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ABSTRACT Motor skill expertise can facilitate more automatic movement, engaging less cortical activity while producing appropriate motor output. Accordingly, cortical-evoked N1 responses to balance perturbation, assessed using electroencephalography (EEG), are smaller in young and older adults with better balance. These responses may thus reflect individual balance challenge versus functional, or objective, task difficulty. However, the effect of balance expertise on cortical responses to balance perturbation has not been studied. We hypothesized that balance ability gained though long-term training facilitates more automatic balance control. Using professional modern dancers as balance experts, we compared cortical-evoked responses and biomechanics of the balance-correcting response between modern dancers and nondancers. We predicted that modern dancers would have smaller cortical-evoked responses and better balance recovery at equivalent levels of balance challenge. Support-surface perturbations were normalized to individual challenge levels by delivering perturbations scaled to 60% and 140% of each individual’s step threshold. In contrast to our prediction, dancers exhibited larger N1 responses compared to nondancers while demonstrating similar biomechanical responses. Our results suggest dancers have greater cortical sensitivity to balance perturbations than nondancers. Further, dancer N1 responses modulated across perturbation magnitudes according to differences in objective task difficulty. In contrast, nondancer N1 responses modulated as a function of individual challenge level. Our findings suggest dance training increases sensitivity of the initial, cortical N1 response to balance perturbation, supporting postural alignment to an objective reference. The N1 response may reflect differences in balance-error processing that are altered with specific long-term training and may have implications for rehabilitation. NEW & NOTEWORTHYModern dancers show larger cortical responses to balance perturbations than nondancers, suggesting a greater sensitivity to perturbations. These results contrast with evidence of larger cortical-evoked responses in young adults with poorer balance, consistent with the cortical N1 response being a balance error assessment signal. Whereas nondancers scaled cortical responses by individual differences in N1 amplitude, dancers’ cortical responses were scaled to objective differences in perturbation magnitude, suggesting increased postural awareness due to training. 

Abstract Exoskeletons assist and augment human movement, but their effects on proprioceptive feedback remain poorly understood. We examined how parallel exoskeleton stiffness influences primary muscle spindle firing. In an anesthetized rat preparation, controlled stretches of the medial gastrocnemius were applied with springs (0–0.5 N/mm) attached in parallel to the muscle-tendon unit (MTU) to simulate passive exoskeleton assistance. Fascicle length was measured with sonomicrometry, force and MTU length with a servo motor, and spindle instantaneous firing rate (IFR) with dorsal root recordings. Increasing exoskeleton stiffness decreased biological muscle force (3.1 ± 0.6 N to 1.6 ± 0.6 N, p < 0.001) and stiffness (4.4 ± 1.5 N/mm to 2.3 ± 1.3 N/mm, p < 0.01), while fascicle length increased (7.9 ± 1.3 mm to 8.3 ± 1.5 mm, p < 0.005). Despite these altered mechanics, spindle firing did not significantly change, and showed weak correlations with muscle length, velocity, force, and yank (R²≤ 0.14). These results indicate that exoskeleton stiffness modifies fascicle dynamics without altering spindle firing. Previously proposed models of primary afferent firing did not sufficiently explain these results. This is the first in situ investigation of exoskeleton effects on primary afferent feedback during active contractions. 

ABSTRACT Hill-type muscle models are widely used, even though they do not accurately represent the relationship between activation and force in dynamic contractions. We explored the use of neural networks as an alternative approach to capture features of dynamic muscle function, without a priori assumptions about force–length–velocity relationships. We trained neural networks using an existing dataset of two guinea fowl muscles to estimate muscle force from activation, fascicle length and velocity. Training data were recorded using sonomicrometry, electromyography and a tendon buckle. First, we compared the neural networks with Hill-type muscle models, using the same data for network training and model optimization. Second, we trained neural networks on larger datasets, in a more realistic machine learning scenario. We found that neural networks generally yielded higher coefficients of determination and lower errors than Hill-type muscle models. Neural networks performed better when estimating forces on the muscle used for training, but on another bird, than on a different muscle of the same bird, likely due to inaccuracies in activation and force scaling. We extracted force–length and force–velocity relationships from the trained neural networks and found that both effects were underestimated and the relationships were not well replicated outside the training data distribution. We discuss suggested experimental designs and the challenge of collecting suitable training data. Given a suitable training dataset, neural networks could provide a useful alternative to Hill-type muscle models, particularly for modeling muscle dynamics in faster movements; however, scaling of the training data should be comparable between muscles and animals. 

ABSTRACT Behavioral variation within a population can be influenced by physical factors such as size, sex, and body condition. This variation may contribute to intraspecific niche breadth by enabling individuals to exploit different niches. To examine how anatomy shapes behavior, we conducted open field tests on desert kangaroo rats (Dipodomys deserti, n=16) and compared their activity to sex, morphology, and body condition. We constructed an arena within the species' natural habitat to simulate ecologically relevant conditions and recorded behavior over 15 min. We quantified speed and distance traveled, used principal component analysis to explore behavioral patterns, and used linear models to test for associations between behavior, locomotor traits, and anatomical variables. We found that individuals with lower body condition scores spent more time exploring, males were more exploratory than females, and individuals with longer feet – a proxy for skeletal size – traveled further. However, behavior and locomotor performance were not significantly correlated. Lastly, individuals moved faster and farther on full moon nights compared to new moon nights, indicating that moonlight influences movement strategy – potentially reflecting trade-offs between foraging and predation risk. These findings highlight species-specific drivers of behavioral variation and underscore the importance of understanding behavioral variability of desert mammals. 

Abstract Musculoskeletal simulations can offer valuable insight into how the properties of our musculoskeletal system influence the biomechanics of our daily movements. One such property is muscle’s history-dependent initial resistance to stretch, also known as short-range stiffness, which is key to stabilizing movements in response to external perturbations. Short-range stiffness is poorly captured by existing musculoskeletal simulations since they employ phenomenological Hill-type muscle models that lack the mechanisms underlying short-range stiffness. While it has been previously shown that biophysical cross-bridge models can reproduce muscle short-range-stiffness, it is unclear which specific biophysical properties are necessary to capture history-dependent muscle force responses in behaviorally relevant conditions. Here, we tested the ability of various biophysical cross-bridge models to reproduce empirical short-range stiffness and its history-dependent changes across a broad range of behaviorally relevant length changes and activation levels, using an existing dataset on permeabilized rat soleus muscle fibers (N = 11). We found that a biophysical cross-bridge model with cooperative myofilament activation reproduced the effects of muscle activation (R²= 0.86), stretch amplitude (R²= 0.71) and isometric recovery time (R²= 0.79) on history-dependent changes in short-range stiffness after shortening. Similar results were obtained when the cross-bridge distribution of the biophysical model was approximated by a Gaussian (R²= 0.73 - 0.88), but at a 20 times lower computational cost. These effects could not be reproduced by either a biophysical cross-bridge model without cooperative myofilament activation or a Hill-type model (R²< 0.5). The reduced computational demand of the Gaussian-approximated models facilitates implementing biophysical cross-bridge models with cooperative myofilament activation in musculoskeletal simulations to improve the prediction of short-range stiffness during movements. 

ABSTRACT Terrestrial animal gaits often use spring-like mechanics to enhance movement economy through elastic energy cycling. Hopping is a relatively simple, constrained task, yet retains essential features of bouncing gaits, requiring cyclic regulation of limb stiffness and generation of high muscle forces to support body weight and enable elastic energy cycling. We investigated how humans adjust hopping frequency and leg stiffness before, during and after experiencing added load. Eighteen participants hopped bipedally for 90 s per trial, with hop frequency and height unconstrained, while kinematic, ground reaction force and ankle muscle electromyographic (EMG) data were collected. We analysed mechanics across four conditions: initial body weight (BWi), two added mass trials (+10% and +20% BW) and final body weight (BWf). With added mass, participants increased leg stiffness and maintained a consistent hopping frequency (∼2.15 Hz); yet, when returning to BWf, the elevated leg stiffness was maintained and hopping frequency increased (to ∼2.36 Hz) and reduced centre of mass (CoM) work per hop. BWf adaptations were driven by greater ankle stiffness, leading to less ankle work. Adaptation rates were consistent across trials, with steady-state mechanics reached in ∼30–40 s. Muscle coactivation decreased following BWi. Triceps surae mean EMG was unchanged with added mass and reduced in BWf. Similar patterns of adaptation were observed in bouncing without an aerial phase. Substantial inter-individual variability was observed in preferred hopping mechanics and adaptation strategy. Together, added mass and increased task familiarity led participants to recalibrate their hopping strategy. Based on literature evidence, the adaptations may align with reduced metabolic cost. 

Abstract Proprioceptive sensory feedback is crucial for the control of movement. In many ways, sensorimotor control loops in the neuromuscular system act as state feedback controllers. These controllers combine input commands and sensory feedback regarding the mechanical state of the muscle, joint or limb to modulate the mechanical output of the muscles. To understand how these control circuits function, it is necessary to understand fully the mechanical state variables that are signalled by proprioceptive sensory (propriosensory) afferents. Using new computational approaches, we demonstrate how combinations of group Ia and II muscle spindle afferent feedback can allow for tuned responses to force and the rate of force (or length and velocity) and how combinations of muscle spindle and Golgi tendon organ feedback can parse external and internal (self‐generated) force. These models suggest that muscle spindle feedback might be used to monitor and control muscle forces in addition to length and velocity and, when combined with tendon organ feedback, can distinguish self‐generated from externally imposed forces. Given that these models combine feedback from different sensory afferent types, they emphasize the utility of analysing muscle propriosensors as an integrated population, rather than independently, to gain a better understanding of propriosensory–motor control. Furthermore, these models propose a framework that links neural connectivity in the spinal cord with neuromechanical control. Although considerable work has been done on propriosensory–motor pathways in the CNS, our aim is to build upon this work by emphasizing the mechanical context. 

Abstract Powering small-scale flapping flight is challenging, yet insects sustain exceptionally fast wingbeats with ease. Since insects act as tiny biomechanical resonators, tuning their wingbeat frequency to the resonant frequency of their springy thorax and wings could make them more efficient fliers. But operating at resonance poses control problems and potentially constrains wingbeat frequencies within and across species. Resonance may be particularly limiting for the many orders of insects that power flight with specialized muscles that activate in response to mechanical stretch. Here, we test whether insects operate at their resonant frequency. First, we extensively characterize bumblebees and find that they surprisingly flap well above their resonant frequency via interactions between stretch-activation and mechanical resonance. Modeling and robophysical experiments then show that resonance is actually a lower bound for rapid wingbeats in most insects because muscles only pull, not push. Supra-resonance emerges as a general principle of high-frequency flight across five orders of insects from moths to flies. 

Legged animals still outperform many terrestrial robots due to the complex interplay of various component subsystems. Centralization is a potential integrated design axis to help improve the performance of legged robots in variable terrain environments. Centralization arises from the coupling of multiple limbs and joints through mechanics or feedback control. Strong couplings contribute to a whole-body coordinated response (centralized) and weak couplings result in localized responses (decentralized). Rarely are both mechanical and neural couplings considered together in designing centralization. In this study, we use an empirical information theory-based approach to evaluate the emergent centralization of a hexapod robot. We independently vary the mechanical and neural coupling through adjustable joint stiffness and variable coupling of leg controllers, respectively. We found an increase in centralization as neural coupling increased. Changes in mechanical coupling did not significantly affect centralization during walking, but did change the total information processing of the neuromechanical control architecture. Information-based centralization increased with robotic performance in terms of cost of transport and speed, implying that this may be a useful metric in robotic design. 

Everyday locomotion is a complex sensorimotor process that can unfold over multiple timescales, from long-term path planning to rapid, reactive adjustments. However, we lack an understanding of how factors such as environmental demands, or the available sensory information simultaneously influence these control timescales. To address this, we present a unified data-driven framework to quantify the control timescales by identifying how early we can predict future actions from past inputs. We apply this framework across tasks including walking and running, environmental contexts including treadmill, overground, and varied terrains, and sensory input modalities including gaze fixations and body states. We find that deep neural network architectures that effectively handle long-range dependencies, specifically Gated Recurrent Units and Transformers, outperform other architectures and widely used linear models when predicting future actions. Our framework reveals the factors that influence locomotor foot placement control timescales. Across environmental contexts, we discover that humans rely more on fast timescale control in more complex terrain. Across input modalities, we find a hierarchy of control timescales where gaze predicts foot placement before full-body states, which predict before center-of-mass states. Our model also identifies mid-swing as a critical phase when the swing foot's state predicts its future placement, with this timescale adapting across environments. Overall, this work offers data-driven insights into locomotor control in everyday settings, offering models that can be integrated with rehabilitation technologies and movement simulations to improve their applicability in everyday settings.