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아이디

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책소개

Humans can learn under a wide variety of feedback conditions. Reinforcement
learning(RL), where a series of rewarded decisions must be made, is a particularly
important type of learning. Computational and behavioral studies of RL have
focused mainly on Markovian decision processes, where the next state depends on
only the current state and action. Little is known about non-Markovian decision
making, where the next state depends on more than the current state and action.
Learning is non-Markovian, for example, when there is no unique mapping
between actions and feedback. We have produced a model based on spiking
neurons that can handle these non-Markovian conditions by performing policy
gradient descent [1]. Here, we examine the model’s performance and compare it
with human learning and a Bayes optimal reference, which provides an
upper-bound on performance. We find that in all cases, our population of spiking
neurons model well-describes human performance.

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
Human and Machine Learning in Non-Markovian Decision Making

1. Introduction 51
2. Results 53
3. Experiment 2: Intermixed Feedback 55
4. Modeling 56
5. Discussion 58
6. Materials and Methods 59
7. Procedures 60
8. Supporting Information 62
9. References 63

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