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Structure of the Golgi tendon organ (GTO). GTO receptor is located in series between the tendon and muscle fibers that insert into it. It is composed of 2 types of collagen: innervated collagen that occupies the capsule lumen and surrounds many afferent endings; and bypassing collagen that occupies the marginal areas of the GTO and lacks contact with GTO afferent endings. A single GTO afferent axon enters the GTO capsule about halfway between the GTO's proximal (muscle) and distal (tendon) end and travels into the middle of the capsule lumen, at which point it splits into 2 myelinated branches. One afferent branch courses toward the GTO's proximal end, the other toward the distal end. Two main branches repeatedly divide further into smaller branches until giving rise to unmyelinated collateral branches that are intertwined among the strands of innervated collagen. 

Structure of the Golgi tendon organ (GTO). GTO receptor is located in series between the tendon and muscle fibers that insert into it. It is composed of 2 types of collagen: innervated collagen that occupies the capsule lumen and surrounds many afferent endings; and bypassing collagen that occupies the marginal areas of the GTO and lacks contact with GTO afferent endings. A single GTO afferent axon enters the GTO capsule about halfway between the GTO's proximal (muscle) and distal (tendon) end and travels into the middle of the capsule lumen, at which point it splits into 2 myelinated branches. One afferent branch courses toward the GTO's proximal end, the other toward the distal end. Two main branches repeatedly divide further into smaller branches until giving rise to unmyelinated collateral branches that are intertwined among the strands of innervated collagen. 

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Article
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We developed a physiologically realistic mathematical model of the Golgi tendon organ (GTO) whose elements correspond to anatomical features of the biological receptor. The mechanical interactions of these elements enable it to capture all salient aspects of GTO afferent behavior reported in the literature. The model accurately describes the GTO's...

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... One study detailed a model of the GTO that captures important anatomical and functional features (574). The model includes innervated and noninnervated collagen fibers and a sensory region modeled as viscoelastic material with specific stress-strain characteristics. ...
Article
When animals walk overground, mechanical stimuli activate various receptors located in muscles, joints, and skin. Afferents from these mechanoreceptors project to neuronal networks controlling locomotion in the spinal cord and brain. The dynamic interactions between the control systems at different levels of the neuraxis ensure that locomotion adjusts to its environment and meets task demands. In this article, we describe and discuss the essential contribution of somatosen-sory feedback to locomotion. We start with a discussion of how biomechanical properties of the body affect somatosensory feedback. We follow with the different types of mechanoreceptors and somatosensory afferents and their activity during locomotion. We then describe central projections to locomotor networks and the modulation of somatosensory feedback during locomotion and its mechanisms. We then discuss experimental approaches and animal models used to investigate the control of locomotion by somatosensory feedback before providing an overview of the different functional roles of somatosensory feedback for locomotion. Lastly, we briefly describe the role of somatosensory feedback in the recovery of locomotion after neurological injury. We highlight the fact that somatosensory feedback is an essential component of a highly integrated system for locomotor control.
... One study detailed a model of the GTO that captures important anatomical and functional features (574). The model includes innervated and noninnervated collagen fibers and a sensory region modeled as viscoelastic material with specific stress-strain characteristics. ...
Chapter
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When animals walk overground, mechanical stimuli activate various receptors located in muscles, joints, and skin. Afferents from these mechanoreceptors project to neuronal networks controlling locomotion in the spinal cord and brain. The dynamic interactions between the control systems at different levels of the neuraxis ensure that locomotion adjusts to its environment and meets task demands. In this article, we describe and discuss the essential contribution of somatosensory feedback to locomotion. We start with a discussion of how biomechanical properties of the body affect somatosensory feedback. We follow with the different types of mechanoreceptors and somatosensory afferents and their activity during locomotion. We then describe central projections to locomotor networks and the modulation of somatosensory feedback during locomotion and its mechanisms. We then discuss experimental approaches and animal models used to investigate the control of locomotion by somatosensory feedback before providing an overview of the different functional roles of somatosensory feedback for locomotion. Lastly, we briefly describe the role of somatosensory feedback in the recovery of locomotion after neurological injury. We highlight the fact that somatosensory feedback is an essential component of a highly integrated system for locomotor control. © 2021 American Physiological Society. Compr Physiol 11:1-71, 2021.
... Such a biological strategy has been proposed (Scott and Loeb, 1994). The ∼ 100 Golgi tendon organs distributed throughout the myotendinous junction of the typical muscle appear to be well-suited to generating an ensemble signal that accurately, albeit nonlinearly, reflects total tendon tension (Mileusnic and Loeb, 2006). The requisite integration of signals from tension-sensing Golgi tendon organs and lengthsensing muscle spindles has been identified in the spinocerebellar tracts Poppele, 1997, 2001). ...
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Estimates of limb posture are critical for controlling robotic systems. This is generally accomplished with angle sensors at individual joints that simplify control but can complicate mechanical design and robustness. Limb posture should be derivable from each joint's actuator shaft angle but this is problematic for compliant tendon-driven systems where (i) motors are not placed at the joints and (ii) nonlinear tendon stiffness decouples the relationship between motor and joint angles. Here we propose a novel machine learning algorithm to accurately estimate joint posture during dynamic tasks by limited training of an artificial neural network (ANN) receiving motor angles and tendon tensions, analogous to biological muscle and tendon mechanoreceptors. Simulating an inverted pendulum—antagonistically-driven by motors and nonlinearly-elastic tendons—we compare how accurately ANNs estimate joint angles when trained with different sets of non-collocated sensory information generated via random motor-babbling. Cross-validating with new movements, we find that ANNs trained with motor angles and tendon tension data predict joint angles more accurately than ANNs trained without tendon tension. Furthermore, these results are robust to changes in network/mechanical hyper-parameters. We conclude that regardless of the tendon properties, actuator behavior, or movement demands, tendon tension information invariably improves joint angle estimates from non-collocated sensory signals.
... The type Ib muscle fiber, also known as the Golgi tendon organ, is responsible for reacting to muscle tension changes. 19,24 With greater momentum transfer, the Golgi tendon contracts the muscles faster, creating a more efficient transfer of energy. 33 Therefore, it seems reasonable to presume that shorter times within the pitching phases correlated with higher ball speed, generated as an outcome of the stronger combined segmental forces activating the Golgi tendon and subsequent muscular contraction. ...
Article
Background Understanding the relationship between the temporal phases of the baseball pitch and subsequent joint loading may improve our understanding of optimal pitching mechanics and contribute to injury prevention in baseball pitchers. Purpose To investigate the temporal phases of the pitching motion and their associations with ball velocity and throwing arm kinetics in high school (HS) and professional (PRO) baseball pitchers. Study Design Descriptive laboratory study. Methods PRO (n = 317) and HS (n = 54) baseball pitchers were evaluated throwing 8 to 12 fastball pitches using 3-dimensional motion capture (480 Hz). Four distinct phases of the pitching motion were evaluated based on timing of angular velocities: (1) Foot-Pelvis, (2) Pelvis-Torso, (3) Torso-Elbow, and (4) Elbow-Ball. Peak elbow varus torque, shoulder internal rotation torque, and shoulder distraction force were also calculated and compared between playing levels using 2-sample t tests. Linear mixed-effect models with compound symmetry covariance structures were used to correlate pitch velocity and throwing arm kinetics with the distinct temporal phases of the pitching motion. Results PRO pitchers had greater weight and height, and faster ball velocities than HS pitchers ( P < .001). There was no difference in total pitch time between groups ( P = .670). PRO pitchers spent less time in the Foot-Pelvis ( P = .010) and more time in the Pelvis-Torso ( P < .001) phase comparatively. Shorter time spent in the earlier phases of the pitching motion was significantly associated with greater ball velocity for both PRO and HS pitchers (Foot-Pelvis: B = −6.4 and B = −11.06, respectively; Pelvis-Torso: B = −6.4 and B = −11.4, respectively), while also associated with increased shoulder proximal force (Pelvis-Torso: B = −76.4 and B = −77.5, respectively). Decreased time in the Elbow-Ball phase correlated with greater shoulder proximal force for both cohorts (B = −1150 and B = −645, respectively) with no significant correlation found for ball velocity. Conclusion Significant differences in temporal phases exist between PRO and HS pitchers. For all pitchers, increased time spent in the final phase of the pitching motion has the potential to decrease shoulder distraction force with no significant loss in ball velocity. Clinical Relevance Identifying risk factors for increased shoulder and elbow kinetics, acting as a surrogate for loading at the respective joints, has potential implications in injury prevention.
