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系統識別號 U0026-2508202016344500
論文名稱(中文) 用於果蠅神經元編碼之類樂高積木的演算法實作與早期評估
論文名稱(英文) Implementation and early evaluation of an algorithm for encoding Drosophila neurons like lego bricks
校院名稱 成功大學
系所名稱(中) 機械工程學系
系所名稱(英) Department of Mechanical Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 賴柏毓
研究生(英文) Po-Yu Lai
學號 N16064129
學位類別 碩士
語文別 英文
論文頁數 49頁
口試委員 指導教授-吳馬丁
口試委員-盧達生
口試委員-陳南佑
中文關鍵字 神經相似度  連接組  集群分析  序列對比  資料轉換  模組化 
英文關鍵字 neural similarity  connectome  clustering analysis  sequence alignment  data conversion  modularity 
學科別分類
中文摘要 背景:
為了了解我們的大腦運作方式,科學家們試圖繪製出所有神經元之間的聯繫,即所謂的連接組。由於成像技術的局限性,即使是果蠅大腦也無法一次成像所有的神經元。因此,需要透過許多局部圖像將整體連接體拼湊在一起,比較和識別單個神經元是不可少的。果蠅神經元圖像的比較是一項艱鉅的工作。目前最好的方法-神經局部比對搜索工具(NBLAST)繁重的前處理會影響結果精準與否。更確切地說,NBLAST要求將每個神經圖像變形為標準模型大小來進行對比。
目標:
本論文主旨在實現和評估一種用於將神經圖像自動處理為一系列標準單位以便於比較神經元的演算法。此演算法的基本概念是由台灣國家高速網路與計算中心(NCHC)的陳南佑博士所發明的。
方法:
我們的演算法從提取神經元每個分支點的幾個無因次特徵開始。通過對這些分支點的特徵進行集群分析,我們將有限的標籤重新分配給每個分支點,重此以後定義為“神經樂高”。將神經樂高的樹狀結構轉換為序列後,可以通過序列比對來比較兩個神經元。
結果與結論:
於此論文中以C/C++程式實現了該算法,並針對過程的一部分探索了幾種替代解決方案。我們發現了用於圖論的模組化方法和分層聚類在一些情況下可產生相似結果,也發現在模組化中使用多分辨率可以提供一種更好的合併群集的方法。我們發現從原始圖像中自動提取的神經元包含雜訊,這使得很難確定聚類結果是否良好,因此需要進一步改進。我們注意到在將帶有標籤的樹狀資料用我們的方法轉換為序列的過程中遺失了些資訊。 沒有足夠的數據來構建統計上有效的序列比對評分系統。對於上述的問題中,有許多替代方法可以解決上述問題。因此,對神經樂高的算法是否有用下結論還為時過早。我們對結果進行一些解釋,並列出一些未來的建議。
英文摘要 Background:
As a part of the scientific endeavor to understand how our brain works, scientists are trying to map out the connections between all neurons—the so called connectome. Due to limitations in the imaging techniques, it is not yet possible to image all neurons at once even for the fruit fly brain. Therefore the connectome need to be pieced together based on many images and it is essential to compare and recognize individual neurons. Comparison of images of Drosophila melanogaster neurons is a laborious job. The state of the art method—Neural Basic Local Alignment Search Tool (NBLAST)—depend on heavy preprocessing before the comparison. More precisely, NBLAST requires warping of each neural image into a standard model size.
Aim:
This thesis aims to implement and evaluate an algorithm for automated processing of neural images into a sequence of standard elements for easy comparison of neurons. The basic algorithm was invented by Dr. Nan ­Yow Chen at the National Center for High-Performance Computing (NCHC) in Taiwan.
Method:
Our algorithm starts with the extraction of several dimensionless features of each branch point of a neuron. By clustering the branch points based on these features, we assign a label from a finite vocabulary to each branch point, henceforth called ``Neural Lego'. This enables comparison of two neurons through sequence alignment after the tree of labels is converted into a sequence.
Results and conclusion:
We implemented the algorithm in functional C/C++ code and explored several alternative solutions for parts of the process. We discovered some cases where modularity and hierarchical clustering yields a similar result. We discovered that the use of multiresolution in modularity can provide a better way of merging clusters. We found that the automatically extracted neurons from the raw images contain noise, which make it difficult to determine whether the clustering result is good or not and thus requires further improvement. We showed that information is lost during the conversion of the tree of labels into a sequence. We did not have enough data to construct a statistical sequence alignment scoring system. There are many alternative ways to solve the mentioned problems. Thus, it is still too early to conclude if our Neural Lego based algorithm can work or not. We provide some interpretation of our results and list some future suggestions.
論文目次 Chinese abstract i
Abstract iii
Acknowledgment v
Table of Contents vi
List of Tables viii
List of Figures ix
List of Symbols ix
1 Introduction 1
1.1 Motivation and objective . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Delimitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Introduction to connectome . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Introduction to neuron similarity . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Introduction to cluster analysis . . . . . . . . . . . . . . . . . . . . . . . . 8
1.6 Introduction to modularity . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.7 Introduction to sequence alignment . . . . . . . . . . . . . . . . . . . . . . 12
1.8 Organization of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 Theory and Method 14
2.1 Information extraction and graph transformation . . . . . . . . . . . . . . . 14
2.2 Modularity-community detection . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 Sequence alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.1 Global and local alignment . . . . . . . . . . . . . . . . . . . . . . 22
2.3.2 Scoring matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Results and Discussion 24
3.1 Results and problems in extraction . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Problem with modularity . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 Sequence alignment and conversion . . . . . . . . . . . . . . . . . . . . . 43
4 Conclusion and Future works 44
4.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.2 Future works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
References 46
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