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工学専攻 情報工学系プログラム知能情報分野 |
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出身学校
出身大学院
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東京大学 大学院新領域創成科学研究科 複雑理工学専攻 博士課程 修了
- 2005年03月
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東京大学 大学院新領域創成科学研究科 複雑理工学専攻 修士課程 修了
- 2002年03月
学外略歴
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名古屋工業大学 教授
2023年04月 - 現在
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文部科学省 21世紀COEプログラム 超ロバスト計算原理プロジェクト 研究補助員
2002年12月 - 2005年03月
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東京大学 生産技術研究所 助教
2007年04月 - 2011年07月
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東京大学 生産技術研究所 特任准教授
2011年08月 - 2013年03月
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東京大学 生産技術研究所 助手
2006年08月 - 2007年03月
論文
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Long-time-constant leaky-integrating oxygen-vacancy drift-diffusion FET for human-interactive spiking reservoir computing. 査読あり
Hisashi Inoue, Hiroto Tamura, Ai Kitoh, Xiangyu Chen, Zolboo Byambadorj, Takeaki Yajima, Yasushi Hotta, Tetsuya Iizuka, Gouhei Tanaka, Isao H. Inoue
2023 IEEE Symposium on VLSI Technology and Circuits 1 - 2 2023年07月
記述言語：英語 掲載種別：研究論文（国際会議プロシーディングス）
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Performance Enhancement of a Spin-Wave-Based Reservoir Computing System Utilizing Different Physical Conditions 査読あり
Ryosho Nakane, Akira Hirose, Gouhei Tanaka
Physical Review Applied 19 ( 3 ) 2023年03月
担当区分：最終著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
We numerically study how to enhance reservoir computing performance by thoroughly extracting the spin-wave device potential for higher-dimensional information generation. The reservoir device has a 1-input exciter and 120-output detectors on the top of a continuous magnetic garnet film for spin-wave transmission. For various nonlinear and fading-memory dynamic phenomena distributing in the film space, small in-plane magnetic fields are used to prepare stripe domain structures and various damping constants at the film sides and bottom are explored. The ferromagnetic resonant frequency and relaxation time of spin precession clearly characterizes the change in spin dynamics with the magnetic field and damping constant. The common input signal for reservoir computing is a 1-GHz cosine wave with random 6-valued amplitude modulation. A basic 120-dimensional reservoir output vector is obtained from time-series signals at the 120-output detectors under each of three magnetic field conditions. Then, 240- and 360-dimensional reservoir output vectors are also constructed by concatenating two and three basic ones, respectively. In nonlinear autoregressive moving average (NARMA) prediction tasks, the computational performance is enhanced as the dimension of the reservoir output vector becomes higher and a significantly low prediction error is achieved for the tenth-order NARMA task using the 360-dimensional vector and optimum damping constant. The results are clear evidence that the collection of diverse output signals efficiently increases the dimensionality of the integrated reservoir state vector (i.e. reservoir-state richness) and thereby contributes to high computational performance. This paper demonstrates that performance enhancement through various configuration settings is a practical approach for on-chip reservoir computing devices with small numbers of real output nodes.
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Multi-Reservoir Echo State Networks with Hodrick-Prescott Filter for nonlinear time-series prediction.
Ziqiang Li, Yun Liu, Gouhei Tanaka
Applied Soft Computing 135 110021 - 110021 2023年03月
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Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory. 査読あり 国際誌
Gouhei Tanaka, Ryosho Nakane, Tomoya Takeuchi, Toshiyuki Yamane, Daiju Nakano, Yasunao Katayama, Akira Hirose
IEEE transactions on neural networks and learning systems 31 ( 1 ) 24 - 38 2020年01月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
The development of hardware neural networks, including neuromorphic hardware, has been accelerated over the past few years. However, it is challenging to operate very large-scale neural networks with low-power hardware devices, partly due to signal transmissions through a massive number of interconnections. Our aim is to deal with the issue of communication cost from an algorithmic viewpoint and study learning algorithms for energy-efficient information processing. Here, we consider two approaches to finding spatially arranged sparse recurrent neural networks with the high cost-performance ratio for associative memory. In the first approach following classical methods, we focus on sparse modular network structures inspired by biological brain networks and examine their storage capacity under an iterative learning rule. We show that incorporating long-range intermodule connections into purely modular networks can enhance the cost-performance ratio. In the second approach, we formulate for the first time an optimization problem where the network sparsity is maximized under the constraints imposed by a pattern embedding condition. We show that there is a tradeoff between the interconnection cost and the computational performance in the optimized networks. We demonstrate that the optimized networks can achieve a better cost-performance ratio compared with those considered in the first approach. We show the effectiveness of the optimization approach mainly using binary patterns and apply it also to gray-scale image restoration. Our results suggest that the presented approaches are useful in seeking more sparse and less costly connectivity of neural networks for the enhancement of energy efficiency in hardware neural networks.