... Muscle spindles located within the belly of muscles which stretches with muscle movement, detect changes in the length, and carries information to the central nervous system (Mileusnic & Loeb, 2006). The Golgi tendon lies between muscle tendons and muscle fibers. ...
Thesis
Material perception is a crucial part of our everyday life. This is more evident, when deciding where to put our step forward while walking under the rain or when applying enough force to a soap to grasp it without it slipping through our fingers. Over the past decade, material perception attracted increasingly more attention. Yet many questions remain unanswered. Material perception is a complex problem with numerous entry points. For instance, in haptic research, softness is generally equated to the compliance of the objects. However, a recent study has shown that this is not the only case. Perceived material dimensions underlying softness (Dovencioglu et al., 2021) include granularity, viscosity, surface softness, and deformability of the materials. Moreover, people adapt their hand movements according to the material. Another open question would be in addition to extrinsic material properties whether our purpose (i.e., information to be gained) affects hand movements when haptically perceiving different softness dimensions. To this extend, in Study 1 we investigated whether the task and the explored material modulate the exploratory movements. Firstly, our findings replicated the previously reported multiple perceptual dimensions of softness (Dovencioglu et al., 2021). More importantly, our results extend the literature by showing that people adapt their movements based on the material, task, and the interaction between the two. Another entry point to the material perception would be to ask how different modalities provide information on the same material. In daily life, we usually see what we touch and touch what we see. Whether vision provide similar information to haptic about the various aspects of softness is an intriguing question. For instance, in order to judge the softness of a rabbit’s fur, we can inspect its softness by looking at a picture or by touching the rabbit’s fur. Another source of information could be, watching someone else petting the rabbit. In contrast to the merely looking at the rabbit’s fur, watching someone else’s action not only provides the visual feature of the fur but also reveals how the material reacts to the hand movements. It is elusive whether these three examples would yield similar interpretations of softness as a multidimensional construct or not. In Study 2, we investigated to what extent the perceived softness dimensions are similar in vision compared to haptics. Our results showed high overall consistency across haptics, static visual information (i.e., images), and dynamic visual information (i.e., hand movement of other exploring materials). These similarities were the strongest between availability of haptic and dynamic visual information. In our daily experience, we do not only touch objects with bare hands but also sometimes through intermediate surfaces (e.g., wearing gloves). Perceiving materials over another layer of material could reduce some of the haptic information such as thermal properties of the material in question. Despite our regular interaction with materials under restrained conditions (i.e., wearing gloves in winter), it is mostly unknown to what extend these restrictions affect our perception of different aspects of softness. Therefore, another entry point to understanding material perception would be to understand how material perception is affected by physical constraints. It is almost ironic that the augmented or mixed reality technologies for haptics generally construct the haptic experience through gloves or other restrictive proxies. Hence, understanding how physical constraints affect material perception is an important question concerning both theoretical and practical research. In Study 3, we seek to understand how haptic constraints affect perceived softness. Participants explored haptic stimuli under four conditions: bare hand, open-fingered glove, open-fingered glove with rigid sensors, and full glove. General results suggest that softness perception was overall highly similar across conditions. However, in a closer inspection, we found that glove condition differed from the others especially in terms of surface softness. So far, the discussed entry points to material perception scrutinized the material perception in sensory domains. However, as in other topics in perception, the material perception - in addition to the sensation - depends on the agent’s cognitive state such as their motivation, emotion, etc. In a similar vein, the previous studies have shown that sensory and affective properties are related. For instance, fine grained materials like sand feel pleasant while rough materials such as sandpaper feel unpleasant (Drewing et al., 2018). The origin of these relationships is another piece of the puzzle. To remedy this gap, in Study IV, we investigated whether the relationship between sensory materials properties (i.e., granular) and affective responses (i.e., feeling pleasant) can be modified by learning. We further investigated previously observed relationships: positive relationship between granular and pleasantness, negative relationship between roughness and valence. With a classical conditioning paradigm, instead of participants’ existing material-emotion associations the opposite affective relationship was reinforced. The results have shown a significantly decreased relationship between valence and granularity in the experimental group compared to the control group. However, valence and roughness relationships did not differ between the experimental and the control groups. The results suggest that not all affective associations of the perceived material dimensions could be modified. We explain these results with the difference in learned and hard-wired connections.
... The Golgi-tendon model react to "static and dynamic responses to activation of single motor units whose muscle fibers insert into the Golgi tendon organ, self and cross adaptation, non-linear summation when multiple motor units are active in the muscle, and the proportional relationship between the cross-adaptation and summation recorded for various pairs of motor units" [47,48]. ...
Chapter
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The proprioception is the sense of positioning and movement. It is mediate by proprioceptors, a small subset of mechanosensory neurons localized in the dorsal root ganglia that convey information about the stretch and tension of muscles, tendons, and joints. These neurons supply of afferent innervation to specialized sensory organs in muscles (muscle spindles) and tendons (Golgi tendon organs). Thereafter, the information originated in the proprioceptors travels throughout two main nerve pathways reaching the central nervous system at the level of the spinal cord and the cerebellum (unconscious) and the cerebral cortex (conscious) for processing. On the other hand, since the stimuli for proprioceptors are mechanical (stretch, tension) proprioception can be regarded as a modality of mechanosensitivity and the putative mechanotransducers proprioceptors begins to be known now. The mechanogated ion channels acid-sensing ion channel 2 (ASIC2), transient receptor potential vanilloid 4 (TRPV4) and PIEZO2 are among candidates. Impairment or poor proprioception is proper of aging and some neurological diseases. Future research should focus on treating these defects. This chapter intends provide a comprehensive update an overview of the anatomical, structural and molecular basis of proprioception as well as of the main causes of proprioception impairment, including aging, and possible treatments.
... Unlike abstract phenomenological models, structural models derive firing patterns of proprioceptors by approximating their anatomical structure [33][34][35][36][37][38][39]. For example, structural models of muscle spindles simulate intrafusal muscle fibers and their interaction with the extrafusal muscle and tendon. ...
... In addition, there remain many proprioceptors for which models do not currently exist. Historically, there has been a strong focus on modeling the activity of mammalian muscle spindles and Golgi tendon organs [18][19][20][21][33][34][35][36][37][38][39]. In contrast, computational models of invertebrate proprioceptors are only beginning to emerge (e.g. ...
Article
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Dexterous motor control requires feedback from proprioceptors, internal mechanosensory neurons that sense the body's position and movement. An outstanding question in neuroscience is how diverse proprioceptive feedback signals contribute to flexible motor control. Genetic tools now enable targeted recording and perturbation of proprioceptive neurons in behaving animals; however, these experiments can be challenging to interpret, due to the tight coupling of proprioception and motor control. Here, we argue that understanding the role of proprioceptive feedback in controlling behavior will be aided by the development of multiscale models of sensorimotor loops. We review current phenomenological and structural models for proprioceptor encoding and discuss how they may be integrated with existing models of posture, movement, and body state estimation.
... Modelling muscle proprioceptors: Numerical models of muscle spindles can be created and placed in parallel to muscle fibers (Section 3.1.1), receiving commands from gamma motor neurons [61]- [63]. ...
Article
Wearable technologies such as bionic limbs, robotic exoskeletons and neuromodulation devices have long been designed with the goal of enhancing human movement. However, current technologies have shown only modest results in healthy individuals and limited clinical impact. A central element hampering progress is that wearable technologies do not interact directly with tissues in the composite neuromuscular system. That is, current wearable systems do not take into account how biological targets (e.g. joints, tendons, muscles, nerves) react to mechanical or electrical stimuli, especially at extreme ends of the spatiotemporal scale (e.g. cell growth over months or years). Here, we outline a framework for ‘closing-the-loop’ between wearable technology and human biology. We envision a new class of wearable systems that will be classified as ‘steering devices’ rather than ‘assistive devices’ and outline the suggested research roadmap for the next 10–15 years. Wearable systems that steer, rather than assist, should be capable of delivering coordinated electro-mechanical stimuli to alter, in a controlled way, neuromuscular tissue form and function over time scales ranging from seconds (e.g. a movement cycle) to months (e.g. recovery stage following neuromuscular injuries) and beyond (e.g. across ageing stages). With an emphasis on spinal cord electrical stimulation and exosuits for the lower extremity, we explore developments in three key directions: (a) recording neuromuscular cellular activity from the intact moving human in vivo, (b) predicting tissue function and adaptation in response to electro-mechanical stimuli over time and (c) controlling tissue form and function with enough certainty to induce targeted, positive changes in the future. We discuss how this framework could restore, maintain or augment human movement and set the course for a new era in the development of bioprotective wearable devices. That is, devices designed to directly respond to biological cues to maintain integrity of underlying physiological systems over the lifespan.
... For each muscle model, the values of parameters describing the properties of individual fiber were taken from [18], whereas the fiber type and morphometric parameters were set as reported in Table 3. Furthermore, it is important to note that Virtual Muscle is equipped with realistic models of spindles [66], Golgi tendon organs [67] and metabolic energy consumption [108]. This last, in particular, was estimated according to the neural excitation and the muscle length and velocity. ...
Article
Full-text available
The tradeoff between speed and accuracy of human movements has been exploited from many different perspectives, such as experimental psychology, workspace design, human–machine interface. This tradeoff is formalized by Fitts’ law, which states a linear relationship between the duration and the difficulty of the movement. The bigger is the required accuracy in reaching a target or farther is the target, the slower has to be the movement. A variety of computational models of neuromusculoskeletal systems have been proposed to pinpoint the neurobiological mechanisms that are involved in human movement. We introduce a neurocomputational model of spinal cord to unveil how the tradeoff between speed and accuracy elicits from the interaction between neural and musculoskeletal systems. Model simulations showed that the speed–accuracy tradeoff is not an intrinsic property of the neuromuscular system, but it is a behavioral trait that emerges from the strategy adopted by the central nervous system for executing faster movements. In particular, results suggest that the velocity of a previous learned movement is regulated by the monosynaptic connection between cortical cells and alpha motoneurons.
... Morphologically realistic electronic models of motoneuron (Balbi et al., 2014(Balbi et al., , 2015 and Renshaw (Bui, 2003) Computational models of ion channels responsible for unique properties of neurons like PIC, plateau potential (Booth et al., 1997;Destexhe, 1997;Huss et al., 2008), Synapses (Destexhe et al., 1994(Destexhe et al., , 1998Best et al., 2010), Neuromuscular junction (Dionne and Leibowitz, 1982), Physiologically realistic motoneuron model (Fuglevand et al., 1993;McIntyre et al., 2002;Cisi and Kohn, 2008), -McCreight et al., 2016), Renshaw (Bui, 2003;Cisi and Kohn, 2008) Models of muscle behavior (Fuglevand et al., 1993;Brown et al., 1999), Mechanical models of Muscle Spindle (Mileusnic, 2006), and GTO model (Mileusnic and Loeb, 2006) Meso 3D localization of neurons (Gleeson et al., 2007) Computational models of CPG (Matsuoka, 1987;Prinz et al., 2003;Iwasaki and Zheng, 2006;Rybak et al., 2006;Rubin et al., 2009;Shevtsova and Rybak, 2016;Danner et al., 2017) Muscle Force-Velocity models (Cheng et al., 2000), and joint coordination (Morasso and Mussa Ivaldi, 1982;Flash and Hogan, 1985) Macro Models of spinal connectomes (Borisyuk et al., 2011) Electrical stimulation and neuromodulation of gait (Capogrosso et al., 2013;Courtine et al., 2016), Efficacy of stimulation in pain modulation (Arle et al., 2014a,b), LFP (Diwakar et al., 2011), Optimum electrode geometry for stimulation (Holsheimer and Wesselink, 1997) Models of human locomotion and control (Geyer and Herr, 2010;Schultz and Mombaur, 2010;Song and Geyer, 2015), Force estimation during movement (Seth and Pandy, 2007) These are categorized based on the scale (micro, meso, macro) and discipline (anatomy, physiology, biomechanics). ...
Article
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Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.