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目的利用小脑的生理结构构造模拟小脑网络回路,研究小脑皮层不同神经细胞的电位发放、外界刺激对小脑皮层细胞的影响以及各类细胞电位发放模式等。方法与结果利用神经元的多房室模型和NEURON软件,研究不同输入刺激对蒲肯野细胞电位发放的影响。对颗粒细胞–高尔基细胞的反馈抑制回路对蒲肯野细胞的时间聚焦以及平行纤维–篮状/星状细胞局部抑制回路对蒲肯野细胞的空间聚焦现象进行了验证。运用施加运动学习的小脑网络模型研究兔子眨眼的条件反射现象,用模型的电位发放指标反映学习后兔子眨眼的实验现象。当刺激信号从攀状纤维输入时,通过精确放电时间依赖的突触可塑性学习,兔子眨眼的适应作用逐渐达到稳定状态。结论本文构造的小脑皮层网络真实可靠。模型的数值结果证实,小脑皮层经过精确放电时间依赖的突触可塑性学习后,输出信号稳定,可以执行时间聚焦和空间聚焦的功能。
Objective To simulate the cerebellum network circuit by using the physiological structure of the cerebellum to study the potential distribution of different nerve cells in the cerebellar cortex and the effects of external stimuli on the cerebellar cortex cells and various cell potential distribution patterns. Methods and Results Neuronal multi-compartment model and NEURON software were used to study the effect of different input stimuli on Purkinje cell potential distribution. The time focusing on the Purkinje cells and the spatial focusing on the Purkinje cells by the parallel fiber-basal / stellate partial repression circuits were validated by the feedback inhibition of granulocyte-Golgi cells. Using the model of cerebellum exerted by motor learning, we studied the conditional reflex of rabbit’s blink, and used the model’s potential distribution to reflect the experimental phenomenon of rabbit blink after learning. When the stimulation signal is input from the pectinate fibers, the adaptive effect of the rabbit’s blink gradually reaches a steady state by the precise discharge time-dependent synaptic plasticity learning. Conclusion The cerebellar cortex network constructed in this paper is reliable. Numerical results of the model confirm that the cerebellar cortex undergoes accurate discharge time-dependent synaptic plasticity learning and the output signal is stable, allowing time focusing and spatial focusing to be performed.