Ebadzadeh M, Tondu B, Darlot C
Ecole Nationale Supérieure des Télécommunications, CNRS URA 820, Département de Traitement des Signaux et des Images, 46 rue Barrault 75634 Paris 13, France.
Neuroscience. 2005;133(1):29-49. doi: 10.1016/j.neuroscience.2004.09.048. Epub 2005 Apr 22.
The command and control of limb movements by the cerebellar and reflex pathways are modeled by means of a circuit whose structure is deduced from functional constraints. One constraint is that fast limb movements must be accurate although they cannot be continuously controlled in closed loop by use of sensory signals. Thus, the pathways which process the motor orders must contain approximate inverse functions of the bio-mechanical functions of the limb and of the muscles. This can be achieved by means of parallel feedback loops, whose pattern turns out to be comparable to the anatomy of the cerebellar pathways. They contain neural networks able to anticipate the motor consequences of the motor orders, modeled by artificial neural networks whose connectivity is similar to that of the cerebellar cortex. These networks learn the direct biomechanical functions of the limbs and muscles by means of a supervised learning process. Teaching signals calculated from motor errors are sent to the learning sites, as, in the cerebellum, complex spikes issued from the inferior olive are conveyed to the Purkinje cells by climbing fibers. Learning rules are deduced by a differential calculation, as classical gradient rules, and they account for the long term depression which takes place in the dendritic arborizations of the Purkinje cells. Another constraint is that reflexes must not impede voluntary movements while remaining at any instant ready to oppose perturbations. Therefore, efferent copies of the motor orders are sent to the interneurones of the reflexes, where they cancel the sensory-motor consequences of the voluntary movements. After learning, the model is able to drive accurately, both in velocity and position, angular movements of a rod actuated by two pneumatic McKibben muscles. Reflexes comparable to the myotatic and tendinous reflexes, and stabilizing reactions comparable to the cerebellar sensory-motor reactions, reduce efficiently the effects of perturbing torques. These results allow to link the behavioral concepts of the equilibrium-point "lambda model" [J Motor Behav 18 (1986) 17] with anatomical and physiological features: gains of reflexes and sensori-motor reactions set the slope of the "invariant characteristic," and efferent copies set the "threshold of the stretch reflex." Thus, mathematical and physical laws account for the raison d'etre of the inhibitory nature of Purkinje cells and for the conspicuous anatomical pattern of the cerebellar pathways. These properties of these pathways allow to perform approximate inverse calculations after learning of direct functions, and insure also the coordination of voluntary and reflex motor orders.
小脑和反射通路对肢体运动的控制是通过一个电路模型来模拟的,该电路的结构是根据功能限制推导出来的。一个限制是,快速肢体运动必须准确,尽管它们不能通过使用感觉信号在闭环中持续控制。因此,处理运动指令的通路必须包含肢体和肌肉生物力学功能的近似逆函数。这可以通过并行反馈回路来实现,其模式与小脑通路的解剖结构相似。它们包含能够预测运动指令的运动后果的神经网络,由人工神经网络建模,其连接性与小脑皮质相似。这些网络通过监督学习过程学习肢体和肌肉的直接生物力学功能。从小脑运动误差计算得出的教学信号被发送到学习位点,就像在小脑中,从下橄榄核发出的复合尖峰通过攀缘纤维传递到浦肯野细胞一样。学习规则通过微分计算推导得出,如同经典梯度规则一样,它们解释了浦肯野细胞树突分支中发生的长时程抑制。另一个限制是,反射在任何时刻都必须随时准备对抗干扰,同时又不能妨碍自主运动。因此,运动指令的传出副本被发送到反射的中间神经元,在那里它们消除自主运动的感觉运动后果。学习后,该模型能够精确地驱动由两个气动麦基本肌肉驱动的杆的角运动,无论是在速度还是位置上。与肌牵张反射和腱反射相当的反射,以及与小脑感觉运动反应相当的稳定反应,有效地减少了干扰扭矩的影响。这些结果使得平衡点“λ模型”[《运动行为杂志》18(1986)17]的行为概念与解剖学和生理学特征联系起来:反射和感觉运动反应的增益设定了“不变特征”的斜率,传出副本设定了“牵张反射的阈值”。因此,数学和物理定律解释了浦肯野细胞抑制性本质的存在理由以及小脑通路明显的解剖模式。这些通路的这些特性使得在学习直接功能后能够进行近似逆计算,并且还确保了自主运动指令和反射运动指令的协调。