2024 Faculty Courses School of Engineering Department of Information and Communications Engineering Graduate major in Information and Communications Engineering
Computational Brain
- Academic unit or major
- Graduate major in Information and Communications Engineering
- Instructor(s)
- Yasuharu Koike
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-2 Wed
- Class
- -
- Course Code
- ICT.H422
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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 |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
original text will be used.
Reference books, course materials, etc.
Principles of Neural Science, McGraw-Hill Professional
Evaluation methods and criteria
The above target is evaluated by final report 60%, exercises 40%.
Related courses
- ICT.H509 ： Mesurement of Brain Function
Prerequisites
Without any requirements