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머신러닝을 이용한 덴드리트 가시의 분석 및 자동식별을 위한 오픈소스 툴


SMART
 

머신러닝을 이용한 덴드리트 가시의 분석 및 자동식별을 위한 오픈소스 툴

Michael S. Smirnov, Tavita R. Garrett, Ryohei Yasuda 저 | 아진

출간일
2020-07-12
파일형태
PDF
용량
17 M
지원 기기
PC
대출현황
보유1, 대출0, 예약중0
콘텐츠 소개
목차
한줄서평

콘텐츠 소개

Synaptic plasticity, the cellular basis for learning and memory, is mediated by a
complex biochemical network of signaling proteins. These proteins are
compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures
found on neuronal dendrites. The ability to screen a high number of molecular
targets for their effect on dendritic spine structural plasticity will require a
high-throughput imaging system capable of stimulating and monitoring hundreds
of dendritic spines in various conditions. For this purpose, we present a program
capable of automatically identifying dendritic spines in live, fluorescent tissue. Our
software relies on a machine learning approach to minimize any need for
parameter tuning from the user. Custom thresholding and binarization functions
serve to “clean” fluorescent images, and a neural network is trained using
features based on the relative shape of the spine perimeter and its corresponding
dendritic backbone. Our algorithm is rapid, flexible, has over 90% accuracy in
spine detection, and bundled with our user-friendly, open-source, MATLAB-based
software package for spine analysis.

목차

제 1편 : MATLAB 기본편
1. MATLAB 기본사용편 ···················· 003
1.1 MATLAB 시작하기 ·························· 003
명령창(command Window)에서의 입력 005
도움말(Help)의 이용 ······························· 007
1.2 입력 오류의 수정 ····························· 008
계산의 중지 ·············································· 009
MATLAB 종료하기 ································· 009
1.3 연산과 변수의 할당 ·························· 009
연산자 우선순위 ······································· 011
내장함수 ···················································· 012
1.4 데이터의 표현 ··································· 013
1.5 변수의 처리 ······································· 015
변수 이름 ·················································· 015
clear 명령어 ············································· 016
특수변수와 정수 ······································· 017
whos 명령어 ············································ 017
1.6 벡터와 행렬 ······································· 018
벡터 ··························································· 018
행렬 ·························································· 023
스크린 출력과 억제 ································· 024
1.7 랜덤(Random)수와 복소수 ·············· 025
랜덤 수 ····················································· 025
복소수 ······················································· 027
1.8 기호를 이용한 연산 ·························· 028
기호식에서의 치환 ··································· 029
1.9 코드 파일 ·········································· 030
스크립트 코드 파일 ································· 030
코멘트의 추가 ·········································· 032
함수 코드 파일 ···································· 033
사용자 정의함수 ······································ 036
1.10 간단한 그래프의 생성 ····················· 037
ezplot을 이용한 그래프 ·························· 037
plot을 이용한 그래프 ·························· 039
3차원 그래프 ··········································· 042
1.11 MATLAB과 엑셀(Excel)의 접속 043 엑셀 데이터 불러오기 ····························· 043
데이터 가져오기 옵션 ························· 046
스크립트 생성 옵션 ································· 049
함수 생성 옵션 ········································ 049
생성된 데이터를 엑셀파일로 저장하기 ·· 050

제 2편 : 연구논문
An open-source tool for analysis and automatic identification of
dendritic spines using machine learning

1. Introduction 51
2. Image acquisition 52
3. Backbone extraction 53
4. Perimeter feature extraction 56
5. Network training 58
6. Software design 59
7. Conclusion 64
8. References 65

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