Shimansky Y P
Division of Neurobiology, Barrow Neurological Institute, Phoenix, AZ 85013, USA.
Biol Cybern. 2000 Oct;83(4):379-89. doi: 10.1007/s004220000159.
The existence and utilization of an internal representation of the controlled object is one of the most important features of the functioning of neural motor control systems. This study demonstrates that this property already exists at the level of the spinal motor control system (SMCS), which is capable of generating motor patterns for reflex rhythmic movements, such as locomotion and scratching, without the aid of the peripheral afferent feedback, but substantially modifies the generated activity in response to peripheral afferent stimuli. The SMCS is presented as an optimal control system whose optimality requires that it incorporate an internal model (IM) of the controlled object's dynamics. A novel functional mechanism for the integration of peripheral sensory signals with the corresponding predictive output from the IM, the summation of information precision (SIP) is proposed. In contrast to other models in which the correction of the internal representation of the controlled object's state is based on the calculation of a mismatch between the internal and external information sources, the SIP mechanism merges the information from these sources in order to optimize the precision of the controlled object's state estimate. It is demonstrated, based on scratching in decerebrate cats as an example of the spinal control of goal-directed movements, that the results of computer modeling agree with the experimental observations related to the SMCS's reactions to phasic and tonic peripheral afferent stimuli. It is also shown that the functional requirements imposed by the mathematical model of the SMCS comply with the current knowledge about the related properties of spinal neuronal circuitry. The crucial role of the spinal presynaptic inhibition mechanism in the neuronal implementation of SIP is elucidated. Important differences between the IM and a state predictor employed for compensating for a neural reflex time delay are discussed.
受控对象内部表征的存在与利用是神经运动控制系统功能的最重要特征之一。本研究表明，这一特性在脊髓运动控制系统（SMCS）层面已然存在，该系统能够在无需外周传入反馈的情况下，为诸如行走和抓挠等反射性节律运动生成运动模式，但会根据外周传入刺激对所生成的活动进行实质性调整。SMCS被视为一个最优控制系统，其最优性要求它纳入受控对象动力学的内部模型（IM）。本文提出了一种将外周感觉信号与IM相应预测输出进行整合的新型功能机制——信息精度求和（SIP）。与其他模型不同，在其他模型中受控对象状态的内部表征校正基于内部和外部信息源之间不匹配的计算，而SIP机制将这些源的信息合并，以优化受控对象状态估计的精度。以去大脑猫的抓挠为例，作为对目标导向运动的脊髓控制，结果表明计算机建模结果与有关SMCS对相位性和紧张性外周传入刺激反应的实验观察结果一致。研究还表明，SMCS数学模型所施加的功能要求符合当前关于脊髓神经元回路相关特性的知识。阐明了脊髓突触前抑制机制在SIP神经元实现中的关键作用。讨论了IM与用于补偿神经反射时间延迟的状态预测器之间的重要差异。