DOI： 10.1109/TNNLS.2019.2899344
その他リンク： https://dblp.uni-trier.de/db/journals/tnn/tnn31.html#TanakaNTYNKH20
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Recent advances in physical reservoir computing: A review 査読あり 国際誌
Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Heroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose
NEURAL NETWORKS 115 100 - 123 2019年07月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：PERGAMON-ELSEVIER SCIENCE LTD
Reservoir computing is a computational framework suited for temporal/sequential data processing. It is derived from several recurrent neural network models, including echo state networks and liquid state machines. A reservoir computing system consists of a reservoir for mapping inputs into a high-dimensional space and a readout for pattern analysis from the high-dimensional states in the reservoir. The reservoir is fixed and only the readout is trained with a simple method such as linear regression and classification. Thus, the major advantage of reservoir computing compared to other recurrent neural networks is fast learning, resulting in low training cost. Another advantage is that the reservoir without adaptive updating is amenable to hardware implementation using a variety of physical systems, substrates, and devices. In fact, such physical reservoir computing has attracted increasing attention in diverse fields of research. The purpose of this review is to provide an overview of recent advances in physical reservoir computing by classifying them according to the type of the reservoir. We discuss the current issues and perspectives related to physical reservoir computing, in order to further expand its practical applications and develop next-generation machine learning systems.
DOI： 10.1016/j.neunet.2019.03.005
その他リンク： https://dblp.uni-trier.de/db/journals/nn/nn115.html#TanakaYHNKTNNH19
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Random and targeted interventions for epidemic control in metapopulation models. 査読あり 国際誌
Gouhei Tanaka, Chiyori Urabe, Kazuyuki Aihara
Scientific reports 4 5522 - 5522 2014年07月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：NATURE PUBLISHING GROUP
In general, different countries and communities respond to epidemics in accordance with their own control plans and protocols. However, owing to global human migration and mobility, strategic planning for epidemic control measures through the collaboration of relevant public health administrations is gaining importance for mitigating and containing large-scale epidemics. Here, we present a framework to evaluate the effectiveness of random (non-strategic) and targeted (strategic) epidemic interventions for spatially separated patches in metapopulation models. For a random intervention, we analytically derive the critical fraction of patches that receive epidemic interventions, above which epidemics are successfully contained. The analysis shows that the heterogeneity of patch connectivity makes it difficult to contain epidemics under the random intervention. We demonstrate that, particularly in such heterogeneously connected networks, targeted interventions are considerably effective compared to the random intervention. Our framework is useful for identifying the target areas where epidemic control measures should be focused.
DOI： 10.1038/srep05522
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Dynamical robustness of coupled heterogeneous oscillators. 査読あり 国際誌
Gouhei Tanaka, Kai Morino, Hiroaki Daido, Kazuyuki Aihara
Physical review. E, Statistical, nonlinear, and soft matter physics 89 ( 5 ) 052906 - 052906 2014年05月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：AMER PHYSICAL SOC
We study tolerance of dynamic behavior in networks of coupled heterogeneous oscillators to deterioration of the individual oscillator components. As the deterioration proceeds with reduction in dynamic behavior of the oscillators, an order parameter evaluating the level of global oscillation decreases and then vanishes at a certain critical point. We present a method to analytically derive a general formula for this critical point and an approximate formula for the order parameter in the vicinity of the critical point in networks of coupled Stuart-Landau oscillators. Using the critical point as a measure for dynamical robustness of oscillator networks, we show that the more heterogeneous the oscillator components are, the more robust the oscillatory behavior of the network is to the component deterioration. This property is confirmed also in networks of Morris-Lecar neuron models coupled through electrical synapses. Our approach could provide a useful framework for theoretically understanding the role of population heterogeneity in robustness of biological networks.
