論文 - 田中 剛平
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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.
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Backbone-based Dynamic Spatio-Temporal Graph Neural Network for epidemic forecasting 査読あり
Junkai Mao, Yuexing Han, Gouhei Tanaka, Bing Wang
Knowledge-Based Systems 296 111952 - 111952 2024年07月
掲載種別：研究論文（学術雑誌）
Accurate epidemic forecasting is a critical task in controlling epidemic spread. Many deep learning-based models focus only on static or dynamic graphs when dealing with spatial information, ignoring their relationship. Additionally, these models often rely on recurrent structures, which can lead to error accumulation and computational time consumption. To address the aforementioned problems, we propose a novel model called Backbone-based Dynamic Spatio-Temporal Graph Neural Network (BDSTGNN). Intuitively, the continuous and smooth changes in graph structure make adjacent graph structures share a basic pattern. To capture this property, we use adaptive methods to generate static backbone graphs containing the primary information, and use temporal models to generate dynamic temporal graphs, and then fuse them to generate a backbone-based dynamic graph. To overcome potential limitations associated with recurrent structures, we introduce a linear model DLinear to handle temporal dependencies, and combine it with dynamic graph convolution for epidemic forecasting. Extensive experiments on two datasets demonstrate that BDSTGNN outperforms baseline models, and ablation comparison further verifies the effectiveness of model components. Furthermore, we analyze and measure the significance of backbone and temporal graphs by using information metrics from different aspects. Finally, we verify the superior efficiency of the BDSTGNN.
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Designing Network Topologies of Multiple Reservoir Echo State Networks: A Genetic Algorithm Based Approach 査読あり
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
2024 International Joint Conference on Neural Networks (IJCNN) 148 1 - 9 2024年06月
担当区分：最終著者, 責任著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：IEEE
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Reconstructive reservoir computing for anomaly detection in time-series signals 査読あり
Junya Kato, Gouhei Tanaka, Ryosho Nakane, Akira Hirose
Nonlinear Theory and Its Applications, IEICE 15 ( 1 ) 183 - 204 2024年01月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Institute of Electronics, Information and Communications Engineers (IEICE)
DOI： 10.1587/nolta.15.183
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Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing.
Shihori Koyama, Daisuke Inoue, Hiroaki Yoshida, Kazuyuki Aihara, Gouhei Tanaka
CoRR abs/2406.03061 2024年
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Diffusion model for relational inference.
Shuhan Zheng, Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
CoRR abs/2401.16755 2024年
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Dynamical Graph Echo State Networks with Snapshot Merging for Dissemination Process Classification. 査読あり
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
Lecture Notes in Computer Science (LNCS) 2023年11月
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Dynamical Graph Echo State Networks with Snapshot Merging for Spreading Process Classification 査読あり
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
Communications in Computer and Information Science 1964 CCIS 523 - 534 2023年11月
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス）
The Spreading Process Classification (SPC) is a popular application of temporal graph classification. The aim of SPC is to classify different spreading patterns of information or pestilence within a community represented by discrete-time temporal graphs. Recently, a reservoir computing-based model named Dynamical Graph Echo State Network (DynGESN) has been proposed for processing temporal graphs with relatively high effectiveness and low computational costs. Inspired by DynGESN, we propose a novel reservoir computing-based model called the Grouped Dynamical Graph Echo State Network (GDGESN) for dealing with SPC tasks. In this model, a novel augmentation strategy named the snapshot merging strategy is designed for forming new snapshots by merging neighboring snapshots over time, and then multiple reservoir encoders are set for capturing spatiotemporal features from merged snapshots. After those, the logistic regression is adopted for decoding the sum-pooled embeddings into the classification results. Experimental results on six benchmark SPC datasets show that our proposed model has better classification performances than the DynGESN and several kernel-based models.
DOI： 10.1007/978-981-99-8141-0_39
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2023-10.html#LiFT23
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An Echo State Network-Based Method for Identity Recognition with Continuous Blood Pressure Data 査読あり
Ziqiang Li, Kantaro Fujiwara, Gouhei Tanaka
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 14257 LNCS 13 - 25 2023年09月
担当区分：最終著者, 責任著者 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）
With the development of Continuous Blood Pressure (CBP) monitoring devices, we can collect real-time blood pressure non-invasively and accurately. Since CBP data can reflect the unique dynamical characteristics of the cardiovascular system for each person, it is reasonable to develop an identity recognition method based on these data. In this study, we propose an Echo State Network-based identity recognition method with CBP data. In the proposed method, we divide each CBP series data into several CBP segments. Then we use a Bi-directional Echo State Network to transform the input segments into high-dimensional reservoir states. Finally, we compute the identity recognition results in an aggregation mode. To evaluate the proposed method, we performed person identification tasks using ten sub-datasets sampled from a large-scale CBP dataset. Our proposed method achieved higher recognition accuracy than other relevant methods in spite of its relatively low computational cost on segment-by-segment and aggregated recognition tasks, respectively.
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Time-domain Fading Channel Prediction Based on Spin-wave Reservoir Computing. 査読あり
Jiaxuan Chen, Haotian Chen, Ryosho Nakane, Gouhei Tanaka, Akira Hirose
2023 International Joint Conference on Neural Networks 1 - 8 2023年08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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Film-penetrating transducers applicable to on-chip reservoir computing with spin waves
Jiaxuan Chen, Ryosho Nakane, Gouhei Tanaka, Akira Hirose
Journal of Applied Physics 132 ( 12 ) 2022年09月
掲載種別：研究論文（学術雑誌）
We have proposed a spin-wave transducer structure named film-penetrating transducers (FPTs). FPTs penetrate an on-chip magnetic film for a spin-wave transmission medium and allow flexible spatial arrangements of many exciters/detectors due to their zero-dimensional feature. We constructed four device models with different spatial arrangements of FPT/conventional exciters using a 10-nm-thick ferrimagnetic garnet film with a central FPT detector. We performed numerical experiments that combine electromagnetics with micromagnetics including thermal noise at 300 K. We evaluated important device features of FPTs, such as the signal-to-noise ratios (SNRs), input/output signal transmission efficiencies, and nonlinear phenomena of spin waves. We applied in-phase sinusoidal input currents with various amplitudes and frequencies and altered the damping strengths near the film boundaries. We obtained sufficient SNRs for the practical use of FPTs and revealed that FPTs have both higher transmission efficiencies and nonlinear strengths than conventional antennas, as the input frequency approaches the ferromagnetic resonance frequency of the film. Moreover, we observed and analyzed various nonlinear phenomena of spin waves, including beats in the time-domain waveform, components of integer harmonic frequencies, wide-range scatterings of inter-harmonic frequencies, and frequency doubling in spin precession. These characteristics probably originate from various device effects: FPTs effectively excite dipolar spin waves with large-angle precession, propagating spin waves reflect from the film boundaries, and spin waves dynamically and nonlinearly interfere with each other. This study demonstrated that FPTs have promising features for both their applications to reservoir computing and the studies on the physics of nonlinear and space-varying spin waves.
DOI： 10.1063/5.0102974
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Co-evolution dynamics of epidemic and information under dynamical multi-source information and behavioral responses
Xiao Hong, Yuexing Han, Gouhei Tanaka, Bing Wang
Knowledge-Based Systems 252 2022年09月
掲載種別：研究論文（学術雑誌）
In the absence of effective treatment programs and limited medical resources, multi-source information dynamically evolves with an epidemic and motivates people to adopt behavioral responses, which contributes much to reducing their infection risk and suppressing the epidemic spread. Here, we aim at studying the effects of dynamical multi-source information and behavioral responses on the co-evolution of epidemic and information in time-varying multiplex networks. We propose the UAU-SIS (Unaware–Aware–Unaware– Susceptible–Infected–Susceptible) model with time-varying self-awareness and behavioral responses. Under the framework of time-varying multiplex networks and with a Microscopic Markov Chain Approach (MMCA), we analytically derive the epidemic thresholds for the proposed model. Experimental results for artificial networks show that time-varying behavioral responses can effectively suppress the epidemic spread with an increased epidemic threshold, while time-varying self-awareness can only reduce the scale of epidemic spread. In addition, the role of dynamical multi-source information in suppressing epidemic spread is limited. When the information transmission rate is beyond a certain critical value or the information efficiency is low, it will no longer affect the epidemic spread. Detailed analysis on the co-evolution of epidemic and information has to consider the heterogeneity of individuals in obtaining multi-source information and taking behavioral responses. Only when many people can obtain multi-source information and take behavioral responses, time-varying self-awareness and behavioral responses have a great impact on suppressing epidemic spread. Furthermore, we apply our proposed framework to two typical real-world networks and find that the results on real-world networks are consistent with those on artificial networks. Thus, the proposed method is expected to provide helpful guidance for coping with the COVID-19 or future emerging epidemics.
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Simulation platform for pattern recognition based on reservoir computing with memristor networks
Gouhei Tanaka, Ryosho Nakane
SCIENTIFIC REPORTS 12 ( 1 ) 2022年06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 PORTFOLIO
Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward the realization of energy-efficient machine learning hardware.
DOI： 10.1038/s41598-022-13687-z
その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr2112.html#abs-2112-00248
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Computational Efficiency of Multi-Step Learning Echo State Networks for Nonlinear Time Series Prediction. 査読あり
Takanori Akiyama, Gouhei Tanaka
IEEE Access 10 28535 - 28544 2022年
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Multi-reservoir echo state networks with sequence resampling for nonlinear time-series prediction. 査読あり
Ziqiang Li, Gouhei Tanaka
Neurocomputing 467 115 - 129 2022年
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2022 roadmap on neuromorphic computing and engineering. 査読あり
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H. P. Pernice, Harish Bhaskaran, Steve B. Furber, Emre Neftci, Franz Scherr, Wolfgang Maass 0001, Srikanth Ramaswamy, Jonathan Tapson, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shih-Chii Liu, Gabriella Panuccio, Mufti Mahmud, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds
Neuromorphic Computing and Engineering 2 ( 2 ) 22501 - 22501 2022年
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Spin waves propagating through a stripe magnetic domain structure and their applications to reservoir computing 査読あり
Ryosho Nakane, Akira Hirose, Gouhei Tanaka
Physical Review Research 3 ( 3 ) 2021年09月
担当区分：最終著者 掲載種別：研究論文（学術雑誌）
Spin waves propagating through a stripe domain structure and reservoir computing with their spin dynamics have been numerically studied, focusing on the relation between physical phenomena and computing capabilities. Our system utilizes a spin-wave-based device that has a continuous magnetic garnet film and one-input/72-output electrodes on top. To control spatially distributed spin dynamics, a stripe magnetic domain structure and amplitude-modulated triangular input waves were used. The spatially arranged electrodes detected spin vector outputs with various nonlinear characteristics that were leveraged for reservoir computing. By moderately suppressing nonlinear phenomena, our system achieves 100% prediction accuracy in temporal exclusive-OR problems with a delay step up to 5. At the same time, it shows perfect inference in delay tasks with a delay step more than 7 and its memory capacity has a maximum value of 21. This study demonstrated that our spin-wave-based reservoir computing has a high potential for edge-computing applications and also can offer a rich opportunity for further understanding the underlying nonlinear physics.
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A Numerical Exploration of Signal Detector Arrangement in a Spin-Wave Reservoir Computing Device. 査読あり
Takehiro Ichimura, Ryosho Nakane, Gouhei Tanaka, Akira Hirose
IEEE Access 9 72637 - 72646 2021年
掲載種別：研究論文（学術雑誌）
This paper studies numerically how the signal detector arrangement influences the performance of reservoir computing using spin waves excited in a ferrimagnetic garnet film. This investigation is essentially important since the input information is not only conveyed but also transformed by the spin waves into high-dimensional information space when the waves propagate in the film in a spatially distributed manner. This spatiotemporal dynamics realizes a rich reservoir-computational functionality. First, we simulate spin waves in a rectangular garnet film with two input electrodes to obtain spatial distributions of the reservoir states in response to input signals, which are represented as spin vectors and used for a machine-learning waveform classification task. The detected reservoir states are combined through readout connection weights to generate a final output. We visualize the spatial distribution of the weights after training to discuss the number and positions of the output electrodes by arranging them at grid points, equiangularly circular points or at random. We evaluate the classification accuracy by changing the number of the output electrodes, and find that a high accuracy (>90%) is achieved with only several tens of output electrodes regardless of grid, circular or random arrangement. These results suggest that the spin waves possess sufficiently complex and rich dynamics for this type of tasks. Then we investigate in which area useful information is distributed more by arranging the electrodes locally on the chip. Finally, we show that this device has generalization ability for input wave-signal frequency in a certain frequency range. These results will lead to practical design of spin-wave reservoir devices for low-power intelligent computing in the near future.
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A Multi-Reservoir Echo State Network with Multiple-Size Input Time Slices for Nonlinear Time-Series Prediction. 査読あり
Ziqiang Li, Gouhei Tanaka
ICONIP (2) 13109 LNCS 28 - 39 2021年
担当区分：最終著者, 責任著者 掲載種別：研究論文（国際会議プロシーディングス）
A novel multi-reservoir echo state network incorporating the scheme of extracting features from multiple-size input time slices is proposed in this paper. The proposed model, Multi-size Input Time Slices Echo State Network (MITSESN), uses multiple reservoirs, each of which extracts features from each of the multiple input time slices of different sizes. We compare the prediction performances of MITSESN with those of the standard echo state network and the grouped echo state network on three benchmark nonlinear time-series datasets to show the effectiveness of our proposed model. Moreover, we analyze the richness of reservoir dynamics of all the tested models and find that our proposed model can generate temporal features with less linear redundancies under the same parameter settings, which provides an explanation about why our proposed model can outperform the other models to be compared on the nonlinear time-series prediction tasks.
DOI： 10.1007/978-3-030-92270-2_3
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2021-2.html#LiT21
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2021 Roadmap on Neuromorphic Computing and Engineering.
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano, Carlo Ricciardi, Shi-Jun Liang, Feng Miao, Mario Lanza, Tyler J. Quill, Scott T. Keene, Alberto Salleo, Julie Grollier, Danijela Markovic, Alice Mizrahi, Peng Yao, J. Joshua Yang, Giacomo Indiveri, John Paul Strachan, Suman Datta, Elisa Vianello, Alexandre Valentian, Johannes Feldmann, Xuan Li, Wolfram H. P. Pernice, Harish Bhaskaran, Emre Neftci, Srikanth Ramaswamy, Jonathan Tapson, Franz Scherr, Wolfgang Maass 0001, Priyadarshini Panda, Youngeun Kim, Gouhei Tanaka, Simon Thorpe, Chiara Bartolozzi, Thomas A. Cleland, Christoph Posch, Shih-Chii Liu, Arnab Neelim Mazumder, Morteza Hosseini, Tinoosh Mohsenin, Elisa Donati, Silvia Tolu, Roberto Galeazzi, Martin Ejsing Christensen, Sune Holm, Daniele Ielmini, N. Pryds
CoRR abs/2105.05956 2021年
掲載種別：研究論文（学術雑誌）
その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr2105.html#abs-2105-05956
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partial-FORCE: A fast and robust online training method for recurrent neural networks. 査読あり
Hiroto Tamura, Gouhei Tanaka
International Joint Conference on Neural Networks(IJCNN) 2021-July 1 - 8 2021年
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：IEEE
Recurrent neural networks (RNNs) are helpful tools for modeling dynamical systems by neuronal populations, but efficiently training RNNs has been a challenging topic. In recent years, a recursive least squares (RLS) based method for modifying all the recurrent connections, called the full-Force method, has been gaining attention as a fast and robust online training rule. This method introduces a second network (called the teacher reservoir) during training to provide suitable target dynamics to all the hidden units of the task-performing network (called the student network). Thanks to the RLS-based approach, the full-FORCE method can be applied to training continuous-time networks and spiking neural networks. In this study, we propose a generalized version of the full-FORCE method: the partial-FORCE method. In the proposed method, only part of the student network neurons (called supervised neurons) is supervised by only part of the teacher reservoir neurons (called supervising neurons). As a result of this relaxation, the size of the student network and that of the teacher reservoir can be different, which is biologically plausible as a possible model of the memory transfer in the brain. Furthermore, we numerically show that the partial-FORCE method converges faster and is more robust against variations in parameter values and initial conditions than the full-FORCE method, even without the price of computational cost.
DOI： 10.1109/IJCNN52387.2021.9533964
その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2021.html#TamuraT21
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Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models. 査読あり
Hiroto Tamura, Gouhei Tanaka
Neural Networks 143 550 - 563 2021年
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Reservoir Computing with Diverse Timescales for Prediction of Multiscale Dynamics.
Gouhei Tanaka, Tadayoshi Matsumori, Hiroaki Yoshida, Kazuyuki Aihara
CoRR abs/2108.09446 2021年
担当区分：筆頭著者 掲載種別：研究論文（学術雑誌）
その他リンク： https://dblp.uni-trier.de/db/journals/corr/corr2108.html#abs-2108-09446
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Processing-Response Dependence on the On-Chip Readout Positions in Spin-Wave Reservoir Computing. 査読あり
Takehiro Ichimura, Ryosho Nakane, Akira Hirose
Neural Information Processing - 28th International Conference 296 - 307 2021年
掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Springer
DOI： 10.1007/978-3-030-92238-2_25
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2021-3.html#IchimuraNH21
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Network structure-based interventions on spatial spread of epidemics in metapopulation networks 査読あり
Bing Wang, Min Gou, YiKe Guo, Gouhei Tanaka, Yuexing Han
PHYSICAL REVIEW E 102 ( 6 ) 2020年12月
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
Mathematical modeling of epidemics is fundamental to understand the mechanism of the disease outbreak and provides helpful indications for effectiveness of interventions for policy makers. The metapopulation network model has been used in the analysis of epidemic dynamics by taking individual migration between patches into account. However, so far, most of the previous studies unrealistically assume that transmission rates within patches are the same, neglecting the nonuniformity of intervention measures in hindering epidemics. Here, based on the assumption that interventions deployed in a patch depend on its population size or economic level, which have shown a positive correlation with the patch's degree in networks, we propose a metapopulation network model to explore a network structure-based intervention strategy, aiming at understanding the interplay between intervention strategy and other factors including mobility patterns, initial population, as well as the network structure. Our results demonstrate that interventions to patches with different intensity are able to suppress the epidemic spreading in terms of both the epidemic threshold and the final epidemic size. Specifically, the intervention strategy targeting the patches with high degree is able to efficiently suppress epidemics. In addition, a detrimental effect is also observed depending on the interplay between the intervention measures and the initial population distribution. Our study opens a path for understanding epidemic dynamics and provides helpful insights into the implementation of countermeasures for the control of epidemics in reality.
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Comparing catch-up vaccination programs based on analysis of 2012-13 rubella outbreak in Kawasaki City, Japan 査読あり 国際誌
Chiyori T. Urabe, Gouhei Tanaka, Takahiro Oshima, Aya Maruyama, Takako Misaki, Nobuhiko Okabe, Kazuyuki Aihara
PLOS ONE 15 ( 8 ) e0237312 2020年08月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：PUBLIC LIBRARY SCIENCE
During the 2012-13 rubella outbreak in Japan, local governments implemented subsidy programs for catch-up vaccination to mitigate the rubella outbreak and prevent congenital rubella syndrome (CRS). In most local governments, to prevent CRS, eligible persons of the subsidy program were women who were planning to have a child and men who were partners of pregnant women. On the other hand, in Kawasaki City, unimmunized men aged 23-39 years were additionally included in the eligible persons, because they were included in an unimmunized men group resulting from the historical transition of the national routine vaccination in Japan. The number of rubella cases in the city decreased earlier than that in the whole Japan. First, in order to estimate the effect of the catch-up vaccination campaign in Kawasaki City on the epidemic outcome, we performed numerical simulations with a Susceptible-Vaccinated-Exposed-Infectious-Recovered (SVEIR) model incorporating real data. The result indicated that the catch-up vaccination campaign showed a beneficial impact on the early decay of the rubella cases. Second, we numerically compared several different implementation strategies of catch-up vaccinations under a fixed amount of total vaccinations. As a result, we found that early and intensive vaccinations are vital for significant reduction in the number of rubella cases and CRS occurrences. Our study suggests that mathematical models with epidemiological and social data can contribute to identifying the most effective vaccination strategy.
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HP-ESN: Echo State Networks Combined with Hodrick-Prescott Filter for Nonlinear Time-Series Prediction. 査読あり
Ziqiang Li, Gouhei Tanaka
2020 International Joint Conference on Neural Networks(IJCNN) 1 - 9 2020年
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：IEEE
Nonlinear time-series prediction is one of the challenging tasks in machine learning. Recurrent neural networks and their variants have been successful in such a task owing to its ability of storing past inputs in their dynamical states. Echo state networks (ESNs) are a special type of recurrent neural networks, which are capable of high-speed learning. To develop this computational scheme, we propose an HP-ESN method which combines ESNs with a preprocessing based on the Hodrick- Prescott (HP) filter. This filter extracts different components from a single time-series data. The extracted components are processed by ESNs. We show that the proposed method yields better prediction performance compared with other state-of- the-art ESN-based methods in prediction tasks with real-world time-series data. We also demonstrate that the computational performance depends on the setting of the smoothing parameter and the number of decompositions by the HP filter.
DOI： 10.1109/IJCNN48605.2020.9206771
その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#LiT20
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Estimation and prediction of ellipsoidal molecular shapes in organic crystals based on ellipsoid packing. 査読あり 国際誌
Daiki Ito, Raku Shirasawa, Yoichiro Iino, Shigetaka Tomiya, Gouhei Tanaka
PloS one 15 ( 9 ) e0239933 2020年
担当区分：最終著者, 責任著者 記述言語：英語 掲載種別：研究論文（学術雑誌）
Crystal structure prediction has been one of the fundamental and challenging problems in materials science. It is computationally exhaustive to identify molecular conformations and arrangements in organic molecular crystals due to complexity in intra- and inter-molecular interactions. From a geometrical viewpoint, specific types of organic crystal structures can be characterized by ellipsoid packing. In particular, we focus on aromatic systems which are important for organic semiconductor materials. In this study, we aim to estimate the ellipsoidal molecular shapes of such crystals and predict them from single molecular descriptors. First, we identify the molecular crystals with molecular centroid arrangements that correspond to affine transformations of four basic cubic lattices, through topological analysis of the dataset of crystalline polycyclic aromatic molecules. The novelty of our method is that the topological data analysis is applied to arrangements of molecular centroids intead of those of atoms. For each of the identified crystals, we estimate the intracrystalline molecular shape based on the ellipsoid packing assumption. Then, we show that the ellipsoidal shape can be predicted from single molecular descriptors using a machine learning method. The results suggest that topological characterization of molecular arrangements is useful for structure prediction of organic semiconductor materials.
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Deep Echo State Networks with Multi-Span Features for Nonlinear Time Series Prediction. 査読あり
Ziqiang Li, Gouhei Tanaka
2020 International Joint Conference on Neural Networks(IJCNN) 1 - 9 2020年
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：IEEE
Nonlinear time-series prediction is one of the challenging topics in machine learning due to complex non-stationarity in the temporal dynamics. Many recurrent neural network models have been proposed for enhancing the prediction accuracy in time-series prediction tasks. Echo state networks (ESNs) are a variant of recurrent neural networks, which have great potential for addressing machine learning tasks with a very low learning cost. However, the existing ESN-based models have used only single-span features to our best knowledge. In this study, we propose two deep ESN models incorporating multi-span features to improve the prediction performance. We show that the two deep ESN models yield better prediction performance compared to the other state-of-the-art ESN-based methods in benchmark time-series prediction tasks with three models: the Lorenz system, the Mackey-Glass system, and the NARMA-10 system. Our analyses illustrate that deeper structures decrease the multicollinearity of the extracted features and thus contribute to improved performance. The presented results suggest that the proposed models contribute to the development of artificial intelligence for temporal information processing.
DOI： 10.1109/IJCNN48605.2020.9207401
その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#LiT20a
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Time delay reservoir computing with VCSEL
Jean Benoit Héroux, Gouhei Tanaka, Toshiyuki Yamane, Naoki Kanazawa, Ryosho Nakane, Hidetoshi Numata, Seiji Takeda, Akira Hirose, Daiju Nakano
Proceedings of SPIE - The International Society for Optical Engineering 11299 2020年
掲載種別：研究論文（国際会議プロシーディングス）
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Neural networks in which the interconnections between the nodes are randomly aßigned are promising for the realization of neuromorphic devices in which the resource requirements for training are lower than for a fully deterministic system. Reservoir computing is a claß of recurrent network for which the input and internal weights are random and fixed over time, and only the output weights are trained via a linear regreßion. In this work, we review the recent work on photonic reservoirs and describe our recent results on the implementation of a single node system based on multi-mode optical interconnect technology developed for high channel density and low power data transfer applications. We discuß the potential advantages of this approach for the realization of a photonic cluster of reservoirs.
DOI： 10.1117/12.2544981
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Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing. 査読あり
Hiroto Tamura, Gouhei Tanaka
Artificial Neural Networks and Machine Learning - ICANN 2020 - 29th International Conference on Artificial Neural Networks 12397 LNCS 459 - 469 2020年
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Springer
Reservoir computing devices are promising as energy-efficient machine learning hardware for real-time information processing. However, some online algorithms for reservoir computing are not simple enough for hardware implementation. In this study, we focus on the first order reduced and controlled error (FORCE) algorithm for online learning with reservoir computing models. We propose a two-step FORCE algorithm by simplifying the operations in the FORCE algorithm, which can reduce necessary memories. We analytically and numerically show that the proposed algorithm can converge faster than the original FORCE algorithm.
DOI： 10.1007/978-3-030-61616-8_37
その他リンク： https://dblp.uni-trier.de/db/conf/icann/icann2020-2.html#TamuraT20
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Keynote Speech: Information processing hardware, physical reservoir computing and complex-valued neural networks
Akira Hirose, Ryosho Nakane, Gouhei Tanaka
IMFEDK 2019 - International Meeting for Future of Electron Devices, Kansai 19 - 24 2019年11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） 出版者・発行元：Institute of Electrical and Electronics Engineers Inc.
First we discuss the essence of neural networks, which are the bases of modern artificial intelligence (AI), to examine the relationship between the neural fundamental framework and the present hardware. Then, in this context, we review reservoir computing and complex-valued neural networks.
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Intervention threshold for epidemic control in susceptible-infected-recovered metapopulation models. 査読あり 国際誌
Akari Matsuki, Gouhei Tanaka
Physical review. E 100 ( 2-1 ) 022302 - 022302 2019年08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）
Metapopulation epidemic models describe epidemic dynamics in networks of spatially distant patches connected via pathways for migration of individuals. In the present study, we deal with a susceptible-infected-recovered (SIR) metapopulation model where the epidemic process in each patch is represented by an SIR model and the mobility of individuals is assumed to be a homogeneous diffusion. We consider two types of patches including high-risk and low-risk ones under the assumption that a local patch is changed from a high-risk one to a low-risk one by an intervention. We theoretically analyze the intervention threshold which indicates the critical fraction of low-risk patches for preventing a global epidemic outbreak. We show that an intervention targeted to high-degree patches is more effective for epidemic control than a random intervention. The theoretical results are validated by Monte Carlo simulations for synthetic and realistic scale-free patch networks. The theoretical results also reveal that the intervention threshold depends on the human mobility network and the mobility rate. Our approach is useful for exploring better local interventions aimed at containment of epidemics.
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Hybrid pooling for enhancement of generalization ability in deep convolutional neural networks 査読あり
Zhiqiang Tong, Gouhei Tanaka
NEUROCOMPUTING 333 76 - 85 2019年03月
担当区分：最終著者, 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
Convolutional neural networks (CNNs) have attracted considerable attention in many application fields for their great ability to deal with image recognition and object detection tasks. A pooling process is an important process in CNNs, which serves to decrease the dimensionality of processed data for reducing computational cost as well as for enhancing tolerance to translation and noise. Although standard pooling methods, such as the max pooling and the average pooling, are typically adopted in many studies, a newly devised pooling method could improve the generalization ability of CNNs. In this study, we propose a hybrid pooling method which stochastically chooses the max pooling or the average pooling in each pooling layer. A characteristic of the hybrid pooling is that the probability for choosing one of the two pooling methods can be controlled for each convolutional layer. In image classification tasks with benchmark datasets, we show that the hybrid pooling is effective for increasing the generalization ability of CNNs. Moreover, we demonstrate that the hybrid pooling combined with the dropout is competitive with other existing methods in classification performance. (C) 2018 Elsevier B.V. All rights reserved.
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Analysis on Characteristics of Multi-Step Learning Echo State Networks for Nonlinear Time Series Prediction. 査読あり
Takanori Akiyama, Gouhei Tanaka
International Joint Conference on Neural Networks(IJCNN) 1 - 8 2019年
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：IEEE
DOI： 10.1109/IJCNN.2019.8851876
その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2019.html#AkiyamaT19
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In a Spin-Wave Reservoir for Machine Learning. 査読あり
Ryosho Nakane, Gouhei Tanaka, Akira Hirose
International Joint Conference on Neural Networks(IJCNN) 1 - 9 2019年
掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：IEEE
DOI： 10.1109/IJCNN.2019.8852280
その他リンク： https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2019.html#NakaneTH19
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Echo State Networks Composed of Units with Time-Varying Nonlinearity. 査読あり
Gouhei Tanaka, Ryosho Nakane, Akira Hirose
Aust. J. Intell. Inf. Process. Syst. 17 ( 2 ) 34 - 39 2019年
担当区分：筆頭著者 掲載種別：研究論文（学術雑誌）
その他リンク： https://dblp.uni-trier.de/db/journals/ajiips/ajiips17.html#TanakaNH19
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Echo State Network with Adversarial Training. 査読あり
Takanori Akiyama, Gouhei Tanaka
Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings - Workshop and Special Sessions 82 - 88 2019年
担当区分：最終著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Springer
DOI： 10.1007/978-3-030-30493-5_8
その他リンク： https://dblp.uni-trier.de/db/conf/icann/icann2019w.html#AkiyamaT19
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Application Identification of Network Traffic by Reservoir Computing 査読あり
Toshiyuki Yamane, Jean Benoit Heroux, Hidetoshi Numata, Gouhei Tanaka, Ryosho Nakane, Akira Hirose
NEURAL INFORMATION PROCESSING, ICONIP 2019, PT V 1143 389 - 396 2019年
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） 出版者・発行元：SPRINGER INTERNATIONAL PUBLISHING AG
We propose a method for application identification for network traffic by reservoir computing. Different from conventional approaches, the proposed method handles traffic flows as dynamical time series data and enables fast and real-time identification. We apply the proposed method to real traffic data and show that high identification accuracy is achieved. We also discuss an implementation as physical reservoirs based on optics and the impact of the proposed method to 5G networking.
DOI： 10.1007/978-3-030-36802-9_41
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2019-5.html#YamaneHNTNH19
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Physical reservoir computing: Possibility to resolve the inconsistency between neuro-AI principles and its hardware. 査読あり
Akira Hirose, Seiji Takeda, Toshiyuki Yamane, Hidetoshi Numata, Naoki Kanazawa, Jean Benoit Héroux, Daiju Nakano, Ryosho Nakane, Gouhei Tanaka
Aust. J. Intell. Inf. Process. Syst. 16 ( 4 ) 49 - 55 2019年
担当区分：最終著者 掲載種別：研究論文（学術雑誌）
その他リンク： https://dblp.uni-trier.de/db/journals/ajiips/ajiips16.html#HiroseTYNKHNNT19
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Bifurcation mechanism for emergence of spontaneous oscillations in coupled heterogeneous excitable units 査読あり
Morino Kai, Tanaka Gouhei, Aihara Kazuyuki
PHYSICAL REVIEW E 98 ( 5 ) 2018年11月
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Recent Advances in Physical Reservoir Computing: A Review. 査読あり
Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit, Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose
CoRR abs/1808.04962 2018年08月
掲載種別：研究論文（学術雑誌）
Reservoir computing is a computational framework suited for<br />
temporal/sequential data processing. It is derived from several recurrent<br />
neural network models, including echo state networks and liquid state machines.<br />
A reservoir computing system consists of a reservoir for mapping inputs into a<br />
high-dimensional space and a readout for extracting features of the inputs.<br />
Further, training is carried out only in the readout. Thus, the major advantage<br />
of reservoir computing is fast and simple learning compared to other recurrent<br />
neural networks. Another advantage is that the reservoir can be realized using<br />
physical systems, substrates, and devices, instead of recurrent neural<br />
networks. In fact, such physical reservoir computing has attracted increasing<br />
attention in various fields of research. The purpose of this review is to<br />
provide an overview of recent advances in physical reservoir computing by<br />
classifying them according to the type of the reservoir. We discuss the current<br />
issues and perspectives related to physical reservoir computing, in order to<br />
further expand its practical applications and develop next-generation machine<br />
learning systems. -
Bifurcation analysis of a mathematical model of atopic dermatitis to determine patient-specific effects of treatments on dynamic phenotypes. 査読あり 国際誌
Gouhei Tanaka, Elisa Domínguez-Hüttinger, Panayiotis Christodoulides, Kazuyuki Aihara, Reiko J Tanaka
Journal of theoretical biology 448 66 - 79 2018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：Academic Press
Atopic dermatitis (AD) is a common inflammatory skin disease, whose incidence is currently increasing worldwide. AD has a complex etiology, involving genetic, environmental, immunological, and epidermal factors, and its pathogenic mechanisms have not yet been fully elucidated. Identification of AD risk factors and systematic understanding of their interactions are required for exploring effective prevention and treatment strategies for AD. We recently developed a mathematical model for AD pathogenesis to clarify mechanisms underlying AD onset and progression. This model describes a dynamic interplay between skin barrier, immune regulation, and environmental stress, and reproduced four types of dynamic behaviour typically observed in AD patients in response to environmental triggers. Here, we analyse bifurcations of the model to identify mathematical conditions for the system to demonstrate transitions between different types of dynamic behaviour that reflect respective severity of AD symptoms. By mathematically modelling effects of topical application of antibiotics, emollients, corticosteroids, and their combinations with different application schedules and doses, bifurcation analysis allows us to mathematically evaluate effects of the treatments on improving AD symptoms in terms of the patients' dynamic behaviour. The mathematical method developed in this study can be used to explore and improve patient-specific personalised treatment strategies to control AD symptoms.
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Dimensionality Reduction by Reservoir Computing and Its Application to IoT Edge Computing 査読あり
Toshiyuki Yamane, Hidetoshi Numata, Jean Benoit Heroux, Naoki Kanazawa, Seiji Takeda, Gouhei Tanaka, Ryosho Nakane, Akira Hirose, Daiju Nakano
NEURAL INFORMATION PROCESSING (ICONIP 2018), PT I 11301 635 - 643 2018年
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） 出版者・発行元：SPRINGER INTERNATIONAL PUBLISHING AG
We propose a method of dimension reduction of high dimensional time series data by reservoir computing. The proposed method is a generalization of random projection techniques to time series, which uses a reservoir smaller than input time series. We demonstrate the method by echo state networks for artificially generated time series data. We also discuss an implementation as physical reservoirs and its application of the proposed method to IoT edge computing, which is the first proposal for industry application of physical reservoir computing beyond standard benchmark tasks.
DOI： 10.1007/978-3-030-04167-0_58
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2018-1.html#YamaneNHKTTNHN18
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Prediction of Molecular Packing Motifs in Organic Crystals by Neural Graph Fingerprints. 査読あり
Daiki Ito, Raku Shirasawa, Shinnosuke Hattori, Shigetaka Tomiya, Gouhei Tanaka
Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part V 26 - 34 2018年
担当区分：最終著者, 責任著者 掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Springer
DOI： 10.1007/978-3-030-04221-9_3
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2018-5.html#ItoSHTT18
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Reservoir Computing with Untrained Convolutional Neural Networks for Image Recognition 査読あり
Zhiqiang Tong, Gouhei Tanaka
2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) 1289 - 1294 2018年
担当区分：最終著者, 責任著者 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） 出版者・発行元：IEEE
Reservoir computing has attracted much attention for its easy training process as well as its ability to deal with temporal data. A reservoir computing system consists of a reservoir part represented as a sparsely connected recurrent neural network and a readout part represented as a simple regression model. In machine learning tasks, the reservoir part is fixed and only the readout part is trained. Although reservoir computing has been mainly applied to time series prediction and recognition, it can be applied to image recognition as well by considering an image data as a sequence of pixel values. However, to achieve a high performance in image recognition with raw image data, a large-scale reservoir including a large number of neurons is required. This is a bottleneck in terms of computer memory and computational cost. To overcome this bottleneck, we propose a new method which combines reservoir computing with untrained convolutional neural networks. We use an untrained convolutional neural network to transform raw image data into a set of smaller feature maps in a preprocessing step of the reservoir computing. We demonstrate that our method achieves a high classification accuracy in an image recognition task with a much smaller number of trainable parameters compared with a previous study.
DOI： 10.1109/ICPR.2018.8545471
その他リンク： https://dblp.uni-trier.de/db/conf/icpr/icpr2018.html#TongT18
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Reservoir Computing With Spin Waves Excited in a Garnet Film. 査読あり
Ryosho Nakane, Gouhei Tanaka, Akira Hirose
IEEE Access 6 4462 - 4469 2018年
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：Institute of Electrical and Electronics Engineers Inc.
We propose a reservoir computing device utilizing spin waves that propagate in a garnet film equipped with multiple input/output electrodes. In recent years, reservoir computing has been expected to realize energy-efficient and/or high-speed machine learning. Our proposed device enhances such significant merits in a hardware approach. It utilizes the nonlinear interference of history-dependent asymmetrically propagating spin waves excited by the magneto-electric effect. First, we investigate a feasible device structure with practical physical parameters in micromagnetic numerical analysis, and show the detailed characteristics of the forward volume magnetostatic spin waves. Then, we demonstrate high generalization ability in the estimation of input-signal parameters performed by the spin-wave-based reservoir computing. We find that the hysteresis characteristics of the spin waves propagating asymmetrically with respect to excitation points, as well as the nonlinear interference, works advantageously to realize high diversity in the time-sequential signals in high-dimensional information space, which has the highest significance for effective learning in reservoir computing. The spin wave device is highly promising for next-generation machine-learning electronics.
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Proposal of Carrier-Wave Reservoir Computing. 査読あり
Akira Hirose, Gouhei Tanaka, Seiji Takeda, Toshiyuki Yamane, Hidetoshi Numata, Naoki Kanazawa, Jean Benoit Héroux, Daiju Nakano, Ryosho Nakane
Neural Information Processing - 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 616 - 624 2018年
掲載種別：研究論文（国際会議プロシーディングス） 出版者・発行元：Springer
DOI： 10.1007/978-3-030-04167-0_56
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2018-1.html#HiroseTTYNKHNN18
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Robustness of coupled oscillator networks with heterogeneous natural frequencies 査読あり 国際誌
Tianyu Yuan, Gouhei Tanaka
CHAOS 27 ( 12 ) 123105 - 123105 2017年12月
担当区分：最終著者, 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 INST PHYSICS
Robustness of coupled oscillator networks against local degradation of oscillators has been intensively studied in this decade. The oscillation behavior on the whole network is typically reduced with an increase in the fraction of degraded (inactive) oscillators. The critical fraction of inactive oscillators, at which a transition from an oscillatory to a quiescent state occurs, has been used as a measure for the network robustness. The larger (smaller) this measure is, the more robust (fragile) the oscillatory behavior on the network is. Most previous studies have used oscillators with identical natural frequencies, for which the oscillators are necessarily synchronized and thereby the analysis is simple. In contrast, we focus on the effect of heterogeneity in the natural frequencies on the network robustness. First, we analytically derive the robustness measure for the coupled oscillator models with heterogeneous natural frequencies under some conditions. Then, we show that increasing the heterogeneity in natural frequencies makes the network fragile. Moreover, we discuss the optimal parameter condition to maximize the network robustness.
DOI： 10.1063/1.4991742
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Interplay between epidemic spread and information propagation on metapopulation networks 査読あり 国際誌
Bing Wang, Yuexing Han, Gouhei Tanaka
JOURNAL OF THEORETICAL BIOLOGY 420 18 - 25 2017年05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） 出版者・発行元：ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
The spread of an infectious disease has been widely found to evolve with the propagation of information. Many seminal works have demonstrated the impact of information propagation on the epidemic spreading, assuming that individuals are static and no mobility is involved. Inspired by the recent observation of diverse mobility patterns, we incorporate the information propagation into a metapopulation model based on the mobility patterns and contagion process, which significantly alters the epidemic threshold. In more details, we find that both the information efficiency and the mobility patterns have essential impacts on the epidemic spread. We obtain different scenarios leading to the mitigation of the outbreak by appropriately integrating the mobility patterns and the information efficiency as well. The inclusion of the impacts of the information propagation into the epidemiological model is expected to provide an support to public health implications for the suppression of epidemics.
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Smoothing effect for spatially distributed renewable resources and its impact on power grid robustness 査読あり 国際誌
Motoki Nagata, Yoshito Hirata, Naoya Fujiwara, Gouhei Tanaka, Hideyuki Suzuki, Kazuyuki Aihara
27 ( 3 ) 033104 - 033104 2017年03月
記述言語：英語 掲載種別：研究論文（学術雑誌）
In this paper, we show that spatial correlation of renewable energy outputs<br />
greatly influences the robustness of power grids. First, we propose a new index<br />
for the spatial correlation among renewable energy outputs. We find that the<br />
spatial correlation of renewable energy outputs in a short time-scale is as<br />
weak as that caused by independent random variables and that in a long<br />
time-scale is as strong as that under perfect synchronization. Then, by<br />
employing the topology of the power grid in eastern Japan, we analyze the<br />
robustness of the power grid with spatial correlation of renewable energy<br />
outputs. The analysis is performed by using a realistic differential-algebraic<br />
equations model and the result shows that the spatial correlation of the energy<br />
resources strongly degrades the robustness of the power grid. Our result<br />
suggests that the spatial correlation of the renewable energy outputs should be<br />
taken into account when estimating the stability of power grids.DOI： 10.1063/1.4977510
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Robustness and fragility in coupled oscillator networks under targeted attacks 査読あり 国際誌
Tianyu Yuan, Kazuyuki Aihara, Gouhei Tanaka
PHYSICAL REVIEW E 95 ( 1 ) 012315 - 012315 2017年01月
担当区分：最終著者, 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
The dynamical tolerance of coupled oscillator networks against local failures is studied. As the fraction of failed oscillator nodes gradually increases, the mean oscillation amplitude in the entire network decreases and then suddenly vanishes at a critical fraction as a phase transition. This critical fraction, widely used as a measure of the network robustness, was analytically derived for random failures but not for targeted attacks so far. Here we derive the general formula for the critical fraction, which can be applied to both random failures and targeted attacks. We consider the effects of targeting oscillator nodes based on their degrees. First we deal with coupled identical oscillators with homogeneous edge weights. Then our theory is applied to networks with heterogeneous edge weights and to those with nonidentical oscillators. The analytical results are validated by numerical experiments. Our results reveal the key factors governing the robustness and fragility of oscillator networks.
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Complex-Valued Neural Networks for Wave-Based Realization of Reservoir Computing. 査読あり
Akira Hirose, Seiji Takeda, Toshiyuki Yamane, Daiju Nakano, Shigeru Nakagawa, Ryosho Nakane, Gouhei Tanaka
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10637 449 - 456 2017年
担当区分：最終著者 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） 出版者・発行元：Springer Verlag
In this paper, we discuss the significance of complex-valued neural-network (CVNN) framework in energy-efficient neural networks, in particular in wave-based reservoir networks. Physical-wave reservoir networks are highly enhanced by CVNNs. From this viewpoint, we also compare the features of reservoir computing and other architectures.
DOI： 10.1007/978-3-319-70093-9_47
その他リンク： https://dblp.uni-trier.de/db/conf/iconip/iconip2017-4.html#HiroseTYNNNT17
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Simulation Study of Physical Reservoir Computing by Nonlinear Deterministic Time Series Analysis 査読あり
Toshiyuki Yamane, Seiji Takeda, Daiju Nakano, Gouhei Tanaka, Ryosho Nakane, Akira Hirose, Shigeru Nakagawa
NEURAL INFORMATION PROCESSING, ICONIP 2017, PT I 10634 639 - 647 2017年
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） 出版者・発行元：SPRINGER INTERNATIONAL PUBLISHING AG
We investigate dynamics of physical reservoir computing by numerical simulations. Our approach is based on nonlinear deterministic time series analysis such as Takens’ theorem and false nearest neighbor methods. We show that this approach is useful for efficient design and implementation of physical reservoir computing systems where only partial information of the reservoir state is accessible. We take nonlinear laser dynamics subject to time delay as physical reservoir and show that the size of physical reservoir can be estimated by these method.
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Waveform Classification by Memristive Reservoir Computing 査読あり
Gouhei Tanaka, Ryosho Nakane, Toshiyuki Yamane, Seiji Takeda, Daiju Nakano, Shigeru Nakagawa, Akira Hirose
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10637 457 - 465 2017年
担当区分：筆頭著者, 最終著者 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） 出版者・発行元：Springer Verlag
Reservoir computing is one of the computational frameworks based on recurrent neural networks for learning sequential data. We study the memristive reservoir computing where a network of memristors, instead of recurrent neural networks, provides a nonlinear mapping from input sequential signals to high-dimensional spatiotemporal dynamics. First we formulate the circuit equations of the memristive networks and describe the simulation methods. Then we use the memristive reservoir computing for solving a waveform classification problem. We demonstrate how the classification ability depends on the number of reservoir outputs and the variability of the memristive elements. Our methods are useful for finding a better architecture of the memristive reservoir under the inevitable element variability when implemented with nano/micro-scale devices.
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Parameter Scaling for Epidemic Size in a Spatial Epidemic Model with Mobile Individuals 査読あり 国際誌
Chiyori T. Urabe, Gouhei Tanaka, Kazuyuki Aihara, Masayasu Mimura
PLOS ONE 11 ( 12 ) e0168127 2016年12月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：PUBLIC LIBRARY SCIENCE
In recent years, serious infectious diseases tend to transcend national borders and widely spread in a global scale. The incidence and prevalence of epidemics are highly influenced not only by pathogen-dependent disease characteristics such as the force of infection, the latent period, and the infectious period, but also by human mobility and contact patterns. However, the effect of heterogeneous mobility of individuals on epidemic outcomes is not fully understood. Here, we aim to elucidate how spatial mobility of individuals contributes to the final epidemic size in a spatial susceptible-exposed-infectious-recovered (SEIR) model with mobile individuals in a square lattice. After illustrating the interplay between the mobility parameters and the other parameters on the spatial epidemic spreading, we propose an index as a function of system parameters, which largely governs the final epidemic size. The main contribution of this study is to show that the proposed index is useful for estimating how parameter scaling affects the final epidemic size. To demonstrate the effectiveness of the proposed index, we show that there is a positive correlation between the proposed index computed with the real data of human airline travels and the actual number of positive incident cases of influenza B in the entire world, implying that the growing incidence of influenza B is attributed to increased human mobility.
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Wave-Based Neuromorphic Computing Framework for Brain-Like Energy Efficiency and Integration 査読あり
Yasunao Katayama, Toshiyuki Yamane, Daiju Nakano, Ryosho Nakane, Gouhei Tanaka
IEEE TRANSACTIONS ON NANOTECHNOLOGY 15 ( 5 ) 762 - 769 2016年09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） 出版者・発行元：IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
We present a framework of wave-based neuromorphic computing aiming at brain-like capabilities and efficiencies with nanoscale device integration. We take advantage of the unique nature of elastic nondissipative wave dynamics in both computations and IO communications in between as a means to natively implement and execute neuromorphic computing functions such as weighted sum in a spatiotemporal manner. Lower bound analysis based on a memory model and wave group velocity scaling is provided for conceptual evaluations.
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Exploiting Heterogeneous Units for Reservoir Computing with Simple Architecture 査読あり
Gouhei Tanaka, Ryosho Nakane, Toshiyuki Yamane, Daiju Nakano, Seiji Takeda, Shigeru Nakagawa, Akira Hirose
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT I 9947 187 - 194 2016年
担当区分：筆頭著者 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） 出版者・発行元：SPRINGER INT PUBLISHING AG
Reservoir computing is a computational framework suited for sequential data processing, consisting of a reservoir part and a read-out part. Not only theoretical and numerical studies on reservoir computing but also its implementation with physical devices have attracted much attention. In most studies, the reservoir part is constructed with identical units. However, a variability of physical units is inevitable, particularly when implemented with nano/micro devices. Here we numerically examine the effect of variability of reservoir units on computational performance. We show that the heterogeneity in reservoir units can be beneficial in reducing the prediction error in the reservoir computing system with a simple cycle reservoir.
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Dynamics of Reservoir Computing at the Edge of Stability 査読あり
Toshiyuki Yamane, Seiji Takeda, Daiju Nakano, Gouhei Tanaka, Ryosho Nakane, Shigeru Nakagawa, Akira Hirose
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT I 9947 205 - 212 2016年
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） 出版者・発行元：SPRINGER INT PUBLISHING AG
We investigate reservoir computing systems whose dynamics are at critical bifurcation points based on center manifold theorem. We take echo state networks as an example and show that the center manifold defines mapping of the input dynamics to higher dimensional space. We also show that the mapping by center manifolds can contribute to recognition of attractors of input dynamics. The implications for realization of reservoir computing as real physical systems are also discussed.
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Computational Performance of Echo State Networks with Dynamic Synapses 査読あり
Ryota Mori, Gouhei Tanaka, Ryosho Nakane, Akira Hirose, Kazuyuki Aihara
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT I 9947 264 - 271 2016年
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） 出版者・発行元：SPRINGER INT PUBLISHING AG
The echo state network is a framework for temporal data processing, such as recognition, identification, classification and prediction. The echo state network generates spatiotemporal dynamics reflecting the history of an input sequence in the dynamical reservoir and constructs mapping from the input sequence to the output one in the readout. In the conventional dynamical reservoir consisting of sparsely connected neuron units, more neurons are required to create more time delay. In this study, we introduce the dynamic synapses into the dynamical reservoir for controlling the nonlinearity and the time constant. We apply the echo state network with dynamic synapses to several benchmark tasks. The results show that the dynamic synapses are effective for improving the performance in time series prediction tasks.
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A Hybrid Pooling Method for Convolutional Neural Networks 査読あり
Zhiqiang Tong, Kazuyuki Aihara, Gouhei Tanaka
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II 9948 454 - 461 2016年
担当区分：最終著者, 責任著者 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） 出版者・発行元：SPRINGER INTERNATIONAL PUBLISHING AG
The convolutional neural network (CNN) is an effective machine learning model which has been successfully used in the computer vision tasks such as image recognition and object detection. The pooling step is an important process in the CNN to decrease the dimensionality of the input image data and keep the transformation invariance for preventing the overfitting problem. There are two major pooling methods, i.e. the max pooling and the average pooling. Their performances depend on the data and the features to be extracted. In this study, we propose a hybrid system of the two pooling methods to improve the feature extraction performance. We randomly choose one of them for each pooling zone with a fixed probability. We show that the hybrid pooling method (HPM) enhances the generalization ability of the CNNs in numerical experiments with the handwritten digit images.
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Oscillation dynamics underlie functional switching of NF-κB for B-cell activation. 査読あり 国際誌
Inoue K, Shinohara H, Behar M, Yumoto N, Tanaka G, Hoffmann A, Aihara K, Okada-Hatakeyama M
NPJ systems biology and applications 2 16024 - 16024 2016年
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Photonic Reservoir Computing Based on Laser Dynamics with External Feedback 査読あり
Seiji Takeda, Daiju Nakano, Toshiyuki Yamane, Gouhei Tanaka, Ryosho Nakane, Akira Hirose, Shigeru Nakagawa
NEURAL INFORMATION PROCESSING, ICONIP 2016, PT I 9947 222 - 230 2016年
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） 出版者・発行元：SPRINGER INT PUBLISHING AG
Reservoir computing is a novel paradigm of neural network, offering advantages in low learning cost and ease of implementation as hardware. In this paper we propose a concept of reservoir computing consisting of a semiconductor laser subject to external feedback by a mirror, where input signal is supplied as modulation pattern of mirror reflectivity. In that system, non-linear interaction between optical field and electrons are enhanced in complex manner under substantial external feedback, leading to achieve highly nonlinear projection of input electric signal to output optical field intensity. It is exhibited that the system can most efficiently classify waveforms of sequential input data when operating around laser oscillation's effective threshold.
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Detecting early warning signals for blackouts in power grids from the viewpoint of nonlinear dynamics 査読あり
Motoki Nagata, Yoshito Hirata, Naoya Fujiwara, Gouhei Tanaka, Hideyuki Suzuki, Kazuyuki Aihara
International Symposium on Nonlinear Theory and its Applications (NOLTA2015) 22 - 25 2015年12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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Dynamics of an HBV Model with Drug Resistance Under Intermittent Antiviral Therapy 査読あり
Ben-Gong Zhang, Gouhei Tanaka, Kazuyuki Aihara, Masao Honda, Shuichi Kaneko, Luonan Chen
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 25 ( 7 ) 2015年06月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：WORLD SCIENTIFIC PUBL CO PTE LTD
This paper studies the dynamics of the hepatitis B virus (HBV) model and the therapy regimens of HBV disease. First, we propose a new mathematical model of HBV with drug resistance, and then analyze its qualitative and dynamical properties. Combining the clinical data and theoretical analysis, we demonstrate that our model is biologically plausible and also computationally viable. Second, we demonstrate that the intermittent antiviral therapy regimen is one of the possible strategies to treat this kind of complex disease. There are two main advantages of this regimen, i.e. it not only may delay the development of drug resistance, but also may reduce the duration of on-treatment time compared with the long-term continuous medication. Moreover, such an intermittent antiviral therapy can reduce the adverse side effects. Our theoretical model and computational results provide qualitative insight into the progression of HBV, and also a possible new therapy for HBV disease.
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Intermittent Androgen Suppression: Estimating Parameters for Individual Patients Based on Initial PSA Data in Response to Androgen Deprivation Therapy. 査読あり 国際誌
Yoshito Hirata, Kai Morino, Koichiro Akakura, Celestia S Higano, Nicholas Bruchovsky, Teresa Gambol, Susan Hall, Gouhei Tanaka, Kazuyuki Aihara
PloS one 10 ( 6 ) e0130372 2015年
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：PUBLIC LIBRARY SCIENCE
When a physician decides on a treatment and its schedule for a specific patient, information gained from prior patients and experience in the past is taken into account. A more objective way to make such treatment decisions based on actual data would be useful to the clinician. Although there are many mathematical models proposed for various diseases, so far there is no mathematical method that accomplishes optimization of the treatment schedule using the information gained from past patients or "rapid learning" technology. In an attempt to use this approach, we integrate the information gained from patients previously treated with intermittent androgen suppression (IAS) with that from a current patient by first fitting the time courses of clinical data observed from the previously treated patients, then constructing the prior information of the parameter values of the mathematical model, and finally, maximizing the posterior probability for the parameters of the current patient using the prior information. Although we used data from prostate cancer patients, the proposed method is general, and thus can be applied to other diseases once an appropriate mathematical model is established for that disease.
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Public opinion formation with the spiral of silence on complex social networks 査読あり
Takeuchi Daiki, Tanaka Gouhei, Fujie Ryo, Suzuki Hideyuki
Nonlinear Theory and Its Applications, IEICE 6 ( 1 ) 15 - 25 2015年
記述言語：英語 出版者・発行元：一般社団法人 電子情報通信学会
Public opinion is formed through social interactions of individuals. A mechanism behind the formation of a highly dominant public opinion is a sociological theory called the spiral of silence. Here we study opinion dynamics resulting from the spiral of silence, using an agent-based model with complex interaction networks. We show that an extremely dominant public opinion arises in the presence of a moderate proportion of neutrals and its dominance level is enhanced by social interactions. Furthermore, we demonstrate that a correlation between characteristics and social interactions of the individuals has a large influence on the opinion formation dynamics.
DOI： 10.1587/nolta.6.15
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Wave-Based Reservoir Computing by Synchronization of Coupled Oscillators 査読あり
Toshiyuki Yamane, Yasunao Katayama, Ryosho Nakane, Gouhei Tanaka, Daiju Nakano
NEURAL INFORMATION PROCESSING, PT III 9491 198 - 205 2015年
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） 出版者・発行元：SPRINGER INT PUBLISHING AG
We propose wave-based computing based on coupled oscillators to avoid the inter-connection bottleneck in large scale and densely integrated cognitive systems. In addition, we introduce the concept of reservoir computing to coupled oscillator systems for non-conventional physical implementation and reduction of the training cost of large and dense cognitive systems. We show that functional approximation and regression can be efficiently performed by synchronization of coupled oscillators and subsequent simple readouts.
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Robustness of oscillatory behavior in correlated networks. 査読あり 国際誌
Takeyuki Sasai, Kai Morino, Gouhei Tanaka, Juan A Almendral, Kazuyuki Aihara
PloS one 10 ( 4 ) e0123722 2015年
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：PUBLIC LIBRARY SCIENCE
Understanding network robustness against failures of network units is useful for preventing large-scale breakdowns and damages in real-world networked systems. The tolerance of networked systems whose functions are maintained by collective dynamical behavior of the network units has recently been analyzed in the framework called dynamical robustness of complex networks. The effect of network structure on the dynamical robustness has been examined with various types of network topology, but the role of network assortativity, or degree-degree correlations, is still unclear. Here we study the dynamical robustness of correlated (assortative and disassortative) networks consisting of diffusively coupled oscillators. Numerical analyses for the correlated networks with Poisson and power-law degree distributions show that network assortativity enhances the dynamical robustness of the oscillator networks but the impact of network disassortativity depends on the detailed network connectivity. Furthermore, we theoretically analyze the dynamical robustness of correlated bimodal networks with two-peak degree distributions and show the positive impact of the network assortativity.
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Regularity and Randomness in Modular Network Structures for Neural Associative Memories 査読あり
Gouhei Tanaka, Toshiyuki Yamane, Daiju Nakano, Ryosho Nakane, Yasunao Katayama
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) 1 - 7 2015年
担当区分：筆頭著者 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） 出版者・発行元：IEEE
This study explores efficient structures of artificial neural networks for associative memories. Motivated by the real brain structure and the demand of energy efficiency in hardware implementation, we consider neural networks with sparse modular structures. Numerical experiments are performed to clarify how the storage capacity of associative memory depends on regularity and randomness of the network structures. We first show that a fully regularized network, suited for design of hardware, has poor recall performance and a fully random network, undesired for hardware implementation, yields excellent recall performance. For seeking a network structure with good performance and high implementability, we consider four different modular networks constructed based on different combinations of regularity and randomness. From the results of associative memory tests for these networks, we find that the combination of random intramodule connections and regular intermodule connections works better than the other cases. Our results suggest that the parallel usage of regularity and randomness in network structures could be beneficial for developing energy-efficient neural networks.
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Node-wise robustness against fluctuations of power consumption in power grids 査読あり
Motoki Nagata, Naoya Fujiwara, Gouhei Tanaka, Hideyuki Suzuki, Eiichi Kohda, Kazuyuki Aihara
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS 223 ( 12 ) 2549 - 2559 2014年10月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：SPRINGER HEIDELBERG
We propose a new concept of node-wise robustness of power grids under variation of effective power in one load node using a mathematical model that takes into account the change in voltage and reactive power of load nodes. We employ the topology of the power grid in eastern Japan. We define the robustness as the threshold value of the effective power, above which the steady state loses its stability. We show that the robustness is highly heterogeneous among the load nodes. We find that the shortest path length from generators is most highly correlated with the robustness of the load nodes. We numerically demonstrate that the supply of reactive power enhances the robustness.
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Epidemic spread on interconnected metapopulation networks 査読あり 国際誌
Bing Wang, Gouhei Tanaka, Hideyuki Suzuki, Kazuyuki Aihara
PHYSICAL REVIEW E 90 ( 3 ) 032806 - 032806 2014年09月
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
Numerous real-world networks have been observed to interact with each other, resulting in interconnected networks that exhibit diverse, nontrivial behavior with dynamical processes. Here we investigate epidemic spreading on interconnected networks at the level of metapopulation. Through a mean-field approximation for a metapopulation model, we find that both the interaction network topology and the mobility probabilities between subnetworks jointly influence the epidemic spread. Depending on the interaction between subnetworks, proper controls of mobility can efficiently mitigate epidemics, whereas an extremely biased mobility to one subnetwork will typically cause a severe outbreak and promote the epidemic spreading. Our analysis provides a basic framework for better understanding of epidemic behavior in related transportation systems as well as for better control of epidemics by guiding human mobility patterns.
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Finite-size scaling in globally coupled phase oscillators with a general coupling scheme 査読あり
Isao Nishikawa, Koji Iwayama, Gouhei Tanaka, Takehiko Horita, Kazuyuki Aihara
PROGRESS OF THEORETICAL AND EXPERIMENTAL PHYSICS 2014 ( 2 ) 2014年02月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：OXFORD UNIV PRESS INC
We investigate the critical exponent of correlation size, related to synchronization transition, in globally coupled nonidentical phase oscillators. The critical exponent has so far been identified for sinusoidal coupling, but has not been fully studied for other coupling schemes. Herein, for a general coupling function including a negative second harmonic term in addition to the sinusoidal term, we numerically estimate the critical exponent of the correlation size, denoted by nu(+), in a synchronized regime of the system by employing a non-conventional statistical quantity. First, we confirm that the estimated value of nu(+) is approximately 5/2 for the sinusoidal coupling case, which is consistent with the well known theoretical result. Second, we show that the value of nu(+) increases with an increase in the strength of the second harmonic term. Our result means that the universality of a critical exponent can break down in the globally coupled phase oscillators.
DOI： 10.1093/ptep/ptu015
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Analysis of Supply Stability of the Power Grid in Eastern Japan Using a Phase Model 査読あり
Motoki Nagata, Naoya Fujiwara, Isao Nishikawa, Gouhei Tanaka, Hideyuki Suzuki, Kazuyuki Aihara
International Symposium on Nonlinear Theory and its Applications (NOLTA2013) 69 - 72 2013年09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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Efficient recovery of dynamic behavior in coupled oscillator networks. 査読あり 国際誌
Kai Morino, Gouhei Tanaka, Kazuyuki Aihara
Physical review. E, Statistical, nonlinear, and soft matter physics 88 ( 3 ) 032909 - 032909 2013年09月
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 an effective method to recover dynamic activity in coupled oscillator networks that have been damaged and lost oscillatory dynamics owing to some inactivated or deteriorated oscillator elements. Recovery of the dynamic behavior can be achieved by newly connecting intact oscillators to the network. We analytically and numerically examine the proportion of the oscillators that are needed to be supported by intact oscillators for recovery of oscillation dynamics. Our results show that it can be more effective to preferentially support active oscillators in the damaged network than to preferentially support inactivated ones. The conditions for this counterintuitive result are discussed. Our framework could be a theoretical foundation for understanding regeneration of oscillatory dynamics in physical and biological systems.
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Nonstandard scaling law of fluctuations in finite-size systems of globally coupled oscillators. 査読あり 国際誌
Isao Nishikawa, Gouhei Tanaka, Kazuyuki Aihara
Physical review. E, Statistical, nonlinear, and soft matter physics 88 ( 2 ) 024102 - 024102 2013年08月
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
Universal scaling laws form one of the central issues in physics. A nonstandard scaling law or a breakdown of a standard scaling law, on the other hand, can often lead to the finding of a new universality class in physical systems. Recently, we found that a statistical quantity related to fluctuations follows a nonstandard scaling law with respect to the system size in a synchronized state of globally coupled nonidentical phase oscillators [I. Nishikawa et al., Chaos 22, 013133 (2012)]. However, it is still unclear how widely this nonstandard scaling law is observed. In the present paper, we discuss the conditions required for the unusual scaling law in globally coupled oscillator systems and validate the conditions by numerical simulations of several different models.
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Effects of seasonal variation patterns on recurrent outbreaks in epidemic models. 査読あり 国際誌
Gouhei Tanaka, Kazuyuki Aihara
Journal of theoretical biology 317 87 - 95 2013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）
Transmission of infectious diseases often depends on seasonal variability. Mathematical epidemic models driven by seasonal forcing have been widely explored to understand recurrent outbreaks of infectious diseases. Here we present an effective method to examine the impact of seasonal variation patterns on epidemic dynamics. The idea is to represent the seasonal variability as a piecewise constant function and analyze the seasonally forced epidemic model by means of a numerical shooting method for switched dynamical systems. Several illustrative examples demonstrate that our method is useful to elucidate the effects of various types of seasonality in outbreak behavior. First, we clarify an effect of the shape of seasonal forcing by comparing sinusoidal and square wave forcing functions. Second, we demonstrate that not only the intensity of seasonality but also its temporal variation pattern significantly influences the outbreak pattern. Finally, we reveal the mechanisms of transitions between different outbreak patterns in an epidemic model driven by realistic term-time seasonal forcing and one driven by seasonal forcing estimated from real data. Our results suggest that accurately estimated seasonal variability is necessary for better understanding the dynamics of infectious diseases.
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Long-term fluctuations in globally coupled phase oscillators with general coupling: finite size effects. 査読あり 国際誌
Isao Nishikawa, Gouhei Tanaka, Takehiko Horita, Kazuyuki Aihara
Chaos (Woodbury, N.Y.) 22 ( 1 ) 013133 - 013133 2012年03月
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 INST PHYSICS
We investigate the diffusion coefficient of the time integral of the Kuramoto order parameter in globally coupled nonidentical phase oscillators. This coefficient represents the deviation of the time integral of the order parameter from its mean value on the sample average. In other words, this coefficient characterizes long-term fluctuations of the order parameter. For a system of N coupled oscillators, we introduce a statistical quantity D, which denotes the product of N and the diffusion coefficient. We study the scaling law of D with respect to the system size N. In other well-known models such as the Ising model, the scaling property of D is D∼O(1) for both coherent and incoherent regimes except for the transition point. In contrast, in the globally coupled phase oscillators, the scaling law of D is different for the coherent and incoherent regimes: D∼O(1/N(a)) with a certain constant a>0 in the coherent regime and D∼O(1) in the incoherent regime. We demonstrate that these scaling laws hold for several representative coupling schemes.
DOI： 10.1063/1.3692966
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Mathematically modelling and controlling prostate cancer under intermittent hormone therapy. 査読あり 国際誌
Yoshito Hirata, Gouhei Tanaka, Nicholas Bruchovsky, Kazuyuki Aihara
Asian journal of andrology 14 ( 2 ) 270 - 7 2012年03月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：ACTA PHARMACOLOGICA SINICA
In this review, we summarize our recently developed mathematical models that predict the effects of intermittent androgen suppression therapy on prostate cancer (PCa). Although hormone therapy for PCa shows remarkable results at the beginning of treatment, cancer cells frequently acquire the ability to grow without androgens during long-term therapy, resulting in an eventual relapse. To circumvent hormone resistance, intermittent androgen suppression was investigated as an alternative treatment option. However, at the present time, it is not possible to select an optimal schedule of on- and off-treatment cycles for any given patient. In addition, clinical trials have revealed that intermittent androgen suppression is effective for some patients but not for others. To resolve these two problems, we have developed mathematical models for PCa under intermittent androgen suppression. The mathematical models not only explain the mechanisms of intermittent androgen suppression but also provide an optimal treatment schedule for the on- and off-treatment periods.
DOI： 10.1038/aja.2011.155
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Robustness of multilayer oscillator networks. 査読あり 国際誌
Kai Morino, Gouhei Tanaka, Kazuyuki Aihara
Physical review. E, Statistical, nonlinear, and soft matter physics 83 ( 5 Pt 2 ) 056208 - 056208 2011年05月
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 consider the robustness of multilayer networks composed of active and inactive oscillators from the viewpoint of interlayer coupling effects through the aging transition [H. Daido and K. Nakanishi, Phys. Rev. Lett. 93, 104101 (2004)]. We show in detail that two-layer networks increase or decrease their robustness depending on interlayer coupling schemes compared with single-layer networks. In addition, we find that an increase of mismatches of oscillator types (active or inactive) among interlayer-connected oscillators reduces the robustness of the networks with mean-field, chain, and diffusive interlayer couplings in two-layer networks. Moreover, we discuss the robustness of networks with more than two layers.
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Mathematical modelling of prostate cancer growth and its application to hormone therapy. 査読あり 国際誌
Gouhei Tanaka, Yoshito Hirata, S Larry Goldenberg, Nicholas Bruchovsky, Kazuyuki Aihara
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences 368 ( 1930 ) 5029 - 44 2010年11月
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：ROYAL SOC
Hormone therapy in the form of androgen deprivation is a major treatment for advanced prostate cancer. However, if such therapy is overly prolonged, tumour cells may become resistant to this treatment and result in recurrent fatal disease. Long-term hormone deprivation also is associated with side effects poorly tolerated by patients. In contrast, intermittent hormone therapy with alternating on- and off-treatment periods is a possible clinical strategy to delay progression to hormone-refractory disease with the advantage of reduced side effects during the off-treatment periods. In this paper, we first overview previous studies on mathematical modelling of prostate tumour growth under intermittent hormone therapy. The model is categorized into a hybrid dynamical system because switching between on-treatment and off-treatment intervals is treated in addition to continuous dynamics of tumour growth. Next, we present an extended model of stochastic differential equations and examine how well the model is able to capture the characteristics of authentic serum prostate-specific antigen (PSA) data. We also highlight recent advances in time-series analysis and prediction of changes in serum PSA concentrations. Finally, we discuss practical issues to be considered towards establishment of mathematical model-based tailor-made medicine, which defines how to realize personalized hormone therapy for individual patients based on monitored serum PSA levels.
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Phase transitions in mixed populations composed of two types of self-oscillatory elements with different periods. 査読あり 国際誌
Gouhei Tanaka, Yusuke Okada, Kazuyuki Aihara
Physical review. E, Statistical, nonlinear, and soft matter physics 82 ( 3 Pt 2 ) 035202 - 035202 2010年09月
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
In globally coupled networks composed of oscillatory and nonoscillatory elements, the balance between the subpopulations plays an important role in network dynamics and phase transitions. To extend this framework, we investigate mixed populations consisting of two types of self-oscillatory elements with different periods, particularly given by limit cycle oscillators and period-doubled ones. Phase transitions in the mixed populations are elucidated by numerical bifurcation analyses of a reduced system. We numerically confirm a formula determining the critical balance between the subpopulations for a phase transition at sufficiently large coupling strength.
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Grazing-induced crises in hybrid dynamical systems 査読あり
Gouhei Tanaka, Shigeki Tsuji, Kazuyuki Aihara
PHYSICS LETTERS A 373 ( 35 ) 3134 - 3139 2009年08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
In hybrid dynamical systems including both continuous and discrete components, an interplay between a continuous trajectory and a discontinuity boundary can trigger a sudden qualitative change in the system dynamics. Grazing phenomena, which occur when a continuous trajectory hits a boundary tangentially, are well known as a representative of such phenomena. We demonstrate that a grazing phenomenon of a chaotic attractor can result in its sudden disappearance and initiate chaotic transients. The mechanism of this grazing-induced crisis is revealed in an illustrative example. Furthermore, we derive a formula to obtain the critical exponent of the power law on the mean duration of chaotic transients. (C) 2009 Elsevier B.V. All rights reserved.
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A Mathematical Model of Intermittent Androgen Suppression for Prostate Cancer 査読あり
Aiko Miyamura Ideta, Gouhei Tanaka, Takumi Takeuchi, Kazuyuki Aihara
JOURNAL OF NONLINEAR SCIENCE 18 ( 6 ) 593 - 614 2008年12月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：SPRINGER
For several decades, androgen suppression has been the principal modality for treatment of advanced prostate cancer. Although the androgen deprivation is initially effective, most patients experience a relapse within several years due to the proliferation of so-called androgen-independent tumor cells. Bruchovsky et al. suggested in animal models that intermittent androgen suppression (IAS) can prolong the time to relapse when compared with continuous androgen suppression (CAS). Therefore, IAS has been expected to enhance clinical efficacy in conjunction with reduction in adverse effects and improvement in quality of life of patients during off-treatment periods. This paper presents a mathematical model that describes the growth of a prostate tumor under IAS therapy based on monitoring of the serum prostate-specific antigen (PSA). By treating the cancer tumor as a mixed assembly of androgen-dependent and androgen-independent cells, we investigate the difference between CAS and IAS with respect to factors affecting an androgen-independent relapse. Numerical and bifurcation analyses show how the tumor growth and the relapse time are influenced by the net growth rate of the androgen-independent cells, a protocol of the IAS therapy, and the mutation rate from androgen-dependent cells to androgen-independent ones.
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Complex-Valued Multistate Associative Memory with Nonlinear Multilevel Functions for Gray-Level Image Reconstruction 査読あり
Gouhei Tanaka, Kazuyuki Aihara
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8 3086 - 3092 2008年
担当区分：筆頭著者 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） 出版者・発行元：IEEE
The complex-signum function has been widely used as an activation function in complex-valued recurrent neural networks for multistate associative memory. This paper presents two alternative activation functions with circularity. One is the complex-sigmoid function based on a multilevel sigmoid function defined on a circle. The other is a characteristic of a bifurcating neuron represented by a circle map. The performance of the complex-valued neural networks with the two kinds of activation functions is investigated in multistate associative memory tests. In both networks, the connection weights to store the memory patterns are determined by the generalized projection rule. We also demonstrate gray-level image reconstruction as a possible application of the proposed methods.
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Sensitivity versus resonance in two-dimensional spiking-bursting neuron models. 査読あり 国際誌
Borja Ibarz, Gouhei Tanaka, Miguel A F Sanjuán, Kazuyuki Aihara
Physical review. E, Statistical, nonlinear, and soft matter physics 75 ( 4 Pt 1 ) 041902 - 041902 2007年04月
記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：AMERICAN PHYSICAL SOC
Through phase plane analysis of a class of two-dimensional spiking and bursting neuron models, covering some of the most popular map-based neuron models, we show that there exists a trade-off between the sensitivity of the neuron to steady external stimulation and its resonance properties, and how this trade-off may be tuned by the neutral or asymptotic character of the slow variable. Implications of the results for the suprathreshold behavior of the neurons, both by themselves and as part of networks, are presented in different regimes of interest, such as the excitable, regular spiking, and bursting regimes. These results establish a consistent link between single-neuron parameters and resulting network dynamics, and will hopefully be useful as a guide for modeling.
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Collective skipping: Aperiodic phase locking in ensembles of bursting oscillators 査読あり
G. Tanaka, K. Aihara
EPL 78 ( 1 ) 2007年
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：EPL ASSOCIATION, EUROPEAN PHYSICAL SOCIETY
Aperiodic phase-locked firing found in biological neurons, called skipping, has been mainly modeled by a forced nonlinear system with noise so far. In contrast, we present another mechanism of skipping as a deterministic collective behavior in a population of coupled bursting oscillators. This collective skipping emerges when the highly irregular repetitive bursts in isolated oscillators are incompletely synchronized by coherent feedback inputs due to mutual connections. We show that the phenomenon is dependent on the coupling strength and the number of connections but almost independent of the network topology. Copyright (c) EPLA, 2007.
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A hybrid systems approach to hormonal therapy of prostate cancer and its Nonlinear dynamics 査読あり
Kazuyuki Aihara, Gouhei Tanaka, Taiji Suzuki, Yoshito Hirata
NOISE AND FLUCTUATIONS 922 479 - + 2007年
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） 出版者・発行元：AMER INST PHYSICS
This talk is to review our recent work on mathematical modeling of prostate cancer and its application to hormonal therapy of intermittent androgen suppression. First, we model the tumor growth of prostate cancer composed of a mixed dynamical assembly of androgen-dependent and androgen-independent cancer cells. Then, we introduce the intermittent androgen suppression to the model as feedback control with monitoring the serum prostate-specific antigen, where the controlled model is described as a hybrid system with continuous and discrete variables. Next, we analyze nonlinear dynamics and bifurcations of the hybrid system. Finally, we discuss a possibility to improve the hormonal therapy.
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Synchronization and propagation of bursts in networks of coupled map neurons. 査読あり 国際誌
Gouhei Tanaka, Borja Ibarz, Miguel A F Sanjuan, Kazuyuki Aihara
Chaos (Woodbury, N.Y.) 16 ( 1 ) 013113 - 013113 2006年03月
担当区分：筆頭著者 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 INST PHYSICS
The present paper studies regular and complex spatiotemporal behaviors in networks of coupled map-based bursting oscillators. In-phase and antiphase synchronization of bursts are studied, explaining their underlying mechanisms in order to determine how network parameters separate them. Conditions for emergent bursting in the coupled system are derived from our analysis. In the region of emergence, patterns of chaotic transitions between synchronization and propagation of bursts are found. We show that they consist of transient standing and rotating waves induced by symmetry-breaking bifurcations, and can be viewed as a manifestation of the phenomenon of chaotic itinerancy.
DOI： 10.1063/1.2148387
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Crisis-induced intermittency in two coupled chaotic maps: Towards understanding chaotic itinerancy 査読あり
G. Tanaka, M. A.F. Sanjuán, K. Aihara
Physical Review E - Statistical, Nonlinear, and Soft Matter Physics 71 ( 1 ) 016219 2005年01月
担当区分：筆頭著者 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
The present paper considers crisis-induced intermittency in a system composed of two coupled logistic maps. Its purpose is to clarify a bifurcation scenario generating such intermittent behaviors that can be regarded as a simple example of chaotic itinerancy. The intermittent dynamics appears immediately after an attractor-merging crisis of two off-diagonal chaotic attractors in a symmetrically coupled system. The scenario for the crisis is investigated through analyses of sequential bifurcations leading to the two chaotic attractors and successive changes in basin structures with variation of a system parameter. The successive changes of the basins are also characterized by variation of a dimension of a fractal basin boundary. A numerical analysis shows that simultaneous contacts between the attractors and the fractal basin boundary bring about the crisis and a snap-back repeller generated at the crisis produces the intermittent transitions. Furthermore, a modified scenario for intermittent behaviors in an asymmetrically coupled system is also discussed. © 2005 The American Physical Society.
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Multistate associative memory with parametrically coupled map networks 査読あり
Gouhei Tanaka, Kazuyuki Aihara
International Journal of Bifurcation and Chaos in Applied Sciences and Engineering 15 ( 4 ) 1395 - 1410 2005年
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：World Scientific Publishing Co. Pte Ltd
The present paper proposes two types of parametrically coupled circle map networks for multistate associative memory. One of the networks uses a circle map exhibiting an attractor-merging crisis of multiple chaotic attractors to represent a multistate element. The other uses another circle map whose bifurcation diagram serves as a substitute for a multilevel activation function. The configuration of each network is suitably selected according to the dynamics of the individual circle map so that the network can bring about self-organizing chaotic dynamics with an association of a memory. Namely, the coupling term is determined by the generalized partial error function in the first network, and by the weighted sum of inputs in the second network. These multistate networks can be considered as extensions of two kinds of interesting binary networks called the parametrically coupled sine map networks [Lee &
Farhat, 2001a], respectively. We illustrate that the proposed networks can exhibit desirable associative dynamics that is missing in the conventional multistate networks © World Scientific Publishing Company. -
Bifurcation structures of period-adding phenomena in an ocean internal wave model 査読あり
Gouhei Tanaka, Sunao Murashige, Kazuyuki Aihara
International Journal of Bifurcation and Chaos in Applied Sciences and Engineering 13 ( 11 ) 3409 - 3424 2003年
担当区分：筆頭著者 記述言語：英語 掲載種別：研究論文（学術雑誌） 出版者・発行元：World Scientific Publishing Co. Pte Ltd
In this paper, we study bifurcation structures of period-adding phenomena in an internal wave model that is a mathematical model for ocean internal waves. It has been suggested that chaotic solutions observed in the internal wave model may be related to the universal property of the energy spectra of ocean internal waves. In numerical bifurcation analyses of the internal wave model, we illustrate bifurcation routes to chaos and parameter regions where chaotic behavior is observed. Furthermore, it is found that the chaotic solutions are related to the period-adding sequence, that is, successive generations of periodic solutions with longer periods as a control parameter is changed. Considering the period-adding sequence as successive local bifurcations, we discuss a mechanism of the phenomena from the viewpoint of bifurcation analysis. We also consider similarity between period-adding phenomena in the internal wave model and ones in the Lorenz model.
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