發(fā)布時(shí)間：2019-03-13 13:02

【摘要】：生物嗅覺系統(tǒng)對氣味具有較高的辨識度,在氣味識別速度、氣味識別精度上遠(yuǎn)遠(yuǎn)超過目前基于化學(xué)傳感器陣列構(gòu)建的電子鼻系統(tǒng)。但如何將生物嗅覺應(yīng)用于實(shí)際的氣味檢測中,仍然存在著很大的挑戰(zhàn)。氣味分子在動(dòng)物鼻腔中與嗅覺感受細(xì)胞作用后,化學(xué)信息轉(zhuǎn)變成生物電信號傳導(dǎo)至嗅覺信息處理的中轉(zhuǎn)站—嗅球。通過嗅球?qū)馕缎畔⒌奶幚碚?嗅覺信息進(jìn)一步傳遞給大腦皮層中嗅覺相關(guān)的皮層,實(shí)現(xiàn)氣味認(rèn)知。其中,嗅球被認(rèn)為是嗅覺初級中樞,對嗅覺形成起到了關(guān)鍵的作用。理解嗅球的氣味認(rèn)知機(jī)制,在嗅覺信息處理和實(shí)際氣味檢測中都具有重要意義。 目前,對于氣味刺激產(chǎn)生的嗅球細(xì)胞響應(yīng)記錄主要采用膜片鉗技術(shù)和光學(xué)成像技術(shù)。膜片鉗技術(shù)不能實(shí)現(xiàn)多點(diǎn)同步測量,缺乏大量神經(jīng)細(xì)胞協(xié)同作用的信息,而光學(xué)成像技術(shù)背景噪聲大,成像精度仍較難突破。因此,采用植入式微電極陣列傳感器對嗅球電生理信號進(jìn)行長時(shí)程、多點(diǎn)同步的監(jiān)測與分析,有利于理解嗅球細(xì)胞群的信息處理,實(shí)現(xiàn)生物電子鼻的設(shè)計(jì)。本文對嗅覺系統(tǒng)多個(gè)層次的細(xì)胞進(jìn)行了基于電生理數(shù)據(jù)的細(xì)胞建模與仿真,設(shè)計(jì)了基于植入式傳感器的在體嗅球僧帽細(xì)胞氣味刺激響應(yīng)記錄與分析平臺,分析了嗅球僧帽細(xì)胞群氣味響應(yīng)模式。結(jié)合生物學(xué)和工程學(xué)手段,構(gòu)建新型的生物電子鼻系統(tǒng)實(shí)現(xiàn)氣味識別。 本論文的主要內(nèi)容和貢獻(xiàn)如下： 1.對嗅覺系統(tǒng)多個(gè)層次的細(xì)胞進(jìn)行建模仿真。利用電生理信號建立了基于電壓門控通道的嗅覺感受細(xì)胞電生理模型,并仿真了嗅覺感受細(xì)胞受到刺激時(shí)引起細(xì)胞動(dòng)作電位的發(fā)放。仿真了僧帽細(xì)胞電生理模型。探索了嗅覺系統(tǒng)模型對嗅覺生理現(xiàn)象的仿真,并結(jié)合生理實(shí)驗(yàn)測得的數(shù)據(jù)對模型參數(shù)進(jìn)行了優(yōu)化。 2.設(shè)計(jì)了麻醉大鼠嗅覺氣味響應(yīng)研究系統(tǒng)。該系統(tǒng)包括氣味刺激裝置、呼吸信號記錄裝置、電生理信號記錄裝置、記錄電極的制備平臺和數(shù)據(jù)分析平臺。詳細(xì)描述了大鼠的手術(shù)流程與電極植入過程,并對嗅球切片進(jìn)行染色,驗(yàn)證電極植入位置。 3.提出了短時(shí)程氣味刺激下大鼠的快速氣味感知,實(shí)現(xiàn)了時(shí)間依賴性的氣味分類。采用細(xì)胞群脈沖發(fā)放矩陣模式分析法,在特征空間中繪制了僧帽細(xì)胞群對氣味刺激的空間響應(yīng)曲線,表明麻醉大鼠在短時(shí)程氣味刺激后的第一個(gè)呼吸周期內(nèi)就完成了對氣味的感知。選取細(xì)胞發(fā)放相似時(shí)間段內(nèi)同步記錄的僧帽細(xì)胞群脈沖發(fā)放特征,用主成分分析方法實(shí)現(xiàn)了對五種氣味的分類。 4.提出基于僧帽細(xì)胞層場電位信號的生物電子鼻設(shè)計(jì)。低頻場電位信號穩(wěn)定且容易獲取,在不同氣味刺激下功率譜能量分布存在差異。使用K最鄰近分類算法對多窗法譜估計(jì)得到的四種氣味刺激時(shí)頻圖進(jìn)行分類,在信號伽馬頻段(40-120Hz)得到77.4%的分類準(zhǔn)確率。
[Abstract]:The bio-olfactory system has a higher recognition degree for odour. The recognition speed and precision of the smell recognition system are much higher than those of the electronic nose system based on the chemical sensor array. However, there is still a great challenge how to apply bio-olfactory to actual odour detection. After the odor molecules interact with olfactory receptive cells in the nasal cavity of animals, the chemical information is transformed into the olfactory bulb, which is transmitted to the olfactory information processing station by bioelectrical signals. Through the integration of olfactory bulb to odor information, olfactory information is further transmitted to the olfactory-related cortex in the cerebral cortex to realize odour cognition. Among them, olfactory bulb is considered as the primary center of olfaction and plays a key role in olfactory formation. Understanding the odor cognition mechanism of olfactory bulb is of great significance in olfactory information processing and actual odour detection. At present, patch clamp technique and optical imaging technique are used to record the response of olfactory bulb cells induced by odour stimulation. Patch clamp technique can not achieve multi-point simultaneous measurement and lacks a lot of information about the synergetic action of nerve cells. However, the background noise of optical imaging technology is large, and the imaging accuracy is still difficult to break through. Therefore, using embedded microelectrode array sensor to monitor and analyze the electrophysiological signals of olfactory bulb for a long time is helpful to understand the information processing of olfactory bulb cell group and realize the design of bio-electronic nose. In this paper, several layers of cells in olfactory system were modeled and simulated based on electrophysiological data, and a recording and analysis platform of odour stimulation response of in vivo olfactory bulb monks was designed based on implanted sensor. The odour response pattern of olfactory bulb monk-cap cell group was analyzed. Combined with biological and engineering methods, a new type of bio-electronic nose system was constructed to realize odor recognition. The main contents and contributions of this paper are as follows: 1. The multiple layers of cells in olfactory system are modeled and simulated. The electrophysiological model of olfactory sensory cells based on voltage-gated channels was established by using electrophysiological signals, and the action potentials of olfactory sensory cells stimulated by stimulation were simulated. The electrophysiological model of monk-cap cells was simulated. This paper explores the simulation of olfactory physiological phenomena by olfactory system model, and optimizes the parameters of the model by combining the measured data of physiological experiments. 2. A study system of olfactory odor response in anesthetized rats was designed. The system comprises an odour stimulating device, a respiratory signal recording device, an electrophysiological signal recording device, a recording electrode preparation platform and a data analysis platform. The procedure of operation and electrode implantation were described in detail. The olfactory bulb sections were stained to verify the location of electrode implantation. 3. Fast odour sensing of rats under short-term odour stimulation was proposed, and time-dependent odour classification was realized. The spatial response curve of monkat cell group to odour stimulation was plotted in the characteristic space by means of the matrix pattern analysis of cell population pulse emission. These results suggest that anaesthetized rats complete the perception of odour during the first respiratory cycle after short-term odour stimulation. According to the pulse emission characteristics of monkat cell group recorded synchronously in the similar period of cell release, the classification of five odors was carried out by principal component analysis (PCA). 4. A design of bio-electronic nose based on the field potential signal of monk-cap cell layer is proposed. The low frequency field potential signal is stable and easy to obtain, and the energy distribution of power spectrum is different under different odour stimuli. The K-nearest neighbor classification algorithm is used to classify the four kinds of odor stimulation time-frequency patterns estimated by multi-window spectral estimation. The classification accuracy is 77.4% in the gamma band (40-120Hz) of the signal.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：R339.12;TP212.3

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 李緒,李光,汪樂,郭宏記,Freeman Walter J.;嗅覺混沌神經(jīng)網(wǎng)絡(luò)的研究和應(yīng)用[J];傳感技術(shù)學(xué)報(bào);2004年02期

2 ;Modeling and simulation of ion channels and action potentials in taste receptor cells[J];Science in China(Series C:Life Sciences);2009年11期

3 張寧,賈自艷,史忠植;使用KNN算法的文本分類[J];計(jì)算機(jī)工程;2005年08期

4 楊如乃;胡志忠;盧健;;生物嗅覺神經(jīng)系統(tǒng)模型的模擬與分析[J];生物醫(yī)學(xué)工程研究;2006年03期

5 武鵬鵬;趙剛;鄒明;;基于多窗譜估計(jì)的改進(jìn)譜減法[J];現(xiàn)代電子技術(shù);2008年12期

相關(guān)博士學(xué)位論文 前2條

1 劉清君;神經(jīng)芯片及其在生物嗅覺傳感機(jī)理中的研究[D];浙江大學(xué);2006年

2 陳培華;味覺細(xì)胞模型及仿生味覺細(xì)胞網(wǎng)絡(luò)傳感器的研究[D];浙江大學(xué);2010年

相關(guān)碩士學(xué)位論文 前4條

1 李緒;嗅覺神經(jīng)系統(tǒng)網(wǎng)絡(luò)模型的研究和應(yīng)用[D];浙江大學(xué);2004年

2 汪樂;嗅覺系統(tǒng)神經(jīng)模型的研究與應(yīng)用[D];浙江大學(xué);2005年

3 楊新令;嗅覺系統(tǒng)神經(jīng)網(wǎng)絡(luò)模型研究及其在人工嗅覺與虹膜識別中的應(yīng)用[D];浙江大學(xué);2006年

4 楊如乃;嗅覺系統(tǒng)模型及嗅覺腦電信號提取方法的研究[D];南京航空航天大學(xué);2007年

，

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