本文選題：前饋神經(jīng)元網(wǎng)絡　切入點：皮層隨機神經(jīng)元網(wǎng)絡　出處：《天津大學》2014年博士論文　論文類型：學位論文

【摘要】：大腦是人體最為復雜的系統(tǒng)，不同神經(jīng)元和神經(jīng)元網(wǎng)絡是實現(xiàn)復雜腦功能的基礎(chǔ)，信息在神經(jīng)元及神經(jīng)元網(wǎng)絡中的檢測和傳導是神經(jīng)科學中的一個重要問題。因此，本論文旨在通過構(gòu)建外部刺激作用下的網(wǎng)絡模型，包括前饋神經(jīng)元網(wǎng)絡、皮層隨機神經(jīng)元網(wǎng)絡以及海馬電阻網(wǎng)絡，揭示外部刺激下，神經(jīng)元網(wǎng)絡神經(jīng)編碼的傳導特性及共振等特性，進一步分析如何基于這些特性利用外部刺激對神經(jīng)信息傳導進行調(diào)控。 本文首先構(gòu)建了不同神經(jīng)元模型組成的前饋神經(jīng)元網(wǎng)絡，分析網(wǎng)絡中信息傳導的基本規(guī)律。噪聲在神經(jīng)系統(tǒng)中是普遍存在的，其對神經(jīng)系統(tǒng)中很多層面都會產(chǎn)生影響。研究發(fā)現(xiàn)，噪聲對多層前饋神經(jīng)元網(wǎng)絡中的弱輸入信號傳導能力有增強作用，噪聲誘導的隨機共振成為弱信息的檢測和傳導的可能機制。從高頻周期刺激信號模擬噪聲環(huán)境所引發(fā)的振動共振研究中發(fā)現(xiàn)，前饋神經(jīng)元網(wǎng)絡中存在最優(yōu)的高頻刺激的幅值和頻率使振動共振現(xiàn)象最為顯著。通過分析網(wǎng)絡參數(shù)對前饋神經(jīng)元網(wǎng)絡共振特性的影響，發(fā)現(xiàn)連接概率、突觸時間常數(shù)、異質(zhì)性等對微弱信息檢測和傳導產(chǎn)生一定的影響。 其次，本文采用Izhikevich神經(jīng)元模型構(gòu)建了外部刺激作用下的皮層隨機神經(jīng)元網(wǎng)絡模型，從振動共振的角度研究外部刺激作用下網(wǎng)絡的同步和共振特性，以及突觸可塑性對網(wǎng)絡結(jié)構(gòu)和功能的作用規(guī)律。研究發(fā)現(xiàn)，，高頻刺激能夠提高網(wǎng)絡中弱信號的傳導能力，網(wǎng)絡的特性包括網(wǎng)絡規(guī)模、突觸連接概率以及突觸權(quán)重等能夠調(diào)節(jié)弱信號的傳導�？伤苄允桥c神經(jīng)元網(wǎng)絡學習相關(guān)的重要特性，通過對外部刺激下網(wǎng)絡的放電規(guī)律進行分析發(fā)現(xiàn)，在沒有外加交流刺激時，學習結(jié)束時網(wǎng)絡表現(xiàn)出不相關(guān)的泊松放電行為。但是，當網(wǎng)絡處于外加交流刺激時，學習過程結(jié)束時網(wǎng)絡則表現(xiàn)出自激節(jié)律放電活動。此外，可塑性的存在使外部刺激的效果累積，即使是微小的改變也能積累產(chǎn)生明顯的效果。 最后，本論文建立了外部刺激作用下的海馬CA3區(qū)椎體神經(jīng)元兩房室PR模型及相應的PR電阻耦合網(wǎng)絡模型。研究發(fā)現(xiàn)，外部刺激參數(shù)不同時，單個神經(jīng)元出現(xiàn)無放電、周期放電及無規(guī)則放電等放電模式。對網(wǎng)絡同步特性研究結(jié)果表明，外部刺激能改變海馬CA3區(qū)神經(jīng)元網(wǎng)絡的同步狀態(tài)。 本文基于同步和共振以突觸可塑性研究了外部刺激下神經(jīng)元網(wǎng)絡信息的檢測與傳導，得到了高頻刺激、噪聲以及網(wǎng)絡結(jié)構(gòu)等對信息的檢測與傳導的影響規(guī)律。本文的研究成果為神經(jīng)信息編碼的調(diào)控方法及裝置的研究提供了理論基礎(chǔ)。
[Abstract]:The brain is the most complex system of human body. Different neurons and neural networks are the basis for the realization of complex brain functions. The detection and transmission of information in neurons and neural networks is an important issue in neuroscience. The purpose of this paper is to reveal the effects of external stimuli by constructing a network model, including feedforward neural network, cortical stochastic neural network and hippocampal resistor network. The conduction and resonance characteristics of neural network coding are analyzed. Based on these characteristics, how to regulate neural information transmission by external stimuli is further analyzed. In this paper, a feedforward neural network composed of different neuron models is constructed, and the basic rules of information transmission in the neural network are analyzed. Noise is ubiquitous in the nervous system. It has an effect on many levels of the nervous system. It has been found that noise enhances the ability of weak input signal transduction in multilayer feedforward neural networks. Noise induced stochastic resonance has become a possible mechanism for detecting and conducting weak information. The vibration resonance phenomenon is most obvious because of the optimal amplitude and frequency of high frequency stimuli in feedforward neural networks. By analyzing the influence of network parameters on the resonance characteristics of feedforward neural networks, the connection probability and synaptic time constant are found. Heterogeneity has a certain effect on weak information detection and transmission. Secondly, the cortical stochastic neural network model under external stimulation is constructed by using Izhikevich neuron model, and the synchronization and resonance characteristics of the network under external stimulus are studied from the view of vibration resonance. And the effect of synaptic plasticity on network structure and function. It has been found that high frequency stimuli can improve the transmission ability of weak signals in the network, and the characteristics of the network include the scale of the network. Synaptic connection probability and synaptic weight can regulate weak signal transduction. Plasticity is an important characteristic related to neural network learning. At the end of the learning process, the network exhibits an unrelated Poisson discharge behavior. However, when the network is stimulated by an additional AC, the network shows a spontaneous rhythmic discharge activity at the end of the learning process. The existence of plasticity accumulates the effects of external stimuli, even minor changes can accumulate obvious effects. Finally, a two-compartment PR model and a corresponding PR resistance-coupled network model of the spinal neurons in the CA3 region of hippocampus induced by external stimulation were established. It was found that there was no discharge in a single neuron at the same time when the external stimulation parameters were different. Periodic and irregular discharge patterns. The results show that external stimuli can change the synchronization state of hippocampal CA3 neural networks. In this paper, the detection and transmission of neural network information under external stimuli are studied by synaptic plasticity based on synchronization and resonance, and high frequency stimuli are obtained. The effects of noise and network structure on the detection and transmission of information are studied. The research results in this paper provide a theoretical basis for the study of the regulation and control methods and devices of neural information coding.
【學位授予單位】：天津大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：R318.04;TN911.23

【參考文獻】

相關(guān)期刊論文 前2條

1 王青云;陸啟韶;;興奮性化學突觸耦合的神經(jīng)元的同步[J];動力學與控制學報;2008年01期

2 梁夏;王金輝;賀永;;人腦連接組研究:腦結(jié)構(gòu)網(wǎng)絡和腦功能網(wǎng)絡[J];科學通報;2010年16期

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