| Presentation | 2014-03-10 Analysis of network structure constructed by self-organizing recurrent network model Tomohiro AWANO, Kantaro FUJIWARA, Tohru IKEGUCHI, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Self-organizing recurrent network (SORN) model is a neural network model that can reproduce statistical and dynamical properties of synaptic connection strengths in the cerebral cortex. In the SORN model, fundamental characteristics of excitatory synaptic connections can be explained as a consequence of self-organization by integrating different forms of plasticity. However, the network structure constructed by the SORN model has not yet been clarified. In this report, we investigated the fraction of connections through spike-timing-dependent plasticity learning. As a result, we revealed that the SORN model has structural sensitive dependence on initial network structures. In addition, we showed that the effect of the structural sensitive dependence on initial network structures is induced by inhibitory spike-timing-dependent plasticity. These results imply that a self-organizing recurrent network with the STDP learning can realize various synaptic connectivities without tuning parameters. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | spike-timing-dependent plasticity / self-organizing recurrent network / network topology |
| Paper # | NLP2013-173 |
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| Conference Information | |
| Committee | NLP |
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| Conference Date | 2014/3/3(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Nonlinear Problems (NLP) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Analysis of network structure constructed by self-organizing recurrent network model |
| Sub Title (in English) | |
| Keyword(1) | spike-timing-dependent plasticity |
| Keyword(2) | self-organizing recurrent network |
| Keyword(3) | network topology |
| 1st Author's Name | Tomohiro AWANO |
| 1st Author's Affiliation | Graduate School of Science and Engineering, Saitama University() |
| 2nd Author's Name | Kantaro FUJIWARA |
| 2nd Author's Affiliation | Graduate School of Science and Engineering, Saitama University |
| 3rd Author's Name | Tohru IKEGUCHI |
| 3rd Author's Affiliation | Graduate School of Science and Engineering, Saitama University:Saitama University, Brain Science Institute |
| Date | 2014-03-10 |
| Paper # | NLP2013-173 |
| Volume (vol) | vol.113 |
| Number (no) | 486 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |