Presentation | 2001/6/21 A model of context-dependent association using selective desensitization of nonmonotonic neural elements Kouhei MATSUZAWA, Kazuhiko MURATA, Masahiko MORITA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Existing neural network models with distributed information representation have difficulty in associating the same input with various targets. We consider the reason of this difficulty and present a model of context-dependent association using a novel method of contextual modification. We also show by computer simulations that this model can simulate a large-scale finite state machine with a relatively small number of neural elements and limited repetitions of learning. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Neural network / Associative memory / Selective desensitization / Nonmonotone dynamics / Trajectory attractor / Finite state machine |
Paper # | NC2001-21 |
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Committee | NC |
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Conference Date | 2001/6/21(1days) |
Place (in Japanese) | (See Japanese page) |
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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) | A model of context-dependent association using selective desensitization of nonmonotonic neural elements |
Sub Title (in English) | |
Keyword(1) | Neural network |
Keyword(2) | Associative memory |
Keyword(3) | Selective desensitization |
Keyword(4) | Nonmonotone dynamics |
Keyword(5) | Trajectory attractor |
Keyword(6) | Finite state machine |
1st Author's Name | Kouhei MATSUZAWA |
1st Author's Affiliation | Doctral Program in Systems and Information Engineering, University of Tsukuba() |
2nd Author's Name | Kazuhiko MURATA |
2nd Author's Affiliation | College of Engineering Systems, University of Tsukuba |
3rd Author's Name | Masahiko MORITA |
3rd Author's Affiliation | Institute of Engineering Mechanics and Systems, University of Tsukuba |
Date | 2001/6/21 |
Paper # | NC2001-21 |
Volume (vol) | vol.101 |
Number (no) | 153 |
Page | pp.pp.- |
#Pages | 8 |
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