Forced Formation of a Geometrical Feature Space by a Neural Network Model with Supervised Learning (Special Section of Letters Selected from the 1993 IEICE Spring Conference)

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概要

• 論文の詳細を見る

• To investigate necessary conditions for the object recognition by simulations using neural network model is one of ways to acquire suggestions for understanding the neuronal representation of objects in the brain. In the present study, we trained a three layered neural network to form a geometrical feature representation in its output layer using back-propagation algorithm. After training using 73 learning examples, 65 testing patterns made by various combinations of above features could be recognized with the network at a rate of 95.3 appropriate response. We could classify four types of hidden layer units on the basis of effects on the output layer.

• 一般社団法人電子情報通信学会の論文
• 1993-07-25

著者

• Kishi Koichiro

  Department Of Pharmacology Jichi Medical School

• Matsuoka Takahide

  Faculty of Engineering, Utsunomiya University

• Takeda Toshiaki

  Laboratory Of Physiological Sciences Jichi Medical School School Of Nursing

• Takeda Toshiaki

  the Laboratory of Physiological Sciences, School of Nursing, Jichi Medical School

• Mizoe Hiroki

  the Faculty of Engineering, Utsunomiya University

• Kishi Koichiro

  the Department of Pharmacology, Jichi Medical School

• Matsuoka Takahide

  the Faculty of Engineering, Utsunomiya University

• Mizoe Hiroki

  The Faculty Of Engineering Utsunomiya University

関連論文

• Asymmetric Expression between Right and Left Hemiface of Human
• Evaluation of Self-Organized Learning in a Neural Network by Means of Mutual Information
• Forced Formation of a Geometrical Feature Space by a Neural Network Model with Supervised Learning (Special Section of Letters Selected from the 1993 IEICE Spring Conference)

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