2022 Computational Brain

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Academic unit or major
Graduate major in Information and Communications Engineering
Koike Yasuharu  Yoshimura Natsue 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
Language used
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.


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

Out-of-Class Study Time (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.


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

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