circuitry and sensory feedback mechanisms. • We survey diverse CPG mathematical models: from biophysically motivated circuits to oscillatory models for virtual robots. • We explore how these models form the foundation for engineering solutions aimed at controlling robot in aquatic locomotion. [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]