The 2nd Deep Learning and Artificial Intelligence Winter School 2018
10th – 13th December 2018, Bangkok, Thailand

Kunihiko Fukushima
Senior Research Scientist, Fuzzy Logic Systems Institute(Iizuka, Fukuoka, Japan)

Topic: Deep CNN neocognitron for artificial vision
Abstract:
Recently, deep convolutional neural networks (deep CNN) have become very popularin the field of visual pattern recognition. The neocognitron, which was first proposed by
Fukushima (1979), is a network classified to this category. It is a hierarchical multi-layerednetwork. Its architecture was suggested by neurophysiological findings on the visual systemsof mammals. It acquires the ability to recognize visual patterns robustly through learning.
Although the neocognitron has a long history, improvements of the network are stillcontinuing. This talk discusses the recent neocognitron, focusing on differences from theconventional deep CNN.
Some other functions of the visual system can also be realized by networks extended
from the neocognitron. For example, by adding top-down connections to the neocognitron,
function of selective attention can be introduced. The ability of recognizing and completing
partly occluded patterns can also be realized.
Bio:Kunihiko Fukushima received a B.Eng. degree in electronics in 1958 and a PhD degree
in electrical engineering in 1966 from Kyoto University, Japan. He was a professor at Osaka
University from 1989 to 1999, at the University of Electro-Communications from 1999 to2001, at Tokyo University of Technology from 2001 to 2006; and a visiting professor at Kansai
University from 2006 to 2010. Prior to his Professorship, he was a Senior Research Scientist
at the NHK Science and Technology Research Laboratories. He is now a Senior ResearchScientist at Fuzzy Logic Systems Institute (part-time position), and usually works at his homein Tokyo.
He received the Achievement Award, Distinguished Achievement and Contributions
Award, and Excellent Paper Awards from IEICE; the Neural Networks Pioneer Award fromIEEE; APNNA Outstanding Achievement Award; Excellent Paper Award, and Academic Award
from JNNS; INNS Helmholtz Award; Pioneer Award from ELM2017; and so on. He was thefounding President of JNNS (the Japanese Neural Network Society) and was a founding
member on the Board of Governors of INNS (the International Neural Network Society). He isa former President of APNNA (the Asia-Pacific Neural Network Assembly).
He is one of the pioneers in the field of neural networks and has been engaged inmodeling neural networks of the brain since 1965. His special interests lie in modeling neuralnetworks of the higher brain functions, especially the mechanism of the visual system. InThe 2nd Deep Learning and Artificial Intelligence Winter School 2018
10th – 13th December 2018, Bangkok, Thailand

1979, he invented “neocognitron “, which is a deep CNN (convolutional neural network) andacquires the ability to recognize visual patterns through learning. The extension of theneocognitron is still continuing. By the introduction of top-down connections and newlearning methods, various kinds of neural networks have been developed. When two or morepatterns are presented simultaneously, the “Selective Attention Model ” can segment andrecognize individual patterns in tern by switching its attention. Even if a pattern is partiallyoccluded by other objects, we human beings can often recognize the occluded pattern. An
extended neocognitron can now have such human-like ability and can, not only recognize
occluded patterns, but also restore them by completing occluded contours. He also
developed neural network models for extracting visual motion and optic flow, for extracting
symmetry axis, and many others. He is recently interested in new learning rules for neuralnetworks.