本文選題：結(jié)構(gòu)腦網(wǎng)絡(luò) + 標(biāo)記神經(jīng)元比例�。� 參考：《計(jì)算機(jī)工程與設(shè)計(jì)》2017年08期

【摘要】：針對(duì)傳統(tǒng)的結(jié)構(gòu)腦網(wǎng)絡(luò)構(gòu)建以及分析方法缺乏對(duì)網(wǎng)絡(luò)中連接的可信程度的驗(yàn)證,且在現(xiàn)有的研究中對(duì)結(jié)構(gòu)腦網(wǎng)絡(luò)進(jìn)行優(yōu)化的方法存在缺陷,介紹一種基于隨機(jī)分塊模型的算法,對(duì)結(jié)構(gòu)腦網(wǎng)絡(luò)中的連接可靠性進(jìn)行量化評(píng)價(jià),作為基礎(chǔ)對(duì)網(wǎng)絡(luò)結(jié)構(gòu)進(jìn)行優(yōu)化。利用網(wǎng)絡(luò)中節(jié)點(diǎn)間的模塊化結(jié)構(gòu),以及節(jié)點(diǎn)間是否存在連接依賴(lài)于節(jié)點(diǎn)在網(wǎng)絡(luò)中起到的作用這兩個(gè)屬性,將節(jié)點(diǎn)分為模塊,迭代多次,直到節(jié)點(diǎn)發(fā)揮其最大作用為止。通過(guò)與真實(shí)網(wǎng)絡(luò)相比較,驗(yàn)證該方法是否可以應(yīng)用在結(jié)構(gòu)腦網(wǎng)絡(luò)。結(jié)果表明,在不同的網(wǎng)絡(luò)稀疏度情況下,使用該算法優(yōu)化后的網(wǎng)絡(luò)的正確邊的比率,均遠(yuǎn)高于原有的方法說(shuō)明該方法對(duì)結(jié)構(gòu)腦網(wǎng)絡(luò)的優(yōu)化有顯著效果。
[Abstract]:In view of the fact that the traditional methods of structural-brain network construction and analysis lack the verification of the credibility of the connections in the network, and the shortcomings of the existing research methods of optimizing the structural brain network, an algorithm based on random block model is introduced. The connection reliability of structural brain network is evaluated quantitatively, and the network structure is optimized based on it. Using the modularization structure between nodes and whether there is a connection between nodes depending on the role of nodes in the network, the nodes are divided into modules and iterated many times until the nodes play their most important role. Compared with the real network, the proposed method can be applied to the structural brain network. The results show that under different network sparsity, the ratio of the correct edges of the optimized network using this algorithm is much higher than that of the original method, which shows that the method has a significant effect on the optimization of the structural brain network.
【作者單位】： 太原理工大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61373101、61472270、61402318) 教育部高等學(xué)校博士學(xué)科點(diǎn)專(zhuān)項(xiàng)科研基金項(xiàng)目(20131402110006) 太原理工大學(xué)青年基金項(xiàng)目(2012L014、2013T047)
【分類(lèi)號(hào)】：O157.5;R318

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本文編號(hào)：1948498