Data from: Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics

Despite decades of research, we lack a mechanistic framework capable of
predicting how movement-related signals are transformed into the diversity
of muscle spindle afferent firing patterns observed experimentally,
particularly in naturalistic behaviors. Here, a biophysical model
demonstrates that well-known firing characteristics of mammalian muscle
spindle Ia afferents – including movement history dependence, and
nonlinear scaling with muscle stretch velocity – emerge from first
principles of muscle contractile mechanics. Further, mechanical
interactions of the muscle spindle with muscle-tendon dynamics reveal how
motor commands to the muscle (alpha drive) versus muscle spindle (gamma
drive) can cause highly variable and complex activity during active muscle
contraction and muscle stretch that defy simple
explanation. Depending on the neuromechanical conditions, the
muscle spindle model output appears to “encode” aspects of muscle force,
yank, length, stiffness, velocity, and/or acceleration, providing
an extendable, multiscale, biophysical framework for understanding and
predicting proprioceptive sensory signals in health and disease.

尽管历经数十年研究，学界仍缺乏一套能够预测运动相关信号如何转化为实验中观测到的多样肌梭（muscle spindle）传入放电模式的机械学框架，尤其是在自然行为场景下。本研究中，一套生物物理模型证实，哺乳动物肌梭Ia类传入纤维（Ia afferents）的经典放电特征——包括运动历史依赖性以及随肌肉牵拉速度的非线性缩放特性——均可从肌肉收缩力学的第一性原理中推导得出。进一步而言，肌梭与肌-腱动力学之间的机械相互作用揭示了：针对肌肉的α驱动（alpha drive）与针对肌梭的γ驱动（gamma drive）运动指令，如何在主动肌肉收缩与肌肉牵拉过程中引发高度可变且复杂的活动，而此类活动难以通过简单逻辑进行解释。根据不同的神经力学条件，该肌梭模型的输出似乎可对肌肉力、力变化率、肌肉长度、刚度、速度及/或加速度等参数进行"编码"，从而为理解和预测健康与疾病状态下的本体感觉信号（proprioceptive sensory signals）提供一套可扩展、多尺度的生物物理框架。