Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions
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- Ibuki Matsumoto
- Graduate School of Information and Computer Science, Chiba Institute of Technology,Chiba,Japan
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- Sou Nobukawa
- Graduate School of Information and Computer Science, Chiba Institute of Technology,Chiba,Japan
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- Tomoki Kurikawa
- Future University Hakodate,Department of Complex and Intelligent Systems,Hokkaido,Japan
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- Nobuhiko Wagatsuma
- Toho University,Department of Information Science,Chiba,Japan
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- Yusuke Sakemi
- Chiba Institute of Technology,Research Center for Mathematical Engineering,Chiba,Japan
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- Takashi Kanamaru
- School of Advanced Engineering, Kogakuin University,Tokyo,Japan
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- Nina Sviridova
- Tokyo City University,Department of Intelligent Systems,Tokyo,Japan
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- Kazuyuki Aihara
- The University of Tokyo,International Research Center for Neurointelligence,Tokyo,Japan
書誌事項
- 公開日
- 2023-06-18
- 資源種別
- journal article
- 権利情報
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- https://doi.org/10.15223/policy-029
- https://doi.org/10.15223/policy-037
- DOI
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- 10.1109/ijcnn54540.2023.10191709
- 公開者
- IEEE
収録刊行物
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- 2023 International Joint Conference on Neural Networks (IJCNN)
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2023 International Joint Conference on Neural Networks (IJCNN) 1-8, 2023-06-18
IEEE
- Tweet
詳細情報 詳細情報について
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- CRID
- 1360021390747760512
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- 資料種別
- journal article
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- データソース種別
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- Crossref
- KAKEN
- OpenAIRE
