Neural Information Processing /Exercise1

This exercise will conduct state-of-the-art research in computational neuroscience, systems neuroscience based on data analysis, and neuroscience-inspired machine learning to develop problem-solving skills.

1. Understand the basic theory and fundamental theories of computational neuroscience and machine learning, and be able to apply them to simple examples.
2. Understand the computational principles of neurons and how to model them using mathematical formulas and their analytical solutions.
3. Understand the basic concepts and basic techniques of neural networks and be able to create simple simulations.
4. Understand the basic theory and fundamental techniques of machine learning and be able to apply them to simple examples.

Japanese(English accepted)

This exercise will conduct state-of-the-art research in computational neuroscience, systems neuroscience based on data analysis, and neuroscience-inspired machine learning to develop problem-solving skills.

Research progress reports and discussions (at least one seminar per week) 50%.
Preparation of master's thesis and status of preparation 40
Conference presentations and preparation of preliminary drafts and presentation materials 10%.
Of the overall evaluation (out of 100 points) based on the above, 60 points or more will be considered as passing.

I'll direct you as appropriate.

Students should acquire knowledge of elementary college mathematics such as linear algebra, calculus, differential equations, probability theory, and discrete mathematics.

• Tuesdays 12:30-13:20

Non-regionally-oriented course

• Course that cultivates a basic problem-solving skills
• Course that cultivates an ability for utilizing knowledge

Most classes are interactive

• 4.QUALITY EDUCATION
• 9.INDUSTRY, INNOVATION AND INFRASTRUCTURE
• 17.PARTNERSHIPS FOR THE GOALS