Presentation | 2007-12-22 Numerical Simulation on Sequence Learning of Dual Network Takeshi NAITOU, Toshiya IWAI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In order to realize the mechanics to maintain short term memory (STM) and to form the long term memory (LTM) from STM by a neural computing, Jensen et al. propose the dual network model by extracting physiological phenomena appeared in and around the Hippocampus. They indicate in their paper the possibility to learn two sequences that have a same item. The method of their numerical experiments for the sequence learning is not shown in their paper in detail. However, our numerical experiments of the sequence learning by using their model is not succeeded: Well learned synaptic weights of the first sequence are destroyed during the learning of the second sequence. By applying STDP to a learning rule of synaptic weights, two sequences which have a same item are successfully learned. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Hippocampus / Short Term Memory / Long Term Memory / Sequence Learning |
Paper # | NC2007-77 |
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Committee | NC |
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Conference Date | 2007/12/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Numerical Simulation on Sequence Learning of Dual Network |
Sub Title (in English) | |
Keyword(1) | Hippocampus |
Keyword(2) | Short Term Memory |
Keyword(3) | Long Term Memory |
Keyword(4) | Sequence Learning |
1st Author's Name | Takeshi NAITOU |
1st Author's Affiliation | Department of Computer Science, Faculty of Engineering, Nihon University() |
2nd Author's Name | Toshiya IWAI |
2nd Author's Affiliation | Department of Computer Science, Faculty of Engineering, Nihon University |
Date | 2007-12-22 |
Paper # | NC2007-77 |
Volume (vol) | vol.107 |
Number (no) | 410 |
Page | pp.pp.- |
#Pages | 6 |
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