Biological and Biomimetic Information Engineering

Course title

F0042000

Middle-level Diploma Policy (mDP)

Program / Major	mDP	Goals	Courses
Fundamental Mechanical Engineering	F	産業界や社会の要請を把握して解決するべき課題を設定し、さまざまな工学分野の知識を関連付けながら設計生産技術を活用することで、立案した構想に従って研究を進め課題を解決することができる。	Sub
Advanced Mechanical Engineering	F	産業界や社会の要請を把握して解決するべき課題を設定し、機械工学の学理を応用して異分野を含む融合分野で革新的な機能を創成することができる。	Sub
Environment and Materials Engineering	B	地球環境や地域社会との調和を見据えて、さまざまな工学分野に関わる問題を解決することができる。	Sub
Chemistry and Biotechnology	B	地球環境や地域社会との調和を見据えて、さまざまな工学分野に関わる問題を解決することができる。	Sub
Electrical Engineering and Robotics	D	電気工学や関連する工学の技術分野を課題に適用し、社会の要求を解決するために応用することができる。	Sub
Advanced Electronic Engineering	E	専門的デザイン課題について解決する能力を身に付けることができる。	Sub
Information and Communications Engineering	B-3	最先端のシステムやネットワークに対応できる高度な専門知識や技能を習得することができる。	Main
Computer Science and Engineering	G	技術的課題に対してさまざまな工学分野の知識を関連付けながら主体的に取り組み、継続的に学修する能力を身に付けることができる。	Sub
Urban Infrastructure and Environment	G	⼟⽊⼯学における現実の問題について、⼯学・専⾨基礎知識を⽤いて理解・解決することができる。	Sub

Purpose of class

The purpose of this course is to learn biomimetic computational systems such as artificial neural networks, reinforcement learning, genetic algorithms, their mathematical foundation, their application to practical problems, and analyses for experimental data of biological information, as acquisition of advanced specialized knowledge and skills that can support cutting-edge systems and networks.

Course description

Living systems adapts their environment flexibly. Functions of information processing in the living system have been modeled in biomimetic computational systems. The biomimetic computational systems effectively solve problems which are difficult to be modeled mathematically or have nonlinearity, with which classical computational methods has difficulty. Some of the biomimetic computational systems have been established in engineering. In this course, students learn elementary techniques of artificial neural network, which is gaining attention as artificial intelligence and deep learning in recent years, reinforcement learning, genetic algorithms, their mathematical foundation and their application to practical problems. Additionally, the students learn the basics of related biological phenomena such as the brain and analyses for experimental data of biological information.

Goals and objectives

Students can understand mathematical foundation of biomimetic computational systems, as acquisition of advanced specialized knowledge and skills that can support cutting-edge systems and networks.
Students can apply biomimetic computational systems to practical problems, as acquisition of advanced specialized knowledge and skills that can support cutting-edge systems and networks.
Students can understand analyses for experimental data of biological information, as acquisition of advanced specialized knowledge and skills that can support cutting-edge systems and networks.

Relationship between 'Goals and Objectives' and 'Course Outcomes'

	assignments	Total.
1.	45%	45%
2.	40%	40%
3.	15%	15%
Total.	100%	-

Evaluation method and criteria

Grade is judged by assignments for evaluation, which are specfied from assignments given in each class (100%) .
The level of 60 points is to understand basics of mathematical foundation of biomimetic computational systems, their application to practical problems of and analyses for experimental data of biological information.

Language

Japanese

Class schedule

	Class schedule	HW assignments (Including preparation and review of the class.)	Amount of Time Required
1.	Guidance - Biomimetic computational systems Basics of Machine learning 1 - Supervised learning	assignments distributed in the class	190minutes
2.	Basics of Machine learning 2 - Regression analysis - Discriminant analysis	assignments distributed in the class	190minutes
3.	Neural information processing 1 - Formal neuron - Perceptron	assignments distributed in the class	190minutes
4.	Basics of Machine learning 3 - Optimization techniques	assignments distributed in the class	190minutes
5.	Neural information processing 2 - Hierarchical neural networks and Backpropagation algorithm 1	assignments distributed in the class	190minutes
6.	Neural information processing 3 - Hierarchical neural networks and Backpropagation algorithm 2	assignments distributed in the class	190minutes
7.	Neural information processing 4 - Hierarchical neural networks and Backpropagation algorithm 3	assignments distributed in the class	190minutes
8.	Neural information processing 5 - Autoencoder - Convolutional neural networks	assignments distributed in the class	190minutes
9.	Neural information processing 6 - Time series data - Recurrent neural networks	assignments distributed in the class	190minutes
10.	Neural information processing 10 - dynamical systems and nonlinear dynamics, chaos dynamical systems - Hopfield type neural networks - Chaos dynamical systems	assignments distributed in the class	190minutes
11.	Reinforcement learning - Basics of reinforcement learning - Application of reinforcement learning	assignments distributed in the class	190minutes
12.	Genetics and information processing - Genetic algorithm - Mathematical models of population genetics	assignments distributed in the class	190minutes
13.	Analyses for experimental data of biological information 1 - Statistical evaluation	assignments distributed in the class	190minutes
14.	Analyses for experimental data of biological information 2 - Analysis of data in brain-computer interface, as an example - Application of biomimetic computational systems	assignments distributed in the class	190minutes
Total.	-	-	2660minutes

Feedback on exams, assignments, etc.

ways of feedback	specific contents about "Other"
Feedback in the class

Textbooks and reference materials

Materials will be distributed.

Prerequisites

Calculus, linear algebra

Office hours and How to contact professors for questions

Before and after the class
E-mail can be received anytime.

Regionally-oriented

Non-regionally-oriented course

Development of social and professional independence

Course that cultivates an ability for utilizing knowledge

Active-learning course

More than one class is interactive

Course by professor with work experience

Work experience	Work experience and relevance to the course content if applicable
N/A	N/A

Education related SDGs:the Sustainable Development Goals

9.INDUSTRY, INNOVATION AND INFRASTRUCTURE

Last modified : Sat Mar 14 14:30:30 JST 2026