电磁场对神经元网络同步影响的研究

发布时间：2018-03-23 17:35

本文选题：神经元　切入点：神经元网络　出处：《天津大学》2013年博士论文

【摘要】：神经网络的同步放电活动广泛的存在于中枢系统中，并在大脑功能的实现中扮演者重要的角色。本文基于外电场作用下的HH模型，分别研究了直流电场和交流电场对神经网络同步活动的影响。在直流电场分析中，研究了电场及网络参数对抑制性网络活动的影响，发现当网络振荡频率处于gamma频段内才对应着较高的同步系数。对于兴奋—抑制性网络来说，在一定的电场区间内可以有效地减弱由于兴奋性群体引入适应性电流或去除抑制性群体内部的突触连接所引起的网络活动变弱的负面影响，还能削弱由于刺激竞争所引起的子网络同步性下降的趋势。研究了不同网络拓扑结构下（随机、小世界、模块化网络）的同步活动，发现不同的拓扑结构对应的同步系数曲线形态各不相同，并分析了网络参数对同步系数的影响。在交流电场分析中，通过ISI序列分析发现了只有当抑制性网络中的神经元处于单周期的放电模式时才会出现较高的同步系数。对于兴奋—抑制性网络来说，分析并得出了交流电场参数对网络同步系数和振荡频率的作用规律，发现低频段电场对网络活动的影响较大。研究发现不同网络拓扑结构（随机、小世界网络）对网络同步活动的影响没有明显区别，网络参数的作用主要体现在对同步系数和放电率的局部调节。时滞普遍存在于神经网络中，本文研究了直流和交流电场作用下时滞和网络同步之间的关系。分别以电耦合的小世界网络和化学突触耦合的随机网络为对象，，分析了不同形式的时滞常数和网络参数对神经网络同步的影响规律。为了探究磁场和皮层网络之间的作用机制，本文以二维的Izhikevich神经元模型为基础，研究了外部磁场及网络参数对小世界网络和模块化网络同步活动的影响。在此基础上，引入突触的STDP学习机制，分析了磁场参数和STDP学习次数对小规模的同质性随机网络和大规模的异质性随机网络的同步活动的影响。本文的研究结果是为揭示外部电磁场对大脑活动的影响提供一些理论上的指导，并为利用磁场刺激来治疗一些精神疾病或者缓解一定的病症提供一定的思路。
[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.
【学位授予单位】：天津大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：R312;R741

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