本文關(guān)鍵詞： 神經(jīng)網(wǎng)絡 突觸可塑性 Izhikevich 神經(jīng)元 放電節(jié)律 興奮性神經(jīng)元抑制性神經(jīng)元　出處：《河北師范大學》2017年碩士論文　論文類型：學位論文

【摘要】：人腦含有上千億的神經(jīng)元細胞,有超過1410個神經(jīng)突觸,其結(jié)構(gòu)與功能極為復雜,需要從不同層次對其進行探究。神經(jīng)網(wǎng)絡與全腦功能是神經(jīng)信息學研究的重點,也是人類腦計劃中的重要研究項目。突觸是神經(jīng)元之間信息傳遞的主要結(jié)構(gòu),它有在一定條件下增減數(shù)目、改變形態(tài)及調(diào)整功能的能力,即突觸可塑性,大量研究證明突觸可塑性是學習與記憶的細胞分子生物學基礎。本文將通過神經(jīng)網(wǎng)絡模型從理論上研究突觸可塑性修正參數(shù)、抑制性神經(jīng)元所占比例對神經(jīng)元放電活動的影響。本文研究內(nèi)容分為以下三部分:第一部分研究了突觸權(quán)重修正強度對神經(jīng)網(wǎng)絡放電活動的影響�？伤苄陨窠�(jīng)網(wǎng)絡中,單個神經(jīng)元的動力學由Izhikevich模型決定,突觸可塑性采用依賴神經(jīng)元脈沖時序的可塑性規(guī)則(STDP),同時考慮了脈沖在軸突上的傳導延遲。研究表明,此模型描述的放電活動中能出現(xiàn)?節(jié)律,?節(jié)律的呈現(xiàn)依賴突觸權(quán)重的修正強度:對于突觸權(quán)重增強修正的最大值(10)A,當??13.009.0(10)A時,網(wǎng)絡在3600s的放電時間內(nèi)存在?節(jié)律。其中當0.09 A0.1(10)??時,隨著A(10)的增大,?節(jié)律首次出現(xiàn)的時間有所推遲,然而當0.1 A0.13(10)??時,隨著(10)A的增大,?節(jié)律首次出現(xiàn)的時間有所提前;對于突觸權(quán)重抑制修正的最大值-A,當??13.012.0-A時,網(wǎng)絡在3600s的放電時間內(nèi)存在?節(jié)律,并且隨著-A的增大,?節(jié)律首次出現(xiàn)的時間有所提前。此部分內(nèi)容在本文第二章。第二部分研究了描述權(quán)重變化快慢時間參數(shù)?對神經(jīng)網(wǎng)絡放電活動的影響。研究表明,當??217?時,網(wǎng)絡在3600s的放電時間內(nèi)存在?節(jié)律,并且隨著?的減小,?節(jié)律首次出現(xiàn)的時間有所提前。此部分內(nèi)容在本文第二章。第三部分研究了抑制性神經(jīng)元所占比例對神經(jīng)網(wǎng)絡放電節(jié)律的影響。為了腦電平衡,神經(jīng)網(wǎng)絡中既有興奮性神經(jīng)元也存在一定數(shù)量的抑制性神經(jīng)元。興奮性神經(jīng)元主要負責傳遞信息;抑制性神經(jīng)元通過釋放抑制性遞質(zhì),使神經(jīng)網(wǎng)絡的活動始終處于一種穩(wěn)定的狀態(tài)。雖然抑制性神經(jīng)元相比于興奮性神經(jīng)元的數(shù)目只是少量的,但它們對于神經(jīng)網(wǎng)絡中神經(jīng)編碼的演化是極其重要的。通過研究發(fā)現(xiàn),?節(jié)律的產(chǎn)生依賴于抑制性神經(jīng)元數(shù)量所占比例,當??174:826Ni:Ne200:800時(其中Ne:Ni為興奮性神經(jīng)元與抑制性神經(jīng)元數(shù)量之比),網(wǎng)絡在3600s的放電時間內(nèi)才會存在?節(jié)律。此外給出了?節(jié)律首次出現(xiàn)的時間隨抑制性神經(jīng)元的數(shù)量所占比例的增減而發(fā)生的變化。在整個變化的過程中存在三個特殊的比例,通過對比這種情況下網(wǎng)絡的放電神經(jīng)元數(shù)目與時間的關(guān)系發(fā)現(xiàn),大數(shù)量的放電事件比較稀疏時,它的放電頻譜分布圖中有?節(jié)律的產(chǎn)生,若大數(shù)量的放電事件比較密集時,它的放電頻譜分布圖中沒有?節(jié)律的產(chǎn)生。
[Abstract]:The human brain contains hundreds of billions of neuronal cells, more than 1,410 synapses, the structure and function of which are extremely complex and need to be explored at different levels. Neural networks and whole brain functions are the focus of neuroinformatics. Synapse is the main structure of the transmission of information between neurons. It has the ability to increase and decrease the number, change shape and adjust function under certain conditions, that is, synaptic plasticity. A large number of studies have proved that synaptic plasticity is the cellular and molecular basis of learning and memory. The effect of the proportion of inhibitory neurons on the firing activity of neurons. This paper is divided into three parts: the first part studies the effect of the strength of synaptic weight correction on the discharge activity of neural networks. The dynamics of a single neuron is determined by the Izhikevich model. The synaptic plasticity is based on the plasticity rule of neuron pulse timing and the conduction delay of the pulse on the axon is taken into account. Rhythm? The presentation of rhythm depends on the correction intensity of synaptic weight: for the maximum value of synaptic weight enhancement correction, 10 A, when? ? At 13.009.0 ~ 10A, the network exists in the discharge time of 3600s? Rhythm. When 0. 09 A 0. 1 0. 0? ? Time, with the increase of A10)? The first time the rhythm appeared was delayed, however, when 0.1 A 0.13 + 10? ? With the increase of 10A? The first time of the rhythm appeared earlier; for the synaptic weight inhibition correction of the maximum A, when? ? At 13.012.0-A, the network exists in the discharge time of 3600s? Rhythm, and with the increase of A? The first time of the rhythm appears in advance. This part of the content in the second chapter. The second part of the study describes the weight changes of the fast and slow time parameters? Effects on the discharge activity of neural networks. ? 217? The network exists during the discharge time of 3600s. Rhythm, and with it? The reduction? In chapter 2, the effect of the proportion of inhibitory neurons on the discharge rhythm of neural networks was studied. There are not only excitatory neurons but also a certain number of inhibitory neurons in neural networks. Excitatory neurons are mainly responsible for transmitting information; inhibitory neurons release inhibitory transmitters. The activity of neural networks is always in a stable state. Although the number of inhibitory neurons compared to excitatory neurons is only a small number, they are extremely important for the evolution of neural coding in neural networks. The production of rhythms depends on the proportion of inhibitory neurons. ? 174: 826Ni2: 800 (where Ne:Ni is the ratio of the number of excitatory to inhibitory neurons, and the network does not exist until 3600s? Rhythm. Also given? The first time that the rhythm changes as the number of inhibitory neurons increases or decreases. There are three special proportions in the whole process. By comparing the relationship between the number of firing neurons and the time of the network, it is found that when a large number of discharge events are sparse, the discharge spectrum of the network has? When a large number of discharge events are dense, the rhythm is not found in the discharge spectrum. The production of rhythm.
【學位授予單位】：河北師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R338

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