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The relative importance of pendulum mechanics and muscle mechanics in chewing dynamics has implications for understanding the optimality criteria driving the evolution of primate feeding systems. The Spring Model (Ross et al., 2009b), which modeled the primate chewing system as a forced mass-spring system, predicted that chew cycle time would increase faster than was actually observed. We hypothesized that if mandibular momentum plays an important role in chewing dynamics, more accurate estimates of the rotational inertia of the mandible would improve the accuracy with which the Spring Model predicts the scaling of primate chew cycle period. However, if mass-related momentum effects are of negligible importance in the scaling of primate chew cycle period, this hypothesis would be falsified. We also predicted that greater “robusticity” of anthropoid mandibles compared with prosimians would be associated with higher moments of inertia. From computed tomography scans, we estimated the scaling of the moment of inertia ( I j ) of the mandibles of thirty-one species of primates, including 22 anthropoid and nine prosimian species, separating I j into the moment about a transverse axis through the center of mass ( I xx ) and the moment of the center of mass about plausible axes of rotation. We found that across primates I j increases with positive allometry relative to jaw length, primarily due to positive allometry of jaw mass and I xx , and that anthropoid mandibles have greater rotational inertia compared with prosimian mandibles of similar length. Positive allometry of I j of primate mandibles actually lowers the predictive ability of the Spring Model, suggesting that scaling of primate chew cycle period, and chewing dynamics in general, are more strongly influenced by factors other than scaling of inertial properties of the mandible, such as the dynamic properties of the jaw muscles and neural control. Differences in cycle period scaling between chewing and locomotion systems reinforce the suggestion that displacement and force control are more important in the design of feeding systems than energetics and speed. [ABSTRACT FROM AUTHOR]

The neuromuscular control of human movement can be described by a set of muscle synergies factorized from myoelectric signals. There is some evidence that the selection, activation and flexible combination of these basic activation patterns are of a neural origin. We investigated the muscle synergies during incline and level walking to evaluate changes in the modular organization of neuromuscular control related to changes in the mechanical demands. Our results revealed five fundamental (not further factorizable) synergies for both walking conditions but with different frequencies of appearance of the respective synergies during incline compared with level walking. Low similarities across conditions were observed in the timing of the activation patterns (motor primitives) and the weightings of the muscles within the respective elements (motor modules) for the synergies associated with the touchdown, mid-stance and early push-off phase. The changes in neuromuscular control could be attributed to changes in the mechanical demands in support, propulsion and medio-lateral stabilization of the body during incline compared with level walking. Our findings provide further evidence that the central nervous system flexibly uses a consistent set of neural control elements with a flexible temporal recruitment and modifications of the relative muscle weightings within each element to provide stable locomotion under varying mechanical demands during walking. [ABSTRACT FROM AUTHOR]

Objective: Recent research has reported that lower maximal rate of torque development (dτ/dt max) exhibited by females, relative to males, during knee extension can be accounted for by normalization to a maximal voluntary contraction (MVC); however, this was not seen in the upper limb.Purpose: The aim of the current work was to examine the contribution of maximum strength (τmax), twitch contraction time (CT), muscle fiber condition velocity (MFCV), and rate of muscle activation (Q30) to sex-differences in the dτ/dt max during maximal isometric dorsiflexion.Methods: Thirty-eight participants (20 males; 18 females) performed both maximal voluntary and evoked isometric contractions of the tibialis anterior across 3 days. Ten maximal compound muscle action potentials were elicited and subsequently followed by three, 5-s contractions. From the recordings, MFCV, dτ/dt max, τmax, CT, electromechanical delay (EMD), root-mean squared (RMS) amplitude, peak-to-peak voltage (Vpp), and Q30 were calculated.Results: An ANCOVA showed that τmax accounted for all the sex-differences in dτ/dt max (p = 0.96). There were no significant differences between groups with respect to MFCV, RMS amplitude, Vpp amplitude, or CT. However, there was a significant sex-difference in dτ/dt max, τmax, and Q30. Females had longer evoked EMD times compared with males (15.69 ± 10.57 ms versus 9.95 ± 3.46 ms; p = 0.01), but the voluntary EMD times were not different.Conclusion: The current research supports the work by Hannah et al. Exp Physiol 97:618-629, (2012) that normalization to MVC in the quadriceps is able to account for all sex-differences in rate of toque development in the lower limb. [ABSTRACT FROM AUTHOR]

Optimization of gait rehabilitation using split-belt treadmills critically depends on our understanding of the roles of somatosensory perception and sensorimotor recalibration in perceiving gait asymmetry and adapting to split-belt walking. Recent evidence justifies the hypothesis that perception of gait asymmetry is based mainly on detection of temporal mismatches between afferent inputs at the spinal level. [ABSTRACT FROM AUTHOR]

Crawling animals with bendable soft bodies use the friction anisotropy of their asymmetric body structures to traverse various substrates efficiently. Although the effect of friction anisotropy has been investigated and applied to robot locomotion, the dynamic interactions between soft body bending at different frequencies (low and high), soft asymmetric surface structures at various aspect ratios (low, medium, and high), and different substrates (rough and smooth) have not been studied comprehensively. To address this lack, we developed a simple soft robot model with a bioinspired asymmetric structure (sawtooth) facing the ground. The robot uses only a single source of pressure for its pneumatic actuation. The frequency, teeth aspect ratio, and substrate parameters and the corresponding dynamic interactions were systematically investigated and analyzed. The study findings indicate that the anterior and posterior parts of the structure deform differently during the interaction, generating different frictional forces. In addition, these parts switched their roles dynamically from push to pull and vice versa in various states, resulting in the robot's emergent locomotion. Finally, autonomous adaptive crawling behavior of the robot was demonstrated using sensor-driven neural control with a miniature laser sensor installed in the anterior part of the robot. The robot successfully adapted its actuation frequency to reduce body bending and crawl through a narrow space, such as a tunnel. The study serves as a stepping stone for developing simple soft crawling robots capable of navigating cluttered and confined spaces autonomously.

Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments. [ABSTRACT FROM AUTHOR]

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons, or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use of biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.

Recent developments in neuroscience techniques such as brain imaging, functional brain imaging, and non-invasive brain stimulation, have made it possible to directly study able-bodied humans while engaged in various motor tasks. As a result of these technological advances, the last couple of decades have experienced a significant increase in new findings in the field of motor control and, more specifically, on the control of gait and posture. However, there are still a lot of difficulties and limitations in studying human neural activities during dynamic movements, like walking. In this short review, recent advances in knowledge on human gait and posture will be reviewed.

As our understanding of the underlying aetiology of hypertension is far from adequate, over 90% of patients with hypertension receive a diagnosis of essential hypertension. This non-specific diagnosis leads to suboptimal therapeutics and a major problem with non-compliance. Understanding the normal control of blood pressure (BP) is, hence, important for a better understanding of the disease.This review attempts to unravel the present understanding of BP control. The local mechanisms of BP control, the neural mechanisms, renal-endocrine mechanisms, and a variety of other hormones that have a bearing in normal BP control are discussed and the possible role in the pathophysiology is alluded to.

INTRODUCTION: Noninvasive positive pressure has been used to treat several diseases. However, the physiological response of the cardiac autonomic system during bilevel positive airway pressure (Bilevel) remains unclear. OBJECTIVE: The aim of this study was to evaluate the heart rate variability (HRV) during Bilevel in young healthy subjects. METHODS: Twenty men underwent 10-minute R-R interval recordings during sham ventilation (SV), Bilevel of 8-15 cmH2O and Bilevel of 13-20 cmH2O. The HRV was analyzed by means of the parallel R-R interval (mean R-Ri), the standard deviation of all R-Ri (SDNN), the root mean square of the squares of the differences between successive R-Ri (rMSSD), the number of successive R-Ri pairs that differ by more than 50 milliseconds (NN50), the percentage of successive R-Ri that differ by more than 50 milliseconds (pNN50), the low frequency (LF), the high frequency (HF) and SD1 and SD2. Additionally, physiological variables, including blood pressure, breathing frequency and end tidal CO2, were collected. Repeated-measures ANOVA and Pearson correlation were used to assess the differences between the three studied conditions and the relationships between the delta of Bilevel at 13-20 cmH2O and sham ventilation of the HRV indexes and the physiological variables, respectively. RESULTS: The R-Ri mean, rMSSD, NN50, pNN50 and SD1 were reduced during Bilevel of 13-20 cmH2O as compared to SV. An R-Ri mean reduction was also observed in Bilevel of 13-20 cmH2O compared to 8-15 cmH2O. Both the R-Ri mean and HF were reduced during Bilevel of 8-15 cmH2O as compared to SV, while the LF increased during application of Bilevel of 8-15 cmH2O as compared to SV. The delta (between Bilevel at 13-20 cmH2O and sham ventilation) of ETCO2 correlated positively with LF, HF, the LF/HF ratio, SDNN, rMSSD and SD1. Acute application of Bilevel was able to alter the cardiac autonomic nervous system, resulting in a reduction in parasympathetic activity and an increase in sympathetic activity and higher level of positive pressure can cause a greater influence on the cardiovascular and respiratory system.

Previous studies have demonstrated the high reproducibility of heart rate variability (HRV) measures in adults while little information exists concerning HRV reproducibility in children. Subsequently, the aim of the current study was to examine the moderate-term reproducibility of heart rate and frequency domain measures of HRV during rest and light to moderate exercise in children. Ten healthy children (6 males, 4 females) aged between 7 and 12 years of age volunteered for this study with HRV recordings obtained during supine rest and three treadmill walking exercise work rates (≤60% maximum heart rate), initially and then 8 weeks later. Differences (P < 0.05) between variables were examined using paired t-tests or Wilcoxon signed rank tests while reliability and reproducibility were examined by intraclass correlation coefficients (ICC), coefficients of variation (CV), and mean bias ratio and ratio limits of agreement (LOA). Heart rate and all measures of HRV at rest and exercise were unchanged after 8 weeks. Significant ICC were documented primarily during rest (0.72-0.85) while weaker relationships (-0.02-0.87) were evident during exercise. A large range of CV was identified during rest (6-33%) and exercise (3-128%) while the ratio LOA were variable and substantial (1.04-2.73). Despite similar HRV over an 8-week period, variable ICC and sizable CV and ratio LOA indicate moderate to poor reproducibility of HRV in children, particularly during light to moderate exercise. Studies examining HRV in children should include age- or maturation stage-matched control participants to address the age-related change in HRV and inadequate HRV reliability.

Muscular hydrostats are organs composed entirely of packed arrays of incompressible muscles and lacking any skeletal support. Found in both vertebrates and invertebrates, they are of great interest for comparative biomechanics from engineering and evolutionary perspectives. The arms of cephalopods (e.g. octopus and squid) are particularly interesting muscular hydrostats because of their flexibility and ability to generate complex behaviors exploiting elaborate nervous systems. Several lines of evidence from octopus studies point to the use of both brain and arm-embedded motor control strategies that have evolved to simplify the complexities associated with the control of flexible and hyper-redundant limbs and bodies. Here, we review earlier and more recent experimental studies on octopus arm biomechanics and neural motor control. We review several dynamic models used to predict the kinematic characteristics of several basic motion primitives, noting the shortcomings of the current models in accounting for behavioral observations. We also discuss the significance of impedance (stiffness and viscosity) in controlling the octopus's motor behavior. These factors are considered in light of several new models of muscle biomechanics that could be used in future research to gain a better understanding of motor control in the octopus. There is also a need for updated models that encompass stiffness and viscosity for designing and controlling soft robotic arms. The field of soft robotics has boomed over the past 15 years and would benefit significantly from further progress in biomechanical and motor control studies on octopus and other muscular hydrostats., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2023. Published by The Company of Biologists Ltd.)

Introduction: Animals such as cattle can achieve versatile and elegant behaviors through automatic sensorimotor coordination. Their self-organized movements convey an impression of adaptability, robustness, and motor memory. However, the adaptive mechanisms underlying such natural abilities of these animals have not been completely realized in artificial legged systems., Methods: Hence, we propose adaptive neural control that can mimic these abilities through adaptive physical and neural communications. The control algorithm consists of distributed local central pattern generator (CPG)-based neural circuits for generating basic leg movements, an adaptive sensory feedback mechanism for generating self-organized phase relationships among the local CPG circuits, and an adaptive neural coupling mechanism for transferring and storing the formed phase relationships (a gait pattern) into the neural structure. The adaptive neural control was evaluated in experiments using a quadruped robot., Results: The adaptive neural control enabled the robot to 1) rapidly and automatically form its gait (i.e., self-organized locomotion) within a few seconds, 2) memorize the gait for later recovery, and 3) robustly walk, even when a sensory feedback malfunction occurs. It also enabled maneuverability, with the robot being able to change its walking speed and direction. Moreover, implementing adaptive physical and neural communications provided an opportunity for understanding the mechanism of motor memory formation., Discussion: Overall, this study demonstrates that the integration of the two forms of communications through adaptive neural control is a powerful way to achieve robust and reusable self-organized locomotion in legged robots., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sun, Dai and Manoonpong.)

Humans make a number of choices when they walk, such as how fast and for how long. The preferred steady walking speed seems chosen to minimize energy expenditure per distance traveled. But the speed of actual walking bouts is not only steady, but rather a time-varying trajectory, which can also be modulated by task urgency or an individual's movement vigor. Here we show that speed trajectories and durations of human walking bouts are explained better by an objective to minimize Energy and Time, meaning the total work or energy to reach destination, plus a cost proportional to bout duration. Applied to a computational model of walking dynamics, this objective predicts dynamic speed vs. time trajectories with inverted U shapes. Model and human experiment (N=10) show that shorter bouts are unsteady and dominated by the time and effort of accelerating, and longer ones are steadier and faster and dominated by steady-state time and effort. Individual-dependent vigor may be characterized by the energy one is willing to spend to save a unit of time, which explains why some may walk faster than others, but everyone may have similar-shaped trajectories due to similar walking dynamics. Tradeoffs between energy and time costs can predict transient, steady, and vigor-related aspects of walking., Competing Interests: RC, AK No competing interests declared, (© 2023, Carlisle and Kuo.)

Octopuses are remarkable in their ability to use many arms together during behavior (e.g., see Levy et al., 1 Mather, 2 Byrne et al., 3 and Hanlon et al. 4). Arm responses and multi-arm coordination can occur without engagement of major brain regions, 5 which indicates the importance of local proprioceptive responses and peripheral connections. Here, we examine the intramuscular nerve cords (INCs), 6,7,8,9 the key proprioceptive anatomy in the arms. INCs are understood to include proprioceptive neurons, multipolar neurons, and motoneurons (reviewed by Graziadei 10) and are thought to contribute to structuring whole-arm movement. 11 There are four INCs running the full length of each arm (e.g., see Guérin-Ganivet, 6 Martoja and May, 8 and Graziadei 9); we focused on the pair closest to the suckers, called the oral INCs. In tracking the oral INCs, we found that they extend proximally and continue beyond the arm, through the arm's base. Each oral INC bypasses two adjacent arms and is continuous with the nearer oral INC of the third arm over. As a result, an arm connects through oral INC pathways to arms that are two arms away to the right and left of it. In addition to connecting distant arms, nerve fibers project from the central region of the INCs, suggesting function in local tissues. The other two INCs, paired aboral INCs, also extend proximally beyond the arm's base with trajectories suggestive of the oral INC pattern. These data identify previously unknown regions of the INCs that link distant arms, creating anatomical connections. They suggest potential INC proprioceptive function in extra-arm tissues and contribute to an understanding of embodied organization for octopus behavioral control. 12,13,14,15 • Octopus bimaculoides ' intramuscular nerve cords extend proximally from the arm • Oral intramuscular nerve cords are anatomically continuous between distant arms • A nerve cord bypasses two arms connecting to the third arm over • The same anatomical connection pattern occurs for all eight arms Kuuspalu et al. describe direct neural connections between distant arms in octopuses. Each of the paired oral intramuscular nerve cords in an arm is continuous with a cord that is three arms over. This pattern occurs for all arms and demonstrates potential paths for inter-arm communication outside of central integrating structures like the brain. [ABSTRACT FROM AUTHOR]

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of 1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, 2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and 3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps as well as climbing over high obstacles. Furthermore we demonstrate that the newly developed recurrent network based approach to sensorimotor prediction outperforms the previous state of the art adaptive neuron forward models.

Birdsong is a learned motor behavior controlled by an interconnected structure of neural nuclei. This pathway is bilaterally organized, with anatomically indistinguishable structures in each brain hemisphere. In this work, we present a computational model whose variables are the average activities of different neural nuclei of the song system of oscine birds. Two of the variables are linked to the air sac pressure and the tension of the labia during canary song production. We show that these time dependent gestures are capable of driving a model of the vocal organ to synthesize realistic canary like songs. [ABSTRACT FROM AUTHOR]

Urinary incontinence is a condition with multifaceted etiology and a significant health issue especially for aging men and women. The purpose of this report is to review the anatomy of the lower urinary tract and pelvic floor, along with the current understanding of the neural control pathways, which act reciprocally at all levels of the nervous system to control the pelvic floor and urinary bladder to maintain urinary continence. Understanding all of these elements and how they affect pelvic floor function, urethral function, or urinary bladder function will assist rehabilitation professionals in addressing this potentially devastating health problem. [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]

The neural mechanisms that take place during learning and adaptation can be directly probed with brain-machine interfaces (BMIs). We developed a BMI controlled paradigm that enabled us to enforce learning by introducing perturbations which changed the relationship between neural activity and the BMI's output. We introduced a uniform perturbation to the system, through a visuomotor rotation (VMR), and a non-uniform perturbation, through a decorrelation task. The controller in the VMR was essentially unchanged, but produced an output rotated at 30° from the neurally specified output. The controller in the decorrelation trials decoupled the activity of neurons that were highly correlated in the BMI task by selectively forcing the preferred directions of these cell pairs to be orthogonal. We report that movement errors were larger in the decorrelation task, and subjects needed more trials to restore performance back to baseline. During learning, we measured decreasing trends in preferred direction changes and cross-correlation coefficients regardless of task type. Conversely, final adaptations in neural tunings were dependent on the type controller used (VMR or decorrelation). These results hint to the similar process the neural population might engage while adapting to new tasks, and how, through a global process, the neural system can arrive to individual solutions. [ABSTRACT FROM AUTHOR]