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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Information and Communications Engineering
- Instructor(s)
- Koike Yasuharu
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Wed1-2(G224)
- Group
- -
- Course number
- ICT.H422
- Credits
- 1
- Academic year
- 2018
- Offered quarter
- 4Q
- Syllabus updated
- 2018/4/10
- Lecture notes updated
- -
- Language used
- Japanese
- Access Index
-
Course description and aims
The brain learns proper actions. In this course, the computational modeling will be introduced.
This course provides the basic knowledge about Brain science, especially Motor control and learning algorithm are lectured.
Student learning outcomes
By completing this course, students will be able to explain the methodology of computational neuroscience for motor control.
Keywords
Motor control, Machine Learning, Brain science
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Each topic will be introduced with lecture note.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Computational neuroscience | Introduction of computational neural science |
| Class 2 | trajectory planning and optimize function | Understand of trajectory planning and optimize function |
| Class 3 | analysis of biological signals | Understand of analysis of biological signals |
| Class 4 | modeling of biological system | Understand of modeling of biological system |
| Class 5 | Learning and control of voluntary movement | Understand of learning and control of voluntary movement |
| Class 6 | decoding of brain signals | Understand of decoding of brain signals |
| Class 7 | Applications of brain science | Understand of Applications of brain science |
| Class 8 | Presentation | Presentation |
Textbook(s)
original text will be used.
Reference books, course materials, etc.
Principles of Neural Science, McGraw-Hill Professional
Assessment criteria and methods
The above target is evaluated by final exam 60%, exercises 40%.
Related courses
- ICT.H509 ： Mesurement of Brain Function
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Without any requirements
