Grandjean Bernard, Maier Marc A
FR3636 CNRS, Université Paris Descartes, Sorbonne Paris Cité, F-75006, Paris, France.
Université Paris Diderot, Sorbonne Paris Cité, F-75013, Paris, France.
J Comput Neurosci. 2017 Feb;42(1):53-70. doi: 10.1007/s10827-016-0627-3. Epub 2016 Sep 27.
Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.
主动运动期间肌梭放电是机械参数和神经参数的函数。肌肉长度变化（及其导数）代表其主要的机械参数，而肌梭运动驱动则代表其神经成分。然而，肌梭运动驱动，尤其是γ驱动的作用和功能尚未明确确立，因为在自愿的非运动性运动期间γ运动活动在很大程度上仍然未知。在此，我们采用计算方法，探讨γ驱动是否会出现在与生物力学拮抗腕部模拟器相连的皮质脊髓系统人工神经网络模型中。腕部模拟器包括对长度敏感且依赖γ驱动的Ia型和II型肌梭活动。通过梯度下降算法得出网络活动和连接性，以在腕部屈伸（F/E）运动期间生成相互的、已知的目标α运动单位活动。模拟了两项任务：交替F/E任务和缓慢F/E跟踪任务。在交替F/E网络中γ运动活动的出现是α运动单位驱动的函数：如果驱动α运动单位需要肌肉传入（连同脊髓上的）输入，那么γ驱动会以α-γ共同激活的形式出现，正如实证研究所预测的那样。在缓慢F/E跟踪网络中，γ驱动以α-γ分离的形式出现，并为包含已知双向目标单位的皮质网络提供关键的双向肌肉传入活动。因此，该模型展示了肌梭输出以及γ驱动的互补方面：i）肌梭活动作为α运动单位活动的驱动力，以及ii）传入活动提供连续的感觉信息，这两者都关键地依赖于γ驱动。
