| Presentation | 2003/12/1 Phaser Neural Network and Mean Field Approximation Haruhisa TAKAHASHI, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | We present a new deterministic network model of representing correlated activation between neurons. The model is represented by a set of complex neural equations computing fire timing as a phase, and of mean field equations computing the mean firing rate. We show that the mean field equations provide a good approximation to stochastic neural model such as Boltzman machine even for large parameter values. We also show that Boltzman learning method is simply replaced by a combination of covariance Hebbian and anti-Hebbian learning on this model. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | phasor / covariance netowork / Boltsmann machine / graphical model |
| Paper # | NC2003-101 |
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| Conference Information | |
| Committee | NC |
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| Conference Date | 2003/12/1(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| 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) | Phaser Neural Network and Mean Field Approximation |
| Sub Title (in English) | |
| Keyword(1) | phasor |
| Keyword(2) | covariance netowork |
| Keyword(3) | Boltsmann machine |
| Keyword(4) | graphical model |
| 1st Author's Name | Haruhisa TAKAHASHI |
| 1st Author's Affiliation | Department of Infromation and Communication Engineering The University of Electro-Communications() |
| Date | 2003/12/1 |
| Paper # | NC2003-101 |
| Volume (vol) | vol.103 |
| Number (no) | 490 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |