本文選題：神經(jīng)元　切入點：神經(jīng)元網(wǎng)絡(luò)　出處：《天津大學(xué)》2013年博士論文

【摘要】：神經(jīng)網(wǎng)絡(luò)的同步放電活動廣泛的存在于中樞系統(tǒng)中，并在大腦功能的實現(xiàn)中扮演者重要的角色。 本文基于外電場作用下的HH模型，分別研究了直流電場和交流電場對神經(jīng)網(wǎng)絡(luò)同步活動的影響。在直流電場分析中，研究了電場及網(wǎng)絡(luò)參數(shù)對抑制性網(wǎng)絡(luò)活動的影響，發(fā)現(xiàn)當(dāng)網(wǎng)絡(luò)振蕩頻率處于gamma頻段內(nèi)才對應(yīng)著較高的同步系數(shù)。對于興奮—抑制性網(wǎng)絡(luò)來說，在一定的電場區(qū)間內(nèi)可以有效地減弱由于興奮性群體引入適應(yīng)性電流或去除抑制性群體內(nèi)部的突觸連接所引起的網(wǎng)絡(luò)活動變?nèi)醯呢?fù)面影響，還能削弱由于刺激競爭所引起的子網(wǎng)絡(luò)同步性下降的趨勢。研究了不同網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)下（隨機、小世界、模塊化網(wǎng)絡(luò)）的同步活動，發(fā)現(xiàn)不同的拓?fù)浣Y(jié)構(gòu)對應(yīng)的同步系數(shù)曲線形態(tài)各不相同，并分析了網(wǎng)絡(luò)參數(shù)對同步系數(shù)的影響。 在交流電場分析中，通過ISI序列分析發(fā)現(xiàn)了只有當(dāng)抑制性網(wǎng)絡(luò)中的神經(jīng)元處于單周期的放電模式時才會出現(xiàn)較高的同步系數(shù)。對于興奮—抑制性網(wǎng)絡(luò)來說，分析并得出了交流電場參數(shù)對網(wǎng)絡(luò)同步系數(shù)和振蕩頻率的作用規(guī)律，發(fā)現(xiàn)低頻段電場對網(wǎng)絡(luò)活動的影響較大。研究發(fā)現(xiàn)不同網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)（隨機、小世界網(wǎng)絡(luò)）對網(wǎng)絡(luò)同步活動的影響沒有明顯區(qū)別，網(wǎng)絡(luò)參數(shù)的作用主要體現(xiàn)在對同步系數(shù)和放電率的局部調(diào)節(jié)。 時滯普遍存在于神經(jīng)網(wǎng)絡(luò)中，本文研究了直流和交流電場作用下時滯和網(wǎng)絡(luò)同步之間的關(guān)系。分別以電耦合的小世界網(wǎng)絡(luò)和化學(xué)突觸耦合的隨機網(wǎng)絡(luò)為對象，，分析了不同形式的時滯常數(shù)和網(wǎng)絡(luò)參數(shù)對神經(jīng)網(wǎng)絡(luò)同步的影響規(guī)律。 為了探究磁場和皮層網(wǎng)絡(luò)之間的作用機制，本文以二維的Izhikevich神經(jīng)元模型為基礎(chǔ)，研究了外部磁場及網(wǎng)絡(luò)參數(shù)對小世界網(wǎng)絡(luò)和模塊化網(wǎng)絡(luò)同步活動的影響。在此基礎(chǔ)上，引入突觸的STDP學(xué)習(xí)機制，分析了磁場參數(shù)和STDP學(xué)習(xí)次數(shù)對小規(guī)模的同質(zhì)性隨機網(wǎng)絡(luò)和大規(guī)模的異質(zhì)性隨機網(wǎng)絡(luò)的同步活動的影響。 本文的研究結(jié)果是為揭示外部電磁場對大腦活動的影響提供一些理論上的指導(dǎo)，并為利用磁場刺激來治療一些精神疾病或者緩解一定的病癥提供一定的思路。
[Abstract]:The synchronous discharge activity of neural network exists widely in the central system and plays an important role in the realization of brain function. Based on the HH model under the external electric field, the effects of DC field and AC electric field on the synchronous activity of neural network are studied respectively. In the analysis of DC field, the influence of electric field and network parameters on the inhibitory network activity is studied. It is found that the oscillation frequency of the network corresponds to a higher synchronization coefficient when the oscillation frequency of the network is in the gamma band. In a certain electric field range, the negative effects of weak network activity caused by the introduction of adaptive currents in excitatory populations or the removal of synaptic connections within inhibitory populations can be effectively mitigated. It also weakens the downward trend of the synchronicity of subnetworks caused by the stimulation of competition. The synchronization activities of different network topologies (random, small world, modular networks) are studied. It is found that the shapes of synchronization coefficient curves are different for different topological structures, and the influence of network parameters on synchronization coefficient is analyzed. In AC field analysis, ISI sequence analysis shows that high synchronization coefficient occurs only when the neurons in the inhibitory network are in a single cycle discharge mode. The effects of AC field parameters on the synchronization coefficient and oscillation frequency of the network are analyzed and obtained. It is found that the electric field in the low frequency band has a great influence on the network activity. There is no significant difference in the effect of small-world network on network synchronization activity. The effect of network parameters is mainly reflected in the partial adjustment of synchronization coefficient and discharge rate. Time delay is ubiquitous in neural networks. In this paper, the relationship between delay and network synchronization under DC and AC electric fields is studied. The electrically coupled small-world networks and the chemical synaptic coupled stochastic networks are taken as objects, respectively. The effects of delay constants and network parameters on neural network synchronization are analyzed. In order to explore the mechanism of interaction between magnetic field and cortical network, the effects of external magnetic field and network parameters on synchronization of small-world and modular networks are studied based on two-dimensional Izhikevich neuron model. The STDP learning mechanism of synapses is introduced to analyze the effects of magnetic field parameters and STDP learning times on the synchronization activities of small homogeneous stochastic networks and large-scale heterogeneous random networks. The results of this paper provide some theoretical guidance for revealing the effects of external electromagnetic fields on brain activity, and provide some ideas for the use of magnetic field stimulation to treat some mental disorders or alleviate certain diseases.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：R312;R741

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