【摘要】 神经元接受到的外界信号是动态变化的,神经放电节律模式则会依据一定的规律动态转化来反映这种变化,以往确定性理论模型(如Chay模型和Rose-Hindmarsh模型)模拟出了部分神经放电模式转化的整体分岔规律。利用Chay模型仿真,通过调节具有生理学意义的参数,模拟出了神经元放电的一系列分岔序列,同时在神经起步点的实验中,应用与模型对应的参数进行调节,观察到了与仿真结果整体上一致的分岔序列,印证了数值模拟的结果,展现了真实的神经元放电整体分岔结构的基本规律,为理解具体的生理调节活动中神经放电节律的转化提供了理论基础。更多还原

【Abstract】 The firing patterns of a neuron would transit regularly and dynamically with the dynamic variation of the input signals to the neuron. Such a dynamic transition thus represented the feature of the input signals. Neuron firing pattern transitions were governed by the bifurcation regularities. The whole bifurcation scenarios in neural firing pattern transitions were simulated with Chay model. A series of bifurcation scenarios in the neural firing pattern transitions were simulated by adjusting the physiological parameters like vc (the reversal potential of calcium concentration) and λn in Chay model. Four types of bifurcation scenarios related to extracellular calcium concentration were discovered in experiments on neural pacemakers. The results not only verified the existence and complexity of various bifurcation scenarios in neuronal systems, but also provided a theoretical foundation for the study of neural coding.更多还原