computer science

Kunihiko Fukushima

Kunihiko Fukushima is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Kunihiko Fukushima rather than just read about it. In short: Kunihiko Fukushima (Japanese: 福島邦彦, born 16 March 1936) is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a senior research scientist at the Fuzzy Logic Systems Institute in Fukuoka, Japan.

Key takeaways

Kunihiko Fukushima belongs to computer science; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Kunihiko Fukushima to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Kunihiko Fukushima from memory before moving on to harder problems.

Reference excerpt

Kunihiko Fukushima (Japanese: 福島邦彦, born 16 March 1936) is a Japanese computer scientist, most noted for his work on artificial neural networks and deep learning. He is currently working part-time as a senior research scientist at the Fuzzy Logic Systems Institute in Fukuoka, Japan.

Notable scientific achievements In 1980, Fukushima published the neocognitron, the original deep convolutional neural network (CNN) architecture. Fukushima proposed several supervised and unsupervised learning algorithms to train the parameters of a deep neocognitron such that it could learn internal representations of incoming data. Today, however, the CNN architecture is usually trained through backpropagation. This approach is now heavily used in computer vision. In 1969 Fukushima introduced the ReLU (Rectifier Linear Unit) activation function in the context of visual feature extraction in hierarchical neural networks, which he called "analog threshold element". (Though the ReLU was first used by Alston Householder in 1941 as a mathematical abstraction of biological neural networks.) As of 2017 it is the most popular activation function for deep neural networks.

Education and career In 1958, Fukushima received his Bachelor of Engineering in electronics from Kyoto University. He became a senior research scientist at the NHK Science & Technology Research Laboratories. In 1989, he joined the faculty of Osaka University. In 1999, he joined the faculty of the University of Electro-Communications. In 2001, he joined the faculty of Tokyo University of Technology. From 2006 to 2010, he was a visiting professor at Kansai University. Fukushima acted as founding president of the Japanese Neural Network Society (JNNS). He also was a founding member on the board of governors of the International Neural Network Society (INNS), and president of the Asia-Pacific Neural Network Assembly (APNNA). He was one of the board of governors of the International Neural Network Society (INNS) in 1989-1990 and 1993-2005.

Awards In 2020, Fukushima received the Bower Award and Prize for Achievement in Science. In 2022, Fukushima became a laureate of the Asian Scientist 100 by the Asian Scientist. He also received the IEICE Achievement Award and Excellent Paper Awards, the IEEE Neural Networks Pioneer Award, the APNNA Outstanding Achievement Award, the JNNS Excellent Paper Award and the INNS Helmholtz Award.

External links ResearchMap profile

References

Worked examples

Example 1 — a first encounter with Kunihiko Fukushima

Start with the simplest possible case. Write down what Kunihiko Fukushima claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Kunihiko Fukushima before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Kunihiko Fukushima ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Kunihiko Fukushima

In research: Kunihiko Fukushima appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Kunihiko Fukushima in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Kunihiko Fukushima is common in secondary-school and first-year university syllabi. It links to neighbouring topics Academic staff of the University of Electro-Communications, Artificial intelligence researchers, Benjamin Franklin Medal (Franklin Institute) laureates, so understanding it makes those chapters shorter.
In everyday life: Look for Kunihiko Fukushima outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Kunihiko Fukushima in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Kunihiko Fukushima means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Kunihiko Fukushima out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Kunihiko Fukushima in simple terms?

Why does Kunihiko Fukushima matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Kunihiko Fukushima?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Kunihiko Fukushima.