Presentation 2005/6/16
Independent component separation by pattern representation in two random symmetric networks connected via anti-Hebbian synapses
Koji WADA, Koji KURATA,
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Abstract(in English) In this paper, we propose a new self-organnizing model and the learning rule of the model. The model consisits of two random symmetric network connected with anti-Hebbian synapses. We confirmed by computational experiment that when input signals that have two independent components are given to the model, each independent components are extracted into two random symmetric network of the model. Conventional self-organizing maps have Mexican-hat type intra-layer connections. So, they extract the information by way of relating the input to the winner-neuron. Our model introduces the random symmetric intra-layer connections. So, to extract the information, instead of relating the input to the winner-neuron, we relate the input to the firing pattern. That is, our model is the self-organizing model of pattern representation, that extract the independent component into two random symmetric network.
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Keyword(in English) Anti-Hebbian learning / random symmetric network / pattern representation / Independent Component Analysis / Self-organization
Paper # NC2005-19
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Committee NC
Conference Date 2005/6/16(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Independent component separation by pattern representation in two random symmetric networks connected via anti-Hebbian synapses
Sub Title (in English)
Keyword(1) Anti-Hebbian learning
Keyword(2) random symmetric network
Keyword(3) pattern representation
Keyword(4) Independent Component Analysis
Keyword(5) Self-organization
1st Author's Name Koji WADA
1st Author's Affiliation Dept. of Electrical Engineering, Kochi National College of Technology()
2nd Author's Name Koji KURATA
2nd Author's Affiliation Faculty of Engineering, Ryukyu University
Date 2005/6/16
Paper # NC2005-19
Volume (vol) vol.105
Number (no) 130
Page pp.pp.-
#Pages 6
Date of Issue