| Presentation | 2011-06-23 Information content analysis for spike trains of neuron and neural networks Takashi TAKEKAWA, Yoshikazu ISOMURA, Tomoki FUKAI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Traditionally, firing rates are considered informative signals represented by action potentials, and compared with observed behavioral or perceptual phenomenon. However, it is possible that the detailed temporal information of action potentials also contains important signals. Recently, many people attempt to understand how particular observed signal can be predicted by spike trains using supervised learning. In this work, we apply a unsupervised learning method, kernel principle component analysis methods, for extracting information from single- or multi-neuron spike trains and seek the most informative decoding scheme only using spike trains. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Kernel Principle Component Analysis / Firing Rate / Temporal Coding |
| Paper # | NC2011-1 |
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| Committee | NC |
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| Conference Date | 2011/6/16(1days) |
| Place (in Japanese) | (See Japanese page) |
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| 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) | Information content analysis for spike trains of neuron and neural networks |
| Sub Title (in English) | |
| Keyword(1) | Kernel Principle Component Analysis |
| Keyword(2) | Firing Rate |
| Keyword(3) | Temporal Coding |
| 1st Author's Name | Takashi TAKEKAWA |
| 1st Author's Affiliation | RIKEN BSI() |
| 2nd Author's Name | Yoshikazu ISOMURA |
| 2nd Author's Affiliation | Brain Science Institute, Tamagawa University |
| 3rd Author's Name | Tomoki FUKAI |
| 3rd Author's Affiliation | RIKEN BSI |
| Date | 2011-06-23 |
| Paper # | NC2011-1 |
| Volume (vol) | vol.111 |
| Number (no) | 96 |
| Page | pp.pp.- |
| #Pages | 2 |
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