A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers

Chacon, Pablo F. S.; Hammer, Maria; Wochner, Isabell; Walter, Johannes R.; Schmitt, Syn

doi:10.1080/10255842.2023.2293652

Computer Methods in Biomechanics and Biomedical Engineering

2023

DOI: 10.1080/10255842.2023.2293652

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A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers

Pablo F. S. Chacon,

Maria Hammer,

Isabell Wochner

et al.

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2024

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Cited by 1 publication

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Modeling Sensorimotor Processing with Physics-Informed Neural Networks

Perez Rotondo,

Marin Vargas,

Dimitriou

et al. 2024

Preprint

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Proprioception is essential for planning and executing precise movements. Muscle spindles, the key mechanoreceptors for proprioception, are the principle sensory neurons enabling this process. Emerging evidence suggests spindles act as adaptable processors, modulated by gamma motor neurons to meet task demands. Yet, the specifics of this modulation remain unknown. Here, we present a novel, physics-informed neural network model that integrates biomechanics and neural dynamics to capture spindle function with high fidelity and efficiency, while maintaining computational tractability. Through validation across multiple experimental datasets and species, our model not only outperforms existing approaches but also reveals key drivers of variability in spindle responses, offering new insights into proprioceptive mechanisms.

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Modeling Sensorimotor Processing with Physics-Informed Neural Networks

Perez Rotondo,

Marin Vargas,

Dimitriou

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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A physiologically enhanced muscle spindle model: using a Hill-type model for extrafusal fibers as template for intrafusal fibers

Cited by 1 publication

References 75 publications

Modeling Sensorimotor Processing with Physics-Informed Neural Networks

Modeling Sensorimotor Processing with Physics-Informed Neural Networks

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