课程大纲
COURSE SYLLABUS
1.
课程代码/名称
Course Code/Title
人脑智能和机器智能/Brain Intelligence and Machine Learning
2.
课程性质
Compulsory/Elective
专业选修课
3.
课程学分/学时
Course Credit/Hours
3 学分/48 学时 3Credits/48Hours
4.
授课语言
Teaching Language
中文&英语/Chinese & English
5.
授课教师
Instructor(s)
刘泉影/Quanying Liu
6.
是否面向本科生开放
Open to undergraduates
or not
否
7.
先修要求
Pre-requisites
（ 如 面 向 本 科 生 开 放 ， 请 注 明 区 分 内 容 。 If the course is open to
undergraduates, please indicate the difference.）
无/NA
8.
教学目标
Course Objectives
（如 面 向 本 科 生开 放 ， 请 注 明 区 分 内容 。 If the course is open to undergraduates, please indicate the
difference.）
课程将从以下 4 个方向展开讨论学习：神经信号处理、神经认知计算、机器智能算法以及人脑智能和机器智能的交
互。学习主流神经信号处理技术（EEG、fMRI、MEG 等）和机器学习的常用算法；了解神经认知计算、机器智能和人脑
智能的交互等前沿动态领域的研究现状；实践科研项目设计与实施；具备对人脑智能和机器智能相融合的深入研究能
力。
9.
教学方法
Teaching Methods
（如 面向本科生 开放， 请注明区分内容。 If the course is open to undergraduates, please indicate the
difference.）
课堂讲授+论文讨论+项目设计及答辩
10.
教学内容
Course Contents
（如面向本科生开放，请注明区分内容。 If the course is open to undergraduates, please indicate the
difference.）
Section 1
Introduction.
Section 2
EEG Signal Processing and Analysis
Section 3
fMRI Signal Processing and Analysis
Section 4
MEG Signal Processing and Analysis
Section 5
Statistical Methods for Data Analysis
Section 6
Machine Learning Techniques for Data Analysis
Section 7
Human intelligence: Human Observational Learning
Section 8
Human intelligence: Causal Inference in the Multisensory Brain
Section 9
Human intelligence: Human Multisensory Perception
Section 10
Human Intelligence: Human Decision-making
Section 11
Machine Intelligence: Cognitive AI with Human-like Commonsense
Section 12
Machine Intelligence: Building Machines That Learn and Think Like People
Section 13
Machine Intelligence: Lifelong Learning in Artificial Neural Networks
Section 14
Brain-Machine Intelligence: Cortical Activity Machine Translation
Section 15
Brain-Machine Intelligence: Human-machine Interaction
Section 16
Brain-Machine Intelligence: Neuroscience-inspired AI
11.
课程考核
Course Assessment
（
○
1
考核形式 Form of examination；
○
2
.分数构成 grading policy；
○
3
如面向本科生开放，请注明区分内容。
If the course is open to undergraduates, please indicate the difference.）
课程讨论 30%
论文讨论 40%
项目设计 30%
12.
教材及其它参考资料
Textbook and Supplementary Readings
教材：
[1] Computational Modelling of Cognition and Behavior, by Simon Farrell and Stephan Lewandowsky
[2] Neuroscience: Exploring the Brain, by Mark F. Bear, Bear, Barry W. Connors, and Michael A. Paradiso
参考资料：
[1] EEGLAB Tutorial. https://sccn.ucsd.edu/wiki/EEGLAB#The_EEGLAB_Tutorial_Outline
[2] Statistical Parametric Mapping Tutorial: https://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf
[3] Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
[4] Hu, L., & Zhang, Z. (2019). EEG Signal Processing and Feature Extraction. Springer Singapore.
[5] Cao, Y., Summerfield, C., Park, H., Giordano, B. L., & Kayser, C. (2019). Causal Inference in the
Multisensory Brain. Neuron, 102(5), 1076-1087.e8.
[6] Charpentier, C. J., Iigaya, K., & O’Doherty, J. P. (2020). A Neuro-computational Account of Arbitration
between Choice Imitation and Goal Emulation during Human Observational Learning. Neuron.
[7] Rohe, T., Ehlis, A.-C. & Noppeney, U. (2019). The neural dynamics of hierarchical Bayesian causal
inference in multisensory perception. Nature Communication.
[8] Ji-An Li, Daoyi Dong, Zhengde Wei, Ying Liu, Yu Pan, Franco Nori, and Xiaochu Zhang. (2020).
Quantum reinforcement learning during human decision-making. Nature Human Behaviour
[9] Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn
and think like people. Behavioral and Brain Sciences, 40.
[10] Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi
Zhang, Siyuan Qi, Yingnian Wu, Joshua B. Tenenbaum, Song-Chun Zhu.(2020). Dark, Beyond Deep: A
Paradigm Shift to Cognitive AI with Human-like Commonsense. Engineering
[11] Makin, J. G., Moses, D. A., & Chang, E. F. (2020). Machine translation of cortical activity to text with an
encoder–decoder framework. Nature Neuroscience, 23(4), 575–582.
[12] G. Gary Anthes. (2019). Lifelong learning in artificial neural networks. Commun. ACM 62, 13– 15.
[13] Nalepka, P., Lamb., M., Kallen, R. W., Shockley, K., Chemero, A., Saltzman, E., & Richardson, M. J.
(2019). Human social motor solutions for human–machine interaction in dynamical task contexts. PNAS, 116
(4), 1437-1446.
[14] Hassabis D, Kumaran D, Summerfield C, Botvinick M.(2017) Neuroscience-Inspired artificial
intelligence. Neuron 95, 245– 258.
