Misc - TANAKA Gouhei
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リザバーコンピューティング Invited
田中剛平
106 ( 6 ) 2023.06
Authorship：Lead author, Last author, Corresponding author Language：Japanese Publishing type：Article, review, commentary, editorial, etc. (scientific journal)
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リザバーコンピューティングの概念と最近の動向
タナカ ゴウヘイ
電子情報通信学会誌 102 ( 2 ) 108 - 113 2019.02
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Reservoir computing utilizing spin waves: enhancement of computational performance through a practical approach for on-chip devices
応用物理学会春季学術講演会講演予稿集(CD-ROM) 70th 2023.03
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Guest Editorial Special Issue on New Frontiers in Extremely Efficient Reservoir Computing
Gouhei Tanaka, Claudio Gallicchio, Alessio Micheli, Juan Pablo Ortega, Akira Hirose
IEEE Transactions on Neural Networks and Learning Systems 33 ( 6 ) 2571 - 2574 2022.06
Publishing type：Book review, literature introduction, etc.
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Reservoir Computing utilizing spin waves propagating through magnetic domains
中根了昌, 廣瀬明, 田中剛平, 田中剛平, 田中剛平
応用物理学会春季学術講演会講演予稿集(CD-ROM) 69th 2022
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Reservoir computing with spiking neural networks and reward-modulated STDP
鶴海杭之, 田中剛平, 田中剛平
電子情報通信学会技術研究報告(Web) 122 ( 65(NLP2022 1-25) ) 2022
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Experiments of Reconstructive Reservoir Computing to Detect Anomaly in Time-series Signals
加藤准也, 田中剛平, 田中剛平, 中根了昌, 廣瀬明
電子情報通信学会技術研究報告(Web) 121 ( 390(NC2021 46-78) ) 2022
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Relationship between Computational Performance and Task Difficulty of Reinforcement Learning Methods Using Reward Machines
渡邊隆二, 田中剛平
電子情報通信学会技術研究報告(Web) 121 ( 444(NLP2021 126-152) ) 2022
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Effects of sparse connections in spiking neural networks for unsupervised pattern recognition
品川大樹, 藤原寛太郎, 田中剛平, 田中剛平
電子情報通信学会技術研究報告(Web) 121 ( 444(NLP2021 126-152) ) 2022
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Neural network hardware and spin-wave reservoir computing
廣瀬明, 田中剛平, 中根了昌
日本物理学会講演概要集(CD-ROM) 76 ( 1 ) 2021
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Influence of regularity in readout electrode arrangement on the performance of a spin-wave reservoir computing chip
市村剛大, 中根了昌, 田中剛平, 廣瀬明
日本神経回路学会全国大会講演論文集 31st (CD-ROM) 2021
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Physical reservoir computing chip and complex-valued neural networks framework to realize ultra low power consumption information processing
廣瀬明, 中根了昌, 田中剛平
応用物理学会秋季学術講演会講演予稿集(CD-ROM) 81st 214 - 214 2020.08
Language：Japanese Publisher：The Japan Society of Applied Physics
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知っておきたいキーワード(第136回)リザバーコンピューティング—Keywords you should know : Reservoir Computing
田中 剛平
74 ( 3 ) 532 - 534 2020.05
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Analysis of epidemic disease control using metapopulation models
MATSUKI Akari, TANAKA Gouhei
SEISAN KENKYU 72 ( 2 ) 129 - 135 2020.03
Language：English Publisher：Institute of Industrial Science The University of Tokyo
Mathematical models of epidemic diseases are used for understanding complex spreading processes of diseases and assessing the efficacy of countermeasures to epidemics. Among them, metapopulation epidemic models consider populations distributed in spatially distant patches and migration of individuals between patches. These models can be theoretically analyzed to some extent as well as can incorporate realistic factors. In this review, we focus on local interventions to a fraction of patches for reducing the effective transmission rates in the patches. We explain the analytical derivation of the minimum fraction of patches (or the intervention threshold） that is necessary for prevention of global epidemic outbreaks and numerical simulations for validating the efficacy of targeted intervention.
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Mathematical aspects and device implementation of reservoir computing
Tanaka Gouhei
JSAP Annual Meetings Extended Abstracts 2020.1 49 - 49 2020.02
Language：Japanese Publisher：The Japan Society of Applied Physics
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ネットワークと脳科学
タナカ ゴウヘイ, アイハラ カズユキ
58 ( 2 ) 59 - 65 2020.02
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Spatial distribution of information effective for logic function learning in spin-wave reservoir computing chip utilizing spatiotemporal physical dynamics.
Takehiro Ichimura, Ryosho Nakane, Gouhei Tanaka, Akira Hirose
2020 International Joint Conference on Neural Networks(IJCNN) 118 ( 470(NC2018 44-88)(Web) ) 1 - 8 2020
Language：Japanese Publisher：IEEE
This paper investigates the spatial distribution of information effective for function learning in a spin-wave reservoir-computing garnet chip. We map the neural weights of a readout neuron virtually connected massively and densely to the reservoir chip. We find that the spatial weight distribution shows wavefront-like lines, suggesting the importance of concurrent and time-different interferences of the spin waves. We also estimate the size of reservoir output electrodes required for the proper information extraction. These results are significantly useful for designing spin reservoir chips in the near future energy efficient devices.
DOI： 10.1109/IJCNN48605.2020.9207629
Other Link： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#IchimuraNTH20
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パターン情報表現およびパターン情報処理を物理的に実現するニューラルネットワークデバイス
廣瀬明, 廣瀬明, 田中剛平, 田中剛平, 武田征士, 山根敏志, 沼田秀俊, 金澤直輝, HEROUX J. B, 中野大樹, 中根了昌, 中根了昌
電子情報通信学会大会講演論文集(CD-ROM) 2019 ROMBUNNO.CS‐2‐7 2019.03
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スピン波を用いたリザバーコンピューティングデバイスにおける荷重の空間分布
市村剛大, 中根了昌, 中根了昌, 田中剛平, 田中剛平, 廣瀬明, 廣瀬明, 廣瀬明
電子情報通信学会大会講演論文集(CD-ROM) 2019 ROMBUNNO.CS‐2‐8 2019.03
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