日本神経回路学会誌
Online ISSN : 1883-0455
Print ISSN : 1340-766X
ISSN-L : 1340-766X
研究論文
人工神経回路網ハイパーコラムモデルにおける組合せ学習ならびに連想学習
島田 敬士鶴田 直之谷口 倫一郎
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2006 年 13 巻 4 号 p. 129-136

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Hyper-Column Model (HCM) is a self-organized, competitive and hierarchical multilayer neural network. It is derived from the Neocognitron by replacing each S cell and C cell with a two layer Hierarchical Self-Organizing Map (HSOM). HCM can recognize images with variant object size, position, orientation and spatial resolution. In this paper, we propose two new learning methods; “Combinatorial Learning, ” and “Associative Learning”. The former enables HCM to learn a pattern of winner neurons which are activated in each HSOM with excitatory lateral connections. HCM is expanded to a supervised learnable model by the latter learning algorithm.

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© 2006 日本神経回路学会
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