本文關(guān)鍵詞： 目標(biāo)導(dǎo)向抉擇行為 gamma子帶 同步似然分析 拓?fù)涮匦?LOO-kNN解碼算法　出處：《鄭州大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：腦是自然界中最復(fù)雜的網(wǎng)絡(luò),腦中數(shù)以億計的神經(jīng)元之間不同的連接模式不僅編碼了動物的行為,而且控制著軀體的功能輸出。因此,與特定行為和功能相對應(yīng)的神經(jīng)元網(wǎng)絡(luò)信息處理機(jī)制的解析,目前已成為神經(jīng)科學(xué)和控制科學(xué)等交叉領(lǐng)域新的研究熱點,有助于深化對大腦特定功能和機(jī)制的理解。本文針對動物目標(biāo)導(dǎo)向抉擇行為的神經(jīng)信息解碼問題,以鴿子為模式動物,首先通過植入式微電極陣列記錄了鴿子弓狀皮質(zhì)尾外側(cè)區(qū)神經(jīng)信號,根據(jù)轉(zhuǎn)向過程中局部場電位(local field potential,LFP)不同頻帶的時頻特性變化,確定了轉(zhuǎn)向行為的編碼頻段;然后利用同步似然算法構(gòu)建了gamma子帶功能網(wǎng)絡(luò),并分析了網(wǎng)絡(luò)的拓?fù)涮匦?確定了轉(zhuǎn)向行為的解碼時間窗;最后提取了gamma子帶功能網(wǎng)絡(luò)特征,并利用聯(lián)合留一法(leave one out,LOO)和k近鄰(k-nearest neightor,kNN)的神經(jīng)信息解碼算法解碼了鴿子的轉(zhuǎn)向行為。本文已完成的工作和取得的研究成果概括如下:1)利用多窗口重疊功率譜估計和小波變換技術(shù),從時域、頻域、時頻域三個角度對鴿子轉(zhuǎn)向過程中的LFP信號進(jìn)行了特性分析,以確定與鴿子轉(zhuǎn)向行為相關(guān)的LFP信號特征頻帶。結(jié)果發(fā)現(xiàn),與等待區(qū)相比,轉(zhuǎn)向區(qū)的gamma子帶(40~60 Hz)能量顯著增加,而其它子帶并沒有明顯變化,這表明LFP信號gamma子帶編碼了鴿子的轉(zhuǎn)向行為。2)利用同步似然分析算法,構(gòu)建了LFP信號gamma子帶功能網(wǎng)絡(luò),度量并分析了網(wǎng)絡(luò)的拓?fù)涮匦�。對比分析了等待區(qū)與轉(zhuǎn)向區(qū)、不同方向以及轉(zhuǎn)向前后gamma子帶功能網(wǎng)絡(luò)的拓?fù)涮匦?發(fā)現(xiàn)轉(zhuǎn)向區(qū)的網(wǎng)絡(luò)聚類系數(shù)和全局效率顯著高于等待區(qū),而且轉(zhuǎn)向區(qū)不同方向的網(wǎng)絡(luò)特性之間的差異也比較明顯。進(jìn)一步的研究發(fā)現(xiàn)轉(zhuǎn)向后聚類系數(shù)和全局效率顯著高于轉(zhuǎn)向前,這表明轉(zhuǎn)向后gamma子帶功能網(wǎng)絡(luò)包含了鴿子的轉(zhuǎn)向行為信息。3)利用網(wǎng)絡(luò)的連接強(qiáng)度值和主成分降維技術(shù),提取了gamma子帶功能網(wǎng)絡(luò)特征,并利用LOO-kNN解碼算法解碼了鴿子的轉(zhuǎn)向行為,對比分析了網(wǎng)絡(luò)特征和能量特征的解碼正確率。結(jié)果表明,網(wǎng)絡(luò)特征的解碼正確率(0.74±0.08)顯著高于能量特征的解碼正確率(0.61±0.12)。同時,通過對轉(zhuǎn)向過程中g(shù)amma子帶網(wǎng)絡(luò)特征解碼正確率的動態(tài)特性分析發(fā)現(xiàn),不同鴿子的解碼正確率峰值大都出現(xiàn)在轉(zhuǎn)向后,這表明在鴿子轉(zhuǎn)向過程中目標(biāo)可能起到了關(guān)鍵作用。
[Abstract]:The brain is the most complex network in nature. The different patterns of connections between hundreds of millions of neurons in the brain not only encode the behavior of animals, but also control the functional output of the body. The analysis of neural network information processing mechanism corresponding to specific behavior and function has become a new research hotspot in the intersecting fields of neuroscience and control science. It is helpful to deepen the understanding of the specific functions and mechanisms of the brain. In this paper, the pigeon is used as a model animal to solve the problem of neural information decoding for the goal-oriented behavior of animals. Firstly, the neural signals in the lateral caudal region of the arcuate cortex of pigeons were recorded by implanted microelectrode arrays. According to the time-frequency characteristics of different frequency bands of local field potential (LFP), the coded frequency band of the steering behavior was determined. Then the gamma sub-band functional network is constructed by using synchronous likelihood algorithm, and the topological characteristics of the network are analyzed, and the decoding time window of the steering behavior is determined. Finally, the feature of the gamma sub-band functional network is extracted. And the neural information decoding algorithm based on joint leave one outloo) and k-nearest neighbor KNN) is used to decode the dove's turn behavior. The work done and the research results obtained in this paper are summarized as follows: 1) using multi-window overlapping power spectrum estimation. And wavelet transform technology, The characteristics of LFP signal in pigeon steering are analyzed from three aspects: time domain, frequency domain and time frequency domain, in order to determine the characteristic frequency band of LFP signal related to dove steering behavior. The energy of the gamma subband 4060 Hz in the steering region increased significantly, but the other subbands did not change significantly. This indicates that the LFP signal gamma subband encodes the dove turning behavior. 2) using the synchronous likelihood analysis algorithm, the LFP signal gamma subband functional network is constructed. The topological characteristics of the network are measured and analyzed, and the topological characteristics of the waiting area and the steering area, the different directions and the gamma subband function network before and after steering are compared. It is found that the network clustering coefficient and global efficiency of the steering region are significantly higher than that of the waiting area. Moreover, the difference between the network characteristics of different directions in the steering region is obvious. Further studies show that the post-steering clustering coefficient and global efficiency are significantly higher than those before turning. This indicates that the post-steering gamma subband functional network contains dove turning behavior information. 3) the feature of gamma subband functional network is extracted by using the connection strength value and principal component dimensionality reduction technique of the network, and the pigeon's turn behavior is decoded by LOO-kNN decoding algorithm. The results show that the decoding accuracy of network features is 0.74 鹵0.08, which is significantly higher than that of energy features (0.61 鹵0.12). By analyzing the dynamic characteristics of the decoding accuracy rate of gamma subband network in the process of steering, it is found that the peak decoding accuracy rate of different pigeons occurs after the turn, which indicates that the target may play a key role in the process of pigeon steering.
【學(xué)位授予單位】：鄭州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：O157.5

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