本文選題：神經元　切入點：神經元網絡　出處：《天津大學》2013年博士論文

【摘要】：神經網絡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網絡同步活動的影響。在直流電場分析中，研究了電場及網絡參數對抑制性網絡活動的影響，發(fā)現當網絡振蕩頻率處于gamma頻段內才對應著較高的同步系數。對于興奮—抑制性網絡來說，在一定的電場區(qū)間內可以有效地減弱由于興奮性群體引入適應性電流或去除抑制性群體內部的突觸連接所引起的網絡活動變弱的負面影響，還能削弱由于刺激競爭所引起的子網絡同步性下降的趨勢。研究了不同網絡拓撲結構下（隨機、小世界、模塊化網絡）的同步活動，發(fā)現不同的拓撲結構對應的同步系數曲線形態(tài)各不相同，并分析了網絡參數對同步系數的影響。 在交流電場分析中，通過ISI序列分析發(fā)現了只有當抑制性網絡中的神經元處于單周期的放電模式時才會出現較高的同步系數。對于興奮—抑制性網絡來說，分析并得出了交流電場參數對網絡同步系數和振蕩頻率的作用規(guī)律，發(fā)現低頻段電場對網絡活動的影響較大。研究發(fā)現不同網絡拓撲結構（隨機、小世界網絡）對網絡同步活動的影響沒有明顯區(qū)別，網絡參數的作用主要體現在對同步系數和放電率的局部調節(jié)。 時滯普遍存在于神經網絡中，本文研究了直流和交流電場作用下時滯和網絡同步之間的關系。分別以電耦合的小世界網絡和化學突觸耦合的隨機網絡為對象，，分析了不同形式的時滯常數和網絡參數對神經網絡同步的影響規(guī)律。 為了探究磁場和皮層網絡之間的作用機制，本文以二維的Izhikevich神經元模型為基礎，研究了外部磁場及網絡參數對小世界網絡和模塊化網絡同步活動的影響。在此基礎上，引入突觸的STDP學習機制，分析了磁場參數和STDP學習次數對小規(guī)模的同質性隨機網絡和大規(guī)模的異質性隨機網絡的同步活動的影響。 本文的研究結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，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.
【學位授予單位】：天津大學
【學位級別】：博士
【學位授予年份】：2013
【分類號】：R312;R741

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本文編號：1654478