Cerebellar learning of accurate predictive control for fast-reaching movements
Cerebellar learning of accurate predictive control for fast-reaching movements
复制标题
小脑学习快速运动的精确预测控制
作者：
J. Spoelstra;N. Schweighofer;M. Arbib
抽象的。神经系统中长时间的传导延迟阻碍了仅通过反馈控制对运动的精确控制。我们提出了一个新的，生物学上合理的小脑模型来研究如何快速手臂运动可以执行，尽管这些延迟。为了提供一个逼真的小脑神经模型的测试平台，我们将小脑网络嵌入到一个模拟的生物运动系统中，该系统包括一个脊髓模型和一个六肌肉二维手臂模型。我们认为，如果在脊髓水平检测到的轨迹错误，在小脑的记忆痕迹可以解决的时间不匹配的问题之间的传出运动命令和延迟的错误信号。此外，通过在模型中加入小脑-核-橄榄环，学习变得稳定。结果表明，小脑网络实现了一个非线性预测调节器，通过学习的植物和脊髓电路的逆动力学的一部分。在学习之后，可以生成快速准确的到达运动。
Abstract. Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between efferent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated.