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*Mathematical Research on Information Processing of the Brain and Nervous Systems [#id80162d] *Complex Systems Mathematical Modeling [#o5308a2b] We study nervous systems from mathematical viewpoint and analyze experimental data to understand the information processing in the brain. The topics include nonlinear dynamics of neural network models, mathematical modeling of cognitive processes, and optimal synaptic learning rules. We have also proposed new methods for analyzing experimental data and developed analog computation devices based on mathematical neuronal models. We study a variety of complex systems and problems―biological systems, social systems, economic systems, diseases, energy problems, natural disasters, and so on―through mathematical modeling and data analyses. We also try to establish fundamental theories and methods for analyzing those specific systems. We aim at further development of researches based on the joint works with the Collaborative Research Center for Innovative Mathematical Modelling. #ref(数理生命情報学研究室/研究紹介/chaos_neurocomputer.jpg,around,60%); *Dynamics of Neural Networks and Its Applications [#ya44fbb1] -Recent publications --K. Sakamoto, Y. Katori, N. Saito, S. Yoshida, K. Aihara, and H. Mushiake: PLOS ONE, Vol. 8, No. 12, e80906-1-26 (2013). --T. Kanamaru, H. Fujii, and K. Aihara: PLOS ONE, Vol. 8, No. 1, e53854 (2013). --Y. Katori, Y. Otsubo, M. Okada, and K. Aihara: Front. Comput. Neurosci., Vol. 7, 6 (2013). --J. Li, Y. Katori, and T. Kohno: Front. Neurosci., Vol. 6, 183 (2012). --K.-I. Sawai, Y. Sato, and K. Aihara: Front. Psychol., Vol. 3, 524 (2012). --T. Teerasit, M. Oku, and K. Aihara: Artif. Life Robot., Vol. 17, No. 1, pp. 75-79 (2012). --R. Yokota, K. Aihara, R. Kanzaki, and H. Takahashi: Neurosci., Vol. 223, No. 25, pp. 377-387 (2012). --T. Leleu and K. Aihara: Cogn. Neurodyn., Vol. 6, No. 6, pp. 499-524 (2012). We are trying to clarify the mechanism of real neural networks and to reveal the high-order functions of the brain through developing mathematical models of neurons/neural networks and identifying underlying non-trivial mathematical structure. As an application, we are also developing analog silicon neural networks and AI. #clear #ref(数理生命情報学研究室/ 研究紹介/chaos_neurocomputer.jpg,,50%); -Recent publications --T. Sase, Y. Katori, M. Komuro, and K. Aihara: Front. Comp. Neurosci., Vol. 11, Article 18 (2017). --Y. Li, M. Oku, G. He, and K. Aihara: Neural Netw., Vol. 88, pp. 9-21 (2017). --T. Nanami and T. Kohno: Front. Neurosci., Vol. 10, Article No. 181 (2016). --C. I. Tajima, S. Tajima, K. Koida, H. Komatsu, K. Aihara, and H. Suzuki: Sci. Rep., Vol. 6, Article No. 22536 (2016). --T. Kiwaki and K. Aihara: Artif. Intell. Res., Vol. 4, No. 1, 53 (2015). --T. Leleu, K. Aihara: Phys. Rev. E, Vol. 91, 022804 (2015). *Nonlinear Systems Analysis and Its Applications to the Real World Systems [#i4e9d478] We study a variety of complex dynamical phenomena in the real world through mathematical modeling and analyses based on nonlinear dynamical systems theory and time series analysis methods. The topics include hybrid dynamical systems, coupled oscillators, game theory, complex networks, recurrence plots, and associative memories. We have also analyzed real data related to weathers, biological systems, economical systems, social systems, earthquakes, and electrical grids. We are studying chaos and many other complex phenomena in the world that have some regularity behind the complexity, by using nonlinear dynamical systems theory. We focus on the "nonlinearity" of the target systems, develop