本文關(guān)鍵詞： 神經(jīng)元建模 泊松點(diǎn)過(guò)程 通用線性模型 數(shù)值梯度下降　出處：《浙江大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：神經(jīng)元脈沖信號(hào)的建模與預(yù)測(cè)是神經(jīng)科學(xué)領(lǐng)域的重要研究問(wèn)題。通過(guò)神經(jīng)元建模來(lái)分析脈沖信號(hào)的發(fā)放特點(diǎn),有助于研究學(xué)者們更加深刻地理解大腦在執(zhí)行高級(jí)認(rèn)知任務(wù)中的工作方式以及神經(jīng)信息在不同腦區(qū)之間的傳遞方式,從而對(duì)大腦的生理特性有一個(gè)更好的認(rèn)識(shí),乃至建立腦機(jī)融合的神經(jīng)假體。本文通過(guò)對(duì)獼猴PMd腦區(qū)與M1腦區(qū)的神經(jīng)元脈沖信號(hào)構(gòu)建數(shù)理統(tǒng)計(jì)模型,來(lái)定性、定量地分析二者之間的功能聯(lián)系。PMd腦區(qū)與M1腦區(qū)在獼猴的高級(jí)認(rèn)知活動(dòng)中具有重要作用,對(duì)這兩個(gè)腦區(qū)的神經(jīng)元進(jìn)行建模,有助于研究學(xué)者們深入了解兩個(gè)腦區(qū)協(xié)同工作的方式細(xì)節(jié)。脈沖信號(hào)建模存在諸多挑戰(zhàn)。例如,神經(jīng)元脈沖信號(hào)本身包含非常豐富的信號(hào)發(fā)放特性,需要模型具備足夠強(qiáng)的表達(dá)能力來(lái)表征脈沖信號(hào)的多樣性;除此之外,神經(jīng)元所傳送的信息包含在脈沖信號(hào)點(diǎn)過(guò)程序列之中,需要模型能夠針對(duì)脈沖信號(hào)的點(diǎn)過(guò)程特性充分挖掘特征。本文以泊松通用線性模型為基礎(chǔ),針對(duì)這幾個(gè)問(wèn)題提出了若干改進(jìn),全文的貢獻(xiàn)點(diǎn)歸納如下:1.本文借鑒集成學(xué)習(xí)中混合模型的思想,訓(xùn)練若干個(gè)弱表征能力的子模型,并對(duì)其進(jìn)行混合構(gòu)成完整模型,從而增強(qiáng)模型整體的表達(dá)能力;2.本文通過(guò)將泊松通用模型對(duì)應(yīng)的目標(biāo)函數(shù)由最大化似然函數(shù)轉(zhuǎn)化為優(yōu)化Discrete Time Rescaling Kolmogorov Smirnov統(tǒng)計(jì)量,借此增強(qiáng)模型對(duì)神經(jīng)元脈沖信號(hào)點(diǎn)過(guò)程特性的考量;3.本文通過(guò)實(shí)驗(yàn)從不同角度驗(yàn)證所提出的模型的預(yù)測(cè)能力,實(shí)驗(yàn)結(jié)果表明本文模型在擬合優(yōu)度角度能夠保持一個(gè)比較突出的結(jié)果,同時(shí)模型本身維持著一個(gè)較好的生物解釋性。
[Abstract]:The modeling and prediction of neuron pulse signal is an important research problem in the field of neuroscience. It is helpful for researchers to understand more deeply how the brain works in performing advanced cognitive tasks and how neural information is transmitted between different brain regions, so as to have a better understanding of the physiological characteristics of the brain. In this paper, a mathematical statistical model of neural pulse signals in the PMd and M1 brain regions of rhesus monkeys was constructed to determine the nature of the neural prosthesis. Quantitative analysis of the functional relationship between the two areas. PMd and M1 brain regions play an important role in the advanced cognitive activities of rhesus monkeys. The neurons in these two brain regions are modeled. It is helpful for researchers to understand the details of how the two brain regions work together. There are many challenges in the modeling of pulse signal. For example, the neuron pulse signal itself contains very rich signaling characteristics. It is necessary for the model to be strong enough to represent the diversity of pulse signals. In addition, the information transmitted by the neuron is contained in the pulse signal point process sequence, which requires that the model can fully mine the characteristics of the point process characteristics of the pulse signal. This paper is based on Poisson's general linear model. The contributions of this paper are summarized as follows: 1. This paper uses the idea of hybrid model in integrated learning to train several sub-models with weak representation ability. A complete model is constructed by mixing it to enhance the expression ability of the model as a whole. 2. In this paper, the objective function corresponding to Poisson's general model is transformed from maximum likelihood function to optimized Discrete Time Rescaling Kolmogorov. Smirnov statistics. The model is used to evaluate the process characteristics of neuron pulse signal points. 3. The prediction ability of the proposed model is verified by experiments from different angles. The experimental results show that the proposed model can maintain a relatively outstanding result in the goodness of fit angle. At the same time, the model itself maintains a better biological interpretation.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：Q42;TN911.6

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