本文關(guān)鍵詞： 生物神經(jīng)元網(wǎng)絡(luò) Hodgkin-Huxley模型 化學(xué)突觸 數(shù)值模擬 現(xiàn)場可編程門陣列　出處：《蘭州交通大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：神經(jīng)元是構(gòu)成神經(jīng)系統(tǒng)的基本單元,其主要功能是接收、整理和傳遞神經(jīng)信息;突觸是實(shí)現(xiàn)神經(jīng)元與神經(jīng)元之間信息傳遞的重要結(jié)構(gòu)。神經(jīng)沖動(dòng)的傳導(dǎo)與傳遞是研究神經(jīng)系統(tǒng)功能的重要方面。本文主要以神經(jīng)元與化學(xué)突觸構(gòu)成的神經(jīng)元網(wǎng)絡(luò)為研究對象。采用具有生物神經(jīng)元電生理特性的Hodkgin-Huxley模型作為神經(jīng)元數(shù)學(xué)模型,采用興奮的Rabinovich模型作為突觸得數(shù)學(xué)模型,對規(guī)則的神經(jīng)元鏈網(wǎng)絡(luò)與神經(jīng)元環(huán)網(wǎng)絡(luò)進(jìn)行仿真模擬和硬件實(shí)現(xiàn)。具體包含如下內(nèi)容:(1)神經(jīng)元網(wǎng)絡(luò)基礎(chǔ)及其數(shù)學(xué)模型。主要介紹神經(jīng)元與突觸相關(guān)的基礎(chǔ)知識。對神經(jīng)回路與小型神經(jīng)元網(wǎng)絡(luò)的關(guān)系進(jìn)行詳細(xì)闡述。對神經(jīng)電信號在神經(jīng)元網(wǎng)絡(luò)中的產(chǎn)生與傳播以及神經(jīng)元細(xì)胞膜兩側(cè)電位的分類進(jìn)行說明。概括現(xiàn)在建模主要采用的神經(jīng)元模型以及突觸模型。(2)神經(jīng)元網(wǎng)絡(luò)的仿真模擬研究。對HH神經(jīng)元模型與Rabinovich突觸模型組成的輻散神經(jīng)元網(wǎng)絡(luò)、聚合神經(jīng)元網(wǎng)絡(luò)、神經(jīng)元鏈網(wǎng)絡(luò)與神經(jīng)元環(huán)網(wǎng)絡(luò)進(jìn)行仿真模擬。研究突觸耦合強(qiáng)度對神經(jīng)電信號的輻散傳播的影響,聚合作用對神經(jīng)元?jiǎng)幼麟娢坏挠绊?采用不同的刺激電流對神經(jīng)元鏈網(wǎng)絡(luò)和神經(jīng)元環(huán)網(wǎng)絡(luò)進(jìn)行刺激,探索在不同刺激下,神經(jīng)電信號在神經(jīng)元網(wǎng)絡(luò)中的傳播機(jī)制,并對DSP Builder與simulink軟件仿真的一致性進(jìn)行驗(yàn)證。(3)神經(jīng)元網(wǎng)絡(luò)的硬件實(shí)現(xiàn)。運(yùn)用FPGA(Field Programmable Gate Array,現(xiàn)場可編程門陣列)對HH神經(jīng)元模型與Rabinovich突觸模型組成的神經(jīng)元鏈網(wǎng)絡(luò)與神經(jīng)元環(huán)網(wǎng)絡(luò)進(jìn)行硬件實(shí)現(xiàn)。依據(jù)仿真描述的神經(jīng)元網(wǎng)絡(luò),運(yùn)用QUARTUSⅡ軟件結(jié)合DSP Builder技術(shù),完成神經(jīng)元鏈網(wǎng)絡(luò)與神經(jīng)元環(huán)網(wǎng)絡(luò)的FPGA硬件實(shí)現(xiàn)。對神經(jīng)元網(wǎng)絡(luò)硬件施加不同的電流刺激,得到神經(jīng)元網(wǎng)絡(luò)的硬件實(shí)現(xiàn)結(jié)果。對比神經(jīng)元網(wǎng)絡(luò)的硬件實(shí)現(xiàn)結(jié)果與仿真模擬結(jié)果,對硬件實(shí)現(xiàn)具有生物神經(jīng)元電生理特性的神經(jīng)元網(wǎng)絡(luò)的正確性進(jìn)行驗(yàn)證。
[Abstract]:Neuron is the basic unit of nervous system, whose main function is to receive, organize and transmit neural information. Synapse is an important structure for the transmission of information between neurons and neurons. The conduction and transmission of nerve impulses is an important aspect of studying the function of nervous system. In this paper, the neuronal network composed of neurons and chemical synapses is mainly used. The Hodkgin-Huxley model with biological neuron electrophysiological characteristics was used as the mathematical model of neurons. Using the excited Rabinovich model as the synaptic mathematical model, The simulation and hardware implementation of regular neuronal chain network and neuronal ring network are carried out, including the following contents: 1) the basic and mathematical model of neuron network. The basic knowledge of neuron and synaptic connection is mainly introduced. The relationship between neural circuits and small neural networks is described in detail. The generation and propagation of nerve signals in neural networks and the classification of neuronal cell membrane potentials are explained. The main modeling methods are summarized. The neuronal model of HH and the synaptic model of Rabinovich were used to simulate and simulate the neural network. The divergence neuron network composed of HH neuron model and Rabinovich synaptic model was studied. The effect of synaptic coupling intensity on the divergence of nerve signal and the effect of aggregation on action potential of neurons were studied. The neuronal chain network and the neuronal loop network were stimulated by different stimulation currents to explore the transmission mechanism of the nerve signal in the neuron network under different stimuli. The consistency of DSP Builder and simulink software simulation is verified. The hardware implementation of the neuronal network is verified. Using FPGA(Field Programmable Gate array, the neuronal chain network and neural network of HH neuron model and Rabinovich synaptic model are made up of HH neuron model and Rabinovich synaptic model by using FPGA(Field Programmable Gate array (FieldProgrammable Gate Array). According to the simulation description of the neural network, Using QUARTUS 鈪，

本文編號：1531291