在神經系統模擬的研究上，有尖峰響應模型(Spike Response Model)來描述多神經系統動力學，有霍吉金-赫胥黎模型(Hodgkin–Huxley model)來描述單神經元及離子通道開關的動態變化，有菲茨休-南雲模型(FitzHugh-Nagumo model)來分析神經元的狀態演化，卻沒有任何一個模型可以用來描述離子通道這個層次的動力學。
　　離子通道的動力學之所以重要，是因為影響整個神經傳遞最基本的因素就只有離子的變化而已。但是過去的模型都用電阻來模擬離子通道，因此無法探討微觀中每個離子的動力學。我們利用可控閘極電壓式單電子電晶體模型(Controllable gate voltage single-electron transistor model)來完整描述離子通道的行為。
　　在這個模型中，我們需要考慮時變的閘極電壓。因此我們以微擾理論給出閘極電壓與系統環境間耦合強度的關係。並利用非平衡格林函數(Non-Equilibrium Green's Function)給出系統的粒子數及電流隨時間的演化。
　　我們整合霍吉金-赫胥黎模型及可控閘極電壓式單電子電晶體模型做反向傳播(backpropagation)，提出一個新的神經系統模型。利用可控閘極電壓式單電子電晶體模型模擬單離子通道算出的電流，加上霍吉金-赫胥黎模型給出單神經中離子通道開啟的數量，可以得到膜電位的變化。再修正霍吉金-赫胥黎模型所得到的膜電位誤差，做多次反向傳播後誤差將會收斂。最後我們再加入尖峰響應模型，考慮其他神經元的訊號以及動作電位後的閥值修正，即可得到完整的神經系統模型。

　　In neuronal dynamics studies, the spike response model is used to describe multiple neurons dynamics. The Hodgkin-Huxley model is used to describe the dynamics of single neuron and the total ion channel gates. The FitzHugh-Nagumo model is used to analyze the state evolution of single neuron. However, no model can be used to describe the dynamics of single ion channel.
　　The dynamics of a single ion channel is important, because the most fundamental factor affecting the entire neuronal dynamics is the dynamics of every single ion. But in the past, all models used a resistor to replace a single ion channel. So, the dynamics of single ion in the microscopic cannot be explored. And so now, we use a gate voltage controllable single-electron transistor model to describe the dynamics of the single ion channel.
　　In this model, we need to consider the time-dependent gate voltage. Therefore, we use the perturbation theory to give the relationship between the gate voltage and the coupling strength between the system and environment. And the non-equilibrium Green's function is used to give the evolution of the number of particles and the current over time.
　　We combine the Hodgkin-Huxley model with a gate voltage controllable single-electron transistor model. Then use backpropagation approach to reduce the error. Finally, we propose a new complete neuronal dynamics algorithm model.

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