Presentation 2008-03-12
Generation of long-term memory based on multilayer chaotic neural network capable of sequential learning
Shingo EMOTO, Hajime ASAMA, Mihoko OTAKE,
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Abstract(in English) The human memory has various aspects i.e., meta memory, short-term and long-term memory, semantic and episodic memory. Numerous studies have been done for understanding memory function to date. Individual models have been proposed for each phenomenon. Some of them contradict each other or cannot be coexist, based on the different hypotheses or definitions. However, human neural network in the brain which controls memory has one solid structure. Different models should converge with an integrated model. Purpose of this study is to propose such an integrated model which can represent diversity of memories through combining previous knowledge. Our model consists of two-layered chaotic neural networks with different learning constants. The model successfully simulated meta-memory, successive memory, free recall, loss of mid-term memory, long-term memory, episodic memory, and semantic memory. We discuss the relationship between different memories in terms of simulation results and constitutive principle.
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Keyword(in English) chaotic neural network / episodic memory / successive learning / semantic memory / long-term memory
Paper # NC2007-113
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Committee NC
Conference Date 2008/3/5(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
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Title (in English) Generation of long-term memory based on multilayer chaotic neural network capable of sequential learning
Sub Title (in English)
Keyword(1) chaotic neural network
Keyword(2) episodic memory
Keyword(3) successive learning
Keyword(4) semantic memory
Keyword(5) long-term memory
1st Author's Name Shingo EMOTO
1st Author's Affiliation Research into Artifacts, The University of Tokyo()
2nd Author's Name Hajime ASAMA
2nd Author's Affiliation Research into Artifacts, The University of Tokyo
3rd Author's Name Mihoko OTAKE
3rd Author's Affiliation Research into Artifacts, The University of Tokyo
Date 2008-03-12
Paper # NC2007-113
Volume (vol) vol.107
Number (no) 542
Page pp.pp.-
#Pages 6
Date of Issue