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Dynamical robustness in complex networks: the crucial role of low-degree nodes. 査読あり 国際誌
Gouhei Tanaka, Kai Morino, Kazuyuki Aihara
Scientific reports 2 232 - 232 2012年
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：NATURE PUBLISHING GROUP
Many social, biological, and technological networks consist of a small number of highly connected components (hubs) and a very large number of loosely connected components (low-degree nodes). It has been commonly recognized that such heterogeneously connected networks are extremely vulnerable to the failure of hubs in terms of structural robustness of complex networks. However, little is known about dynamical robustness, which refers to the ability of a network to maintain its dynamical activity against local perturbations. Here we demonstrate that, in contrast to the structural fragility, the nonlinear dynamics of heterogeneously connected networks can be highly vulnerable to the failure of low-degree nodes. The crucial role of low-degree nodes results from dynamical processes where normal (active) units compensate for the failure of neighboring (inactive) units at the expense of a reduction in their own activity. Our finding highlights the significant difference between structural and dynamical robustness in complex networks.
DOI： 10.1038/srep00232
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Complex-Valued Multistate Associate Memory with Nonlinear Multilevel Functions for Gray-Level Image Reconstruction 査読あり 国際誌
Gouhei Tanaka, Kazuyuki Aihara
IEEE Transactions on Neural Networks 20 20 ( 9 ) 1463 - 73 2009年09月
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Bifurcation analysis on a hybrid systems model of intermittent hormonal therapy for prostate cancer 査読あり
Gouhei Tanaka, Kunichika Tsumoto, Shigeki Tsuji, Kazuyuki Aihara
PHYSICA D-NONLINEAR PHENOMENA 237 ( 20 ) 2616 - 2627 2008年10月
担当区分：筆頭著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：ELSEVIER SCIENCE BV
Hybrid systems are widely used to model dynamical phenomena that are characterized by interplay between continuous dynamics and discrete events. An example of biomedical application is modeling of disease progression of prostate cancer under intermittent hormonal therapy, where continuous tumor dynamics is switched by interruption and reinstitution of medication. In the present paper, we study a hybrid systems model representing intermittent androgen suppression (IAS) therapy for advanced prostate cancer. Intermittent medication with switching between on-treatment and off-treatment periods is intended to possibly prevent a prostatic tumor from developing into a hormone-refractory state and is anticipated as a possible strategy for delaying or hopefully averting a cancer relapse which most patients undergo as a result of long-term hormonal suppression. Clinical efficacy of IAS therapy for prostate cancer is still under investigation but at least worth considering in terms of reduction of side effects and economic costs during off-treatment periods. In the model of IAS therapy, it depends on some clinically controllable parameters whether a relapse of prostate cancer occurs or not. Therefore, we examine nonlinear dynamics and bifurcation structure of the model by exploiting a numerical method to clarify bifurcation sets in the hybrid system. Our results suggest that adjustment of the normal androgen level in combination with appropriate medication scheduling could enhance the possibility of relapse prevention. Moreover, a two-dimensional piecewise-linear system reduced from the original model highlights the origin of nonlinear phenomena specific to the hybrid system. (C) 2008 Elsevier B.V. All rights reserved.
書籍等出版物
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リザバーコンピューティング : 時系列パターン認識のための高速機械学習の理論とハードウェア
田中, 剛平, 中根, 了昌, 廣瀬, 明（ 担当： 共著）
森北出版 2021年03月 （ ISBN:9784627855311 ）
総ページ数：vi, 207p 記述言語：日本語 著書種別：学術書
MISC
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リザバーコンピューティング 招待あり
田中剛平
電子情報通信学会誌 106 ( 6 ) 2023年06月
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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 (小特集 リザバーコンピューティング)—Concept of Reservoir Computing and Its Recent Trends
田中 剛平
電子情報通信学会誌 = The journal of the Institute of Electronics, Information and Communication Engineers 102 ( 2 ) 108 - 113 2019年02月
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スピン波リザバーコンピューティング:実用的な計算性能向上の手法
中根了昌, 廣瀬明, 田中剛平
応用物理学会春季学術講演会講演予稿集(CD-ROM) 70th 2023年03月
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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年06月
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磁区を伝播するスピン波を用いたリザバーコンピューティング
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年
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スパイキングニューラルネットワークとreward-modulated STDPによるリザバーコンピューティング
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年
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リザバーコンピューティングによる再構成処理を利用した時系列信号の異常検知の試み
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年
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リザバー状態のマハラノビス距離を用いた省メモリ型時系列異常検知
田村浩人, 田中剛平, 田中剛平, 藤原寛太郎
日本神経化学会大会抄録集(Web) 65th 2022年
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リワードマシンを用いる強化学習手法の計算性能とタスク難易度の関係
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年
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教師なしパターン認識のためのスパイキングニューラルネットワークにおけるスパース結合の影響
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年
講演・口頭発表等
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Diverse-Timescale Echo State Networks for Multiscale Modeling 国際会議
Gouhei Tanaka
International Conference on Neuromorphic, Natural, and Physical Computing 2023年10月
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Tailoring oxygen diffusion dynamics in three-terminal devices for spiking reservoir computing 国際会議
Hisashi Inoue, H. Tamura, A. Kitoh, X. Chen, Z. Byambadorj, T. Yajima, Y. Hotta, T. Iizuka, G. Tanaka, I. H. Inoue
MemriSys 2023 2023年11月
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リザバーコンピューティングによる時系列データ学習 招待あり
田中 剛平
第15回Nagoyaオープンイノベーション研究会 2023年09月
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Mahalanobis Distance of Reservoir States for Highly-Efficient Time-Series Classification
Hiroto Tamura, Kantaro Fujiwara, Kazuyuki Aihara, Gouhei Tanaka
The 33rd Annual Meeting of the Japanese Neural Network Society 2023年09月
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Identity Recognition with Bidirectional Echo State Networks from Continuous Blood Pressure Data
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
The 33rd Annual Meeting of the Japanese Neural Network Society 2023年09月
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リザバーコンピューティングの高性能化・高効率化に関して 招待あり
田中 剛平
第15回 AI Optics研究グループ研究会 2023年03月
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AIによる複雑ダイナミクスのパターン認識 招待あり
田中 剛平
日本生理学会第100回記念大会, 100周年記念事業委員会企画シンポジウム「AIが切り開く医学・生理学・生命科学の新展開」 2023年03月
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リザバーコンピューティングとマルチスケールモデリング 招待あり
田中 剛平
電子情報通信学会総合大会, チュートリアルセッション「NBT-1. 次世代ネットワークを支える数理モデルの展開」 2023年03月
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アテンションメカニズムをもつマルチリザバーエコーステートネット ワークを用いた室内空気質予測
電子情報通信学会 非線形問題研究会 2023年01月
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Inference of network structure in dynamic systems using a generative model 国際会議
Shuhan Zheng, Gouhei Tanaka
Dynamics Days US 2023 2023年01月
受賞
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Best Paper Award 3rd Prize, 29th International Conference on Artificial Neural Networks (ICANN)
2020年11月 European Neural Network Society Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing
Hiroto Tamura, Gouhei Tanaka
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Best Paper Award
2017年 IEEE Transcations on Nanotechnology 2016
Gouhei Tanaka, Yasunao Katayama, Daiju Nakano, Toshiyuki Yamane, Ryosho Nakan
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Best Poster Award (Silver Prize)
2011年 The FIRST International Symposium on Innovative Mathematical Modelling Robustness of Scale-free Networks of Oscillators against Partial Inactivation
Gouhei Tanaka
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Honorable Mention Award, DSWeb Tutorials Contest
2005年 Society for Industrial and Applied Mathematics Crisis-induced Intermittency in Coupled Chaotic Maps
Gouhei Tanaka