mathematical models that can reproduce the complex phenomena, and analyze the models to reveal the essential factors. Topics include: synchronization of coupled oscillators, forecast of renewable energy generation, analysis of economic and seismic data, etc. #ref(数理生命情報学研究室/研究紹介/chaosNN_simulation.jpg,around,100%) #ref(数理生命情報学研究室/ 研究紹介/chaosNN_simulation.jpg,,100%) -Recent publications --T. Omi, Y. Ogata, Y. Hirata, and K. Aihara: Sci. Rep., Vol. 3, 2218 (2013). --K. Sasahara, Y. Hirata, M. Toyoda, M. Kitsuregawa, and K. Aihara: PLOS ONE, Vol. 8, No. 4, e61823 (2013). --H. Suzuki, J. Imura, Y. Horio, and K. Aihara: Sci. Rep., Vol. 3, 1610 (2013). --H. Suzuki, J. Imura, and K. Aihara: Sci. Rep., Vol. 3, 1127 (2013). --G. Tanaka, K. Morino, and K. Aihara: Sci. Rep., Vol. 2, 232 (2012). --K. Iwayama, Y. Hirata, K. Takahashi, K. Watanabe, K. Aihara, and H. Suzuki: Sci. Rep., Vol. 2, 423 (2012). --T. Omi, Y. Hirata, and K. Aihara: Phys. Rev. E, Vol. 96, 012303 (2017). --K. Kamiyama, M. Komuro, and K. Aihara: IJBC, Vol. 27, No. 3, 1730012 (2017). --T. Yuan, K. Aihara, and G. Tanaka: Phys. Rev. E, Vol. 95, No. 1, 012315 (2017). --M. Chayama, and Y. Hirata: Phys. Lett. A, Vol. 380, pp. 2359-2365 (2016). --M. Fukino, Y. Hirata, and K. Aihara: Chaos, Vol. 26, No. 2, 023116 (2016). --T. Sase, J. Peña Ramírez, K. Kitajo, K. Aihara, and Y. Hirata: Phys. Lett. A, Vol. 380, pp. 1151-1163 (2016). --L. Speidel, R. Lambiotte, K. Aihara, N. Masuda: Phys. Rev. E, Vol. 91, 012806 (2015). #clear *Mathematical Modelling of Diseases [#r984bfe1] *Quantum Artificial Brain and Combinatorial Optimization [#xc3f1488] To understand diseases such as cancers and infectious diseases for which effective therapies and preventions have not yet been established, we are trying to make mathematical models and propose effective countermeasures for the diseases. We have investigated the efficacy of intermittent hormone therapy for prostate cancer using time series analysis and bifurcation analysis. We have also developed a system for a large-scale simulation of spread of pandemic flu by using person trip data. We are mathematically studying a new paradigm of computation—quantum artificial brain—based on neural information processing and optical quantum computing. It aims for solving problems that are difficult for conventional computers such as combinatorial optimization problems in a rapid and accurate manner, which may contribute to resolve many social issues. #ref(数理生命情報学研究室/研究紹介/bifurcation_analysis_IAS.jpg,around,60%) -Recent publications --K. Ejima, K. Aihara, and H. Nishiura: PLOS ONE, Vol. 8, No. 4, e62062 (2013). --R. Liu, M. Li, Z.-P. Liu, J. Wu, L. Chen, and K. Aihara: Sci. Rep., Vol. 2, 813 (2012). --B. Wang, L. Cao, H. Suzuki, and K. Aihara: Sci. Rep., Vol. 2, 887 (2012). --L. Chen, R. Liu, Z.-P. Liu, M. Li, and K. Aihara: Sci. Rep., Vol. 2, 342 (2012). --Y Hirata, G. Tanaka, N. Bruchovsky, and K. Aihara: Asian J. Androl., Vol. 14, pp. 270-277 (2012). --T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P.L. McMahon, T. Umeki, K. Enbutsu, O. Tadanaga, H. Takenouchi, K. Aihara, K. Kawarabayashi, K. Inoue, S. Utsunomiya, and H. Takesue, Science, Vol. 354, No. 6312, pp. 603-606 (2016). --P.L. McMahon, A. Marandi, Y. Haribara, R. Hamerly, C. Langrock, S. Tamate, T. Inagaki, H. Takesue, S. Utsunomiya, K. Aihara, R.L. Byer, M.M. Fejer, H. Mabuchi, and Y. Yamamoto, Science, Vol. 354, No.6312, pp.614-617 (2016). --H. Sakaguchi, K. Ogata, T. Isomura, S. Utsunomiya, Y. Yamamoto, and K. Aihara, Entropy, Vol. 18, No. 10, 365 (2016). --Y. Haribara, S. Utsunomiya, and Y. Yamamoto: Entropy, Vol. 18, No. 4, 151 (2016). #clear