Objective: To investigate the spike activities of cerebellar cortical cells in a computational network model constructed based on the anatomical structure of cerebellar cortex.
Methods and results: The multicompartment model of neuron and NEURON software were used to study the external influences on cerebellar cortical cells. Various potential spike patterns in these cells were obtained. By analyzing the impacts of different incoming stimuli on the potential spike of Purkinje cell, temporal focusing caused by the granule cell-golgi cell feedback inhibitory loop to Purkinje cell and spatial focusing caused by the parallel fiber-basket/stellate cell local inhibitory loop to Purkinje cell were discussed. Finally, the motor learning process of rabbit eye blink conditioned reflex was demonstrated in this model. The simulation results showed that when the afferent from climbing fiber existed, rabbit adaptation to eye blinking gradually became stable under the Spike Timing-Dependent Plasticity (STDP) learning rule.
Conclusion: The constructed cerebellar cortex network is a reliable and feasible model. The model simulation results confirmed the output signal stability of cerebellar cortex after STDP learning and the network can execute the function of spatial and temporal focusing.
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。