Presentation | 1998/11/17 A neural network model with excitable map and its application to temporal learning Yoshinori Hayakawa, Yasuji Sawada, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | We propose a pulse-coupled neural network model in which one-dimensional excitable map is introduced as the neural processing unit connected with time delay. Although individual processing unit solely has simple dynamical properties, the network behaves as a collective chaotic system with robust synchronization within a portion of the units. By introducing a covariance learning algorithm for synaptic connections, we find that the synchronization of excitation timing of the unit can be enhanced and such synchronization can be even controlled by external signals. The learnable range of time is much longer than the inherent time scale of the processing unit, i.e., synaptic delay time. Also, the network can reproduce periodic signals in higher resolution of time than such characteristic time scale. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | one-dimensional map / neural network / temporal learning / synchronization |
Paper # | NC98-61 |
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Committee | NC |
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Conference Date | 1998/11/17(1days) |
Place (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) | A neural network model with excitable map and its application to temporal learning |
Sub Title (in English) | |
Keyword(1) | one-dimensional map |
Keyword(2) | neural network |
Keyword(3) | temporal learning |
Keyword(4) | synchronization |
1st Author's Name | Yoshinori Hayakawa |
1st Author's Affiliation | Research Institute of Electrical Communication, Tohoku university() |
2nd Author's Name | Yasuji Sawada |
2nd Author's Affiliation | Research Institute of Electrical Communication, Tohoku university |
Date | 1998/11/17 |
Paper # | NC98-61 |
Volume (vol) | vol.98 |
Number (no) | 401 |
Page | pp.pp.- |
#Pages | 8 |
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