视动反应中PF-PC突触钙级联机制：物质状态预测

Calcium Cascade of PF-PC Synapses in Optokinetic Response: Prediction of Material State

摘要: 小脑的运动学习记忆所涉及的PF-PC突触权重变化被称为钙级联机制，这种机制利用化学受体磷酸化和去磷酸化的动态调节作用来模拟突触权重的变化。鉴于钙级联涉及多种化学物质组成的复杂动力学系统，在计算刺激信号是随机输入的视动反应时耗时较大，本文创新地采用与常微分方程性质相似的长短期记忆网络来预测短周期连续状态序列，从而在保证准确性的同时，有效减少了计算成本。不仅对小脑运动学习记忆形成有着理论意义，而且为小脑控制器提供工程上的应用价值。

Abstract: The change of PF-PC synaptic weight involved in cerebellar motor learning and memory is called calcium cascade. This mechanism uses the dynamic regulation of chemical receptor phosphorylation and dephosphorylation to simulate the change of synaptic weight. Since the calcium cascade involves a complex dynamic system composed of a variety of chemicals, it takes a lot of time to calculate the optokinetic response when the stimulus signal is random input. In this paper, the short-term and long-term memory network with similar properties to ordinary differential equations is innovatively used to predict the short-term continuous state sequence, so as to ensure the accuracy and effectively reduce the computational cost. It not only has theoretical significance for the formation of cerebellar motor learning and memory, but also provides engineering application value for cerebellar controller.