The Effects of Biological Constraints on the Performance of Spiking Neural Networks

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カテゴリ: 論文誌(論文単位)グループ名: 【C】電子・情報・システム部門発行日: 2023/07/01タイトル(英語): The Effects of Biological Constraints on the Performance of Spiking Neural Networks著者名: Bin Li (Department of Precisio n Engineering, Graduate School of engineering, The University of Tokyo), Ryuki Iguchi (Department of Precision Engineering, Graduate School of engineering, The University of Tokyo), Hiroki Noyama (Department of Precision Engi著者名(英語): Bin Li (Department of Precision Engineering, Graduate School of engineering, The University of Tokyo), Ryuki Iguchi (Department of Precision Engineering, Graduate School of engineering, The University of Tokyo), Hiroki Noyama (Department of Precision Engineering, Graduate School of engineering, The University of Tokyo), Tianyi Zheng (Department of Precision Engineering, Graduate School of engineering, The University of Tokyo), Kiyoshi Kotani (Research Center for Advanced Science and Technology, The University of Tokyo), Yasuhiko Jimbo (Department of Precision Engineering, Graduate School of engineering, The University of Tokyo)キーワード: SNNs，RNNs，biology，working memory，machine learning要約(英語): Brain-inspired intelligence technology is always cutting-edge research in Artificial Intelligence (AI). These years, mimicking the properties of nerve impulses in the brain, a new type of deep learning network structure has been introduced-Spiking Neural Networks (SNNs). However, the properties of SNNs are still poorly understood, especially their potential biological plausibility. Here, we investigated Spiking Recurrent Neural Networks (SRNNs) obtained by parameters transformation. We investigated their performance and characteristics when achieving working memory tasks under biological constraints from the real brain. Finally, it was proved that the constraints introduced by us are biologically reasonable and can help to create SNNs with keeping both working memory capacity and biological plausibility.本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.7 （2023） 特集：2022年電子・情報・システム部門大会本誌掲載ページ: 634-640 p原稿種別: 論文／英語電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/143/7/143_634/_article/-char/ja/ Show more