A LEARNING RULE TO CONTROL THE FREQUENCY OF THE OSCILLATORY NEURAL NETWORK
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- Kurokawa Hiroaki
- Keio University
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- Ho Chun Ying
- City University of Hong Kong
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- Mori Shinsaku
- Nippon Institute of technology
Bibliographic Information
- Other Title
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- Learning Rule to Control the Frequency
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Abstract
This paper proposes the learning methods to control the frequency of an oscillatory neural network. The learning rules are applied to the neural oscillator that comprises two excitatory neurons, in which only one neuron has a positive feedback weight. Since it is assumed that only the feedback parameter has plasticity, the proposed learning rule can be realized with a high simplicity. By defining the phase of the neural oscillation, a mathematical model is conceived so as to conjure up of the blurred vision of phase trajectories in the system. Successful examples of the frequency learning of the sinusoidal function is shown by the computer simulation. With the proposed learning methods, the frequency of the oscillatory neural network can be adjusted to that of any desired value.
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 118 (7-8), 1108-1113, 1998
The Institute of Electrical Engineers of Japan
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Details 詳細情報について
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- CRID
- 1390001204608355712
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- NII Article ID
- 130006844107
- 10002814591
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- NII Book ID
- AN10065950
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- ISSN
- 13488155
- 03854221
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- NDL BIB ID
- 4496730
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed