本文關(guān)鍵詞： 膜片鉗技術(shù) 反卷積 系統(tǒng)辨識(shí) 白噪聲 互相關(guān)技術(shù) 子空間法 自動(dòng)補(bǔ)償　出處：《華中科技大學(xué)》2012年博士論文　論文類型：學(xué)位論文

【摘要】：膜片鉗技術(shù)是細(xì)胞離子通道記錄方法的金標(biāo)準(zhǔn)。20年來，，神經(jīng)科學(xué)的發(fā)展體現(xiàn)出與信息技術(shù)（計(jì)算機(jī)）越發(fā)緊密結(jié)合的趨勢(shì)，而膜片鉗技術(shù)中計(jì)算機(jī)的參與度相對(duì)較低。膜片鉗技術(shù)中的信號(hào)處理主要使用模擬電路中的概念，如濾波、檢波、補(bǔ)償?shù)�。為了適應(yīng)神經(jīng)科學(xué)發(fā)展的總體趨勢(shì)，本文致力于以膜片鉗系統(tǒng)的數(shù)學(xué)模型為基礎(chǔ)，為膜片鉗的信號(hào)處理引入正、逆問題的概念，以便經(jīng)典的膜片鉗技術(shù)從現(xiàn)代信息技術(shù)的豐碩成果中獲益。 本文的工作主要有兩點(diǎn)：1）討論膜片鉗放大器、電極、細(xì)胞構(gòu)成的統(tǒng)一電系統(tǒng)的數(shù)學(xué)模型，并總結(jié)膜片鉗電路設(shè)計(jì)及信號(hào)處理中的正問題和逆問題；2）對(duì)于膜片鉗系統(tǒng)中的逆問題用反卷積進(jìn)行描述，并提出采用白噪聲驅(qū)動(dòng)的時(shí)間序列分析及基于子空間的系統(tǒng)辨識(shí)法對(duì)反卷積問題進(jìn)行求解。 用正、逆問題的概念對(duì)膜片鉗技術(shù)中信號(hào)處理問題進(jìn)行分類，可使信號(hào)之間的關(guān)系更為清晰，使問題的描述更系統(tǒng)，使問題的求解有堅(jiān)實(shí)的理論基礎(chǔ)和可借鑒的方法。 根據(jù)正、逆問題的基本概念，細(xì)胞通道電流記錄本質(zhì)上是個(gè)逆問題（信號(hào)還原）。若細(xì)胞以單室模型建模，通道電流與膜電容被動(dòng)響應(yīng)電流相并聯(lián)。于是，通道電流的求解可看作加法問題的逆問題，即減法問題。待減去的膜電容電流，其大小與膜電容參數(shù)有關(guān)，從測(cè)量數(shù)據(jù)獲得膜電容參數(shù)信息是個(gè)逆問題（系統(tǒng)辨識(shí)）。 線性時(shí)不變系統(tǒng)中的逆問題就是反卷積。反卷積的求解以數(shù)學(xué)模型為基礎(chǔ)。本文討論了膜片鉗放大器和細(xì)胞構(gòu)成的電系統(tǒng)的若干種數(shù)學(xué)模型，如微分方程模型、傳遞函數(shù)模型、卷積模型和狀態(tài)空間方程模型，以及它們之間的關(guān)系。文章從信號(hào)與系統(tǒng)的角度，討論了膜片鉗實(shí)驗(yàn)中的幾個(gè)典型的反卷積問題，包括信號(hào)復(fù)原和系統(tǒng)辨識(shí)。 受基于ARMA模型的時(shí)間序列分析的啟發(fā)，可用白噪聲激勵(lì)膜片鉗系統(tǒng)，通過互相關(guān)技術(shù)求解系統(tǒng)的時(shí)域特性，即沖激響應(yīng)（本文中稱為卷積核），并開發(fā)了基于卷積核的非迭代快電容自動(dòng)補(bǔ)償算法——K-method；為了確定膜片鉗探頭反饋電阻的雜散電容，可采用子空間法對(duì)其進(jìn)行系統(tǒng)辨識(shí)，并以此為基礎(chǔ)開發(fā)了高值串聯(lián)電阻估計(jì)方法和軟件高頻補(bǔ)償方法——SHB。本文詳細(xì)介紹了這些具體應(yīng)用的原理和實(shí)現(xiàn)步驟。相比原迭代方法，K-method具有簡(jiǎn)單、準(zhǔn)確、抗飽和的優(yōu)點(diǎn)；SHB有利于減小膜片鉗體積、提高膜片鉗的集成度。 本文工作不但為膜片鉗技術(shù)結(jié)合現(xiàn)代信號(hào)處理技術(shù)打下了理論基礎(chǔ)，還提供了兩個(gè)具體范例展示了膜片鉗技術(shù)的發(fā)展方向。
[Abstract]:Patch clamp technology is the gold standard of cell ion channel recording. In the past 20 years, the development of neuroscience has shown a trend of closer integration with information technology (computer). The signal processing in patch clamp technology mainly uses the concept of analog circuit, such as filtering, detection, compensation, etc. In order to adapt to the general trend of neuroscience development. Based on the mathematical model of patch clamp system, this paper introduces the concepts of forward and inverse problems for the signal processing of patch clamp, so that the classical patch clamp technology can benefit from the fruitful results of modern information technology. In this paper, there are two points: 1) the mathematical model of the unified electrical system composed of patch clamp amplifier, electrode and cell is discussed, and the forward and inverse problems in the design of patch clamp circuit and signal processing are summarized. 2) the inverse problem in patch clamp system is described by deconvolution, and the white noise-driven time series analysis and system identification method based on subspace are proposed to solve the deconvolution problem. Using the concepts of positive and inverse problems to classify the signal processing problems in patch clamp technology can make the relationship between signals clearer and the description of problems more systematic. So that the solution of the problem has a solid theoretical basis and can be used for reference. According to the basic concepts of forward and inverse problems, the recording of cell channel current is essentially an inverse problem (signal reduction). If the cell is modeled by a single cell model, the channel current is parallel with the passive response current of membrane capacitance. The solution of channel current can be regarded as the inverse problem of the addition problem, that is, the subtraction problem. The magnitude of the membrane capacitance current to be subtracted is related to the parameters of the membrane capacitance. It is an inverse problem to obtain membrane capacitance parameter information from measurement data (system identification). The inverse problem in linear time-invariant system is deconvolution. The solution of deconvolution is based on mathematical model. In this paper, some mathematical models of electric system composed of patch clamp amplifier and cell are discussed. For example, differential equation model, transfer function model, convolution model and state space equation model, and their relations. Several typical deconvolution problems in patch clamp experiments, including signal recovery and system identification, are discussed. Inspired by the time series analysis based on ARMA model, white noise excited patch clamp system can be used to solve the time-domain characteristics of the system by cross-correlation technique, that is, impulse response (in this paper called convolution kernel). A non-iterative fast capacitor compensation algorithm based on convolution kernels is developed. In order to determine the stray capacitance of the feedback resistance of the patch clamp probe, the subspace method can be used to identify the system. On this basis, the high value series resistance estimation method and the software high frequency compensation method, SHB, are developed. The principle and implementation steps of these specific applications are introduced in detail, compared with the original iterative method. K-method has the advantages of simplicity, accuracy and anti-saturation. SHB can reduce the size of patch clamp and improve the integration of patch clamp. This work not only lays a theoretical foundation for patch clamp technology combined with modern signal processing technology, but also provides two concrete examples to show the development direction of patch clamp technology.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2012
【分類號(hào)】：R329

【共引文獻(xiàn)】

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1 李e鹲

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