Self-Organizing Small-World Structure of Neural Networks by STDP Learning Rule
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概要
- 論文の詳細を見る
Spike-timing-dependent plasticity (STDP) learning strengthens or weakens synaptic weights of a neural network, thus the neural network temporally evolves by the STDP rule. By estimating the characteristic path length and clustering coefficient, we examined how the neural network structure changes and synaptic spikes synchronize. Even if the neural networks do not have any initial structure, small-world characteristics emerge; the characteristic path length is as small as that of a random graph, but the clustering coefficient is greater.
- 同志社大学の論文
著者
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SUZUKI Tomoya
Department of Chemistry, University of Tsukuba
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IKEGUCHI Tohru
Graduate School of Science, Tokyo University of Science,
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Suzuki Tomoya
Department Of Information System Design Faculty Of Engineering Doshisha University
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Ikeguchi Tohru
Graduate School Of Sci. And Eng. Saitama University
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Suzuki Tomoya
Department Of Chemical Engineering Nagoya University
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