Presentation | 2005-12-16 Burst firing ID model and its application Shinya SUENAGA, Yoshihiro HAYAKAWA, Koji NAKAJIMA, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We modify Inverse function Delayed (ID) model to obtain the ability to show burst firing activity. This modification is based on the relation between the ID model and Hodgkin-Huxley model. The burst firing behaviour in our new model is analysed by using phase diagram and the idea of the particle motion in a potential. In these analyses, it is important to classify variables of the new model into a slow variable and a fast variable. The model is able to be applied to solve the N-Queen problem, which is optimization problem, and achieves higher performance in this problem. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Burst firing / Neural network / Optimization problem |
Paper # | NLP2005-95 |
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Conference Information | |
Committee | NLP |
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Conference Date | 2005/12/9(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Burst firing ID model and its application |
Sub Title (in English) | |
Keyword(1) | Burst firing |
Keyword(2) | Neural network |
Keyword(3) | Optimization problem |
1st Author's Name | Shinya SUENAGA |
1st Author's Affiliation | Laboratory for Brainware Laboratory for Nanoelectoronics and Spintronics, Research Institute of Electrical Communication, Tohoku University() |
2nd Author's Name | Yoshihiro HAYAKAWA |
2nd Author's Affiliation | Laboratory for Brainware Laboratory for Nanoelectoronics and Spintronics, Research Institute of Electrical Communication, Tohoku University |
3rd Author's Name | Koji NAKAJIMA |
3rd Author's Affiliation | Laboratory for Brainware Laboratory for Nanoelectoronics and Spintronics, Research Institute of Electrical Communication, Tohoku University |
Date | 2005-12-16 |
Paper # | NLP2005-95 |
Volume (vol) | vol.105 |
Number (no) | 483 |
Page | pp.pp.- |
#Pages | 6 |
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