驟放式發火神經元的數值模擬

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姓名

周祐陞(Yu-Sheng Chou) 查詢紙本館藏

畢業系所

物理學系

論文名稱

驟放式發火神經元的數值模擬
(Numerical Simulations of Bursting Activities in Neuronal System)

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摘要(中)

我們發展了一個現象模型以及修改Sivan 模型來模擬神經細胞-膠質細胞連結的網絡中神經細胞驟放式的發火。首先，我們提出一個抑制機制來代表膠質細胞的動力學，這個抑制機制與神經模型(FitzHugh-Nagumo 模型)組合在一起。當神經與膠質細胞的平均連結強度超過某個門檻之後，神經細胞的驟放式行為就會發生。其中的平均場模型和網絡模型都得到類似的結果，在網絡中的任何連結都會增加神經發火的同步性。第二，我們使用Sivan 模型，它提供了與實驗相似的膜電位活動，模型中的鈣離子動力學導致神經的驟放式發火，因而把鈣離子視為膠質細胞的效應。驟放式的發火可以完全由神經本身或是膠質細胞的擾動引起，這種發火的同調性會隨著擾動的增加而變大。而在Sivan 所組成的網絡中，神經的發火頻率與相互的同步性皆會隨著連結強度和擾動的大小而有所改變。

摘要(英)

We develop a phenomenological model and modify the Sivan model[1] to simulate the neuron bursting behavior to model the biological neuron-glia network. First, we propose a generic inhibitory mechanism to represent glia dynamics and combine with the spiking neuron model(FitzHugh-Nagumo model[2]). Bursting occurs when mean connection strength of neurons with the inhibitory elements exceeds a threshold value. Both the mean-field model and the network model show similar results and the synchronization of firing can be enhanced by connections in the network including neurons and glial cells. Secondly, we use the Sivan model which provides a similar membrane voltage activity to experiment results. The calcium dynamics in Sivan model leads to bursting behavior and can be regarded as an effect from the glial cells. Bursting can be induced purely by intrinsic neuron noise and the fluctuations
of glial activity where burst coherence increases with noise in both parts. In the network system, the bursting frequency and synchronization between neurons are also computed with different connection strengths and noise levels.

關鍵字(中)

★ 神經驟放式發火

關鍵字(英)

★ neuron bursting

論文目次

1 Introduction 1
2 Theoretical background and The Models 6
2.1 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.1 Hodgkin-Huxley model and FitzHugh-Nagumo model . . . . . 6
2.1.2 Coherence resonance . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 The inhibitory mechanism on neuron model . . . . . . . . . . . . . . 13
2.3 Sivan bursting neuron Model . . . . . . . . . . . . . . . . . . . . . . . 17
3 Results and Discussion 20
3.1 Mean field FHN-g model . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.1 Noise-free case . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1.2 Noise-enhanced bursting . . . . . . . . . . . . . . . . . . . . . 21
3.2 FHN-g network model . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3 Noise effect on Sivan model . . . . . . . . . . . . . . . . . . . . . . . 39
3.4 Network of the Noisy Sivan model . . . . . . . . . . . . . . . . . . . . 44
4 Conclusions and Outlook 52
Bibliography 55
Appendix 58
A Nernst and Goldman equations 58
B Gating functions and parameters of models 61
B.1 Hodgkin-Huxley model . . . . . . . . . . . . . . . . . . . . . . . . . . 61
B.2 Sivan Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

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指導教授

黎璧賢(Pik-Yin Lai)

審核日期

2007-7-19

推文