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    National Tsing Hua University Institutional Repository > 生命科學院  > 系統神經科學研究所 > 博碩士論文 >  ASNeuPI - 以型態骨架辨認神經元極性之演算法

    Please use this identifier to cite or link to this item: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/77217

    Title: ASNeuPI - 以型態骨架辨認神經元極性之演算法
    Authors: 李怡萱
    Lo, Chung-Chuan
    Lee, Yi-Hsuan
    教師: 羅中泉
    Date: 2012
    Keywords: 神經網路
    neural networks
    neural imaging
    neuron polarity
    Abstract: 訊號傳遞的方向對神經網路是重要的。因此,在分析神經網路時,方向性是需納入的資訊之一,而訊號的傳遞方向可藉由判斷神經元的極性獲得。目前的辨識方式是以生化實驗為主,但若要處理大尺度的神經網路,實驗可能過度耗時。

    為了解決這個問題,我們提出一個從型態骨架辨認神經元極性的演算法 (ASNeuPI)。在ASNeuPI中,我們首先將單一神經元從型態上拆成幾個子結構,並計算每個子結構的形態特徵。接著利用機器學習(Machine Learning)的方法找出提供最高正確率的特徵組合作為極性分類使用。

    我們利用有連結果蠅protocerebral bridge(PCB)或medulla(MED)腦區的神經細胞來測試這套方法,資料來源為國立清華大學腦科學研究中心。平均起來,一個神經細胞中有85%以上的端點(terminal point)其極性能被正確的辨認。在所有測試的形態特徵中,距細胞本體(soma)的遠近對判斷神經極性最為有用。我們的結果顯示ASNeuPI具可行性,並有發展成一套利用型態骨架辨認神經元極性的半自動化流程之潛力。
    The direction of signal transmission is crucial for neural networks. Therefore, the direction of signal flow, which could be provided by identifying neuronal polarity, should be included when we analyze neural networks. However, neuronal polarity is usually identified by biochemical method which is time consuming and might not be an appropriate way to deal with large-scale neural networks.

    To solve this problem, we proposed the algorithm for skeleton-based neuronal polarity identification (ASNeuPI). In ASNeuPI, we first morphologically divide a neuron into several substructures, and then extract their morphological features. By applying methods in machine learning, we got an optimal axis providing highest accuracy to serve as the discriminant feature for polarity classification.

    We tested this method on neurons innervating protocerebral bridge (PCB) or medulla (MED) in Drosophila. The data were obtained from Brain Research Center, National Tsing Hua University. On average, the polarity of above 85% terminal points in a neuron could be correctly identified. Among all the morphological features tested, the distance to soma is the most useful one. Our results show that ASNeuPI is workable and has the potential to provide a computer-based semi-automatic procedure to predict neuronal polarity from skeleton data.
    URI: http://nthur.lib.nthu.edu.tw/dspace/handle/987654321/77217
    Appears in Collections:[系統神經科學研究所] 博碩士論文

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