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*Neuroscience and Biological Information Processing [#md796902] Our research on neuroscience aims at understanding information processing and various behaviors in the brain. For instance, we have considered a relationship between neurophysiologic properties and neuronal functions through mathematical modeling of a neuron and neural networks [1,2]. We have derived a theoretical framework for an optimal learning rule in neuronal systems from the viewpoint of information theory [3]. Mathematical analysis of neuron models is also included in our research interests [4]. Moreover, we have been analyzing real data of neuronal spikes [5] and implementing analog devices based on neuronal properties for realizing a novel computation scheme. +K. Morita, K. Tsumoto, and K. Aihara, Biophys. J., Vol.90, pp.1925-1938 (2006). +Y. Katori, N. Masuda, and K. Aihara, Neural Networks, Vol.19, pp.1463-1466 (2006). +T. Toyoizumi, K. Aihara, and S. Amari, Phys. Rev. Lett., Vol.97, 098102 (2006). +K. Tsumoto, H. Kitajima, T. Yoshinaga, K. Aihara, and H. Kawakami, Neurocomput., Vol.69, pp.293-316 (2006). +K. Fujiwara, H. Fujiwara, M. Tsukada, and K. Aihara, Biosystems, in press (2007). *Neuroscience and Biological Information Processing [#md796902] We have theoretically studied neural network models [1-3] and developed new methods to analyze real data of neuronal spikes [4-6] for understanding information processing and various behaviors in living systems. We are also collaborating with other laboratories performing experiments on nervous activities [7]. Moreover, we are constructing a basic methodology for realizing a novel computation device by using information processing schemes of living systems [8]. + Y. Kakimoto and K. Aihara, New Math. and Natural Comput., Vol.5, No. 1, pp.123-134 (2009). + T. Kanamaru and K. Aihara, Neural Comput., Vol.22, No.5, pp.1383-1398 (2009). + S. Kubota, K. Hamaguchi, and K. Aihara, Neural Computing and Applications, Vol.18, No.6, pp.591-602 (2009). + K. Fujiwara and K. Aihara, J. Artificial Life and Robotics, Vol.13, No.2, pp.470-473 (2009). + K. Fujiwara and K. Aihara, European Phys. J. B, Vol.68, No.2, pp.283-289 (2009). + Y. Hirata and K. Aihara, J. Neurosci. Methods, Vol.183, No.2, pp.277-286 (2009). + H. Mushiake, K. Sakamoto, N. Saito, T. Inui, and K. Aihara, Int. Review of Neurobiology, Vol.85, pp.1-11 (2009). + H. Tanaka, T. Morie, and K. Aihara, IEICE Trans. Fund. Electron. Comm. Comput. Sci., Vol.E92-A, No.7, pp.1096-1098 (2009). *Nonlinear Dynamical Systems and its Applications [#c84d3134] The aim of our research on nonlinear science is to understand the essence of complex behaviors through analyses of various complex nonlinear phenomena in biological, physical, and engineering systems. Putting the focus on nonlinearity, we investigate how highly complex phenomena arise in a simple nonlinear system and how self-organization takes place in a chaotic system, by developing the methods for bifurcation analysis, time series analysis, and statistical analysis [1-5]. Application studies include information processing based on nonlinear dynamics, information extraction from biological data, and deterministic chaos in wind. +H. Suetani, Y. Iba, and K. Aihara, J. Phys. A, Vol.39, pp.10723―10742 (2006). +H. Ando and K. Aihara, Phys. Rev. E, Vol.74, 066205 (2006). +G. Tanaka, B. Ibarz, M.A.F. Sanjuan, and K. Aihara, Chaos, Vol.16, 013113 (2006). +Y. Hirata, H. Suzuki, and K. Aihara, Phys. Rev. E, Vol.74, 026202 (2006). +N. Masuda, G. Jakimoski, K. Aihara, and L. Kocarev, IEEE Trans. Circ. Syst. I, Vol.53, pp.1341-1352 (2006). The aim of our research on nonlinear science is to understand the essence of complex behaviors through mathematical modelling and analyses of nonlinear systems. The topics of our research include coupled oscillators [1,2], game theory [3,4], complex networks [5], recurrence plots [6], and hybrid dynamical systems [7]. We are also trying to develop a new method to address determinism and nonlinearity of real systems and applying them to data analysis of wind profile [8], gray-scale image restoration [9], pattern analysis of partial discharges, and economic data analysis. + I. Nishikawa, N. Tsukamoto, and K. Aihara, Physica D, Vol.238, pp.1197-1202 (2009). + Y. Hirata, M. Aono, and K. Aihara, Chaos, Vol.20, 013117 (2010). + B. Wang, Y. Han, L. Chen, and K. Aihara, Phys. Lett. A, Vol.373, pp.1519-1523 (2009). + K. Hashimoto and K. Aihara, J. Theor. Biol., Vol.258, pp.637-645 (2009). + H. Fan, Z. Wang, L. Chen, and K. Aihara, Phys. Rev. E, Vol.79, 026107 (2009). + Y. Hirata and K. Aihara, Phys. Rev. E, Vol.81, 016203 (2009). + G. Tanaka, S. Tsuji, and K. Aihara, Phys. Lett. A, Vol.373, pp.3134-3139 (2009). + D. Mandic, S. Javidi, A. Kuh, and K. Aihara, Renewable Energy, Vol.34, No.1, pp.196-201 (2009). + G. Tanaka and K. Aihara, IEEE Trans, Neural Networks, Vol.20, No.9, pp.1463-1473 (2009). *Mathematical Modeling of Biochemical Systems [#t4a56ead] We study nonlinear dynamics in biochemical reactions in the cell in order to understand collective activities in the ensembles of cells. We have been studied the mechanism of biological rhythms by constructing a mathematical model of genome-proteome networks and attempted to propose a method to design artificial gene networks [1-8]. We have constructed mathematical models and developed methods for analyzing experimental images and data to understand quantitative features of cell development and differentiation. We have clarified mechanisms and roles of stochastic fluctuations in cell systems [1,2] and elucidated the mechanism of circadian rhythms related to jet lag syncrome [3]. Moreover, we perform theoretical studies on oscillations signal transductions in biomolecular networks [4-7]. + H. Tozaki, T. J. Kobayashi, et al., FEBS Letter, Vol.582, No.7, pp.1067-1072 (2008). + H. Okano, T. J Kobayashi, et al., Biophys. J., Vol.95, pp.1063-1074 (2008). + H. Ukai, T. J. Kobayashi, et al., Nature Cell Biology, Vol.9, No.11, pp.1327—1334 (2007). + R. Wang, C. Li, L. Chen, and K. Aihara, Proc. IEEE, Vol.96, No.8, pp.1361-1385 (2008). + X.-M. Zhao, R.-S. Wang, L. Chen, and K. Aihara, Nucleic Acids Res., Vol.36, No.9, e48 (2008). + X.-M. Zhao, R.-S. Wang, L. Chen, and K. Aihara, Journal of Bioinformatics and Computational Biology, Vol.7, No.2, pp.309-322 (2009). +L. Chen, R. Wang, C. Li, and K. Aihara, [[Modeling Biomolecular Networks in Cells: Structures and Dynamics>../../発表文献/書籍リスト/Modeling Biomolecular Networks in Cells]], Springer (2010). +D. Battogtokh, K. Aihara, and J. J. Tyson, Phys. Rev. Lett., Vol.96, 148102 (2006). +Y. Morishita, T. J. Kobayashi, and K. Aihara, Biophys. J., Vol.91, pp.2072-2081 (2006). +G. Kurosawa, K. Aihara, and Y. Iwasa, Biophys. J., Vol.91, pp.2015-2023 (2006). +C. Li, L. Chen, and K. Aihara, Physical Biology, Vol.3, pp.37-44 (2006). +C. Li, L. Chen, and K. Aihara, IEEE Trans. CAS-I, Vol.53, pp.2451-2458 (2006). +C. Li, L. Chen, and K. Aihara, PLoS Comput. Biol., Vol.2, e103 (2006). +R. Wang, L. Chen, and K. Aihara, J. Theor. Biol., Vol.242, pp.454-463 (2006). *Mathematical Modeling of Diseases [#kb74e6ec] We have been studied mathematical modeling of modern diseases that should be addressed emergently. Especially, we aim at understanding the essential mechanism and the origin of a disease and proposing a possible effective approach to avoid or treat the disease. We have discussed the efficacy of intermittent hormone suppression therapy for prostate cancer [1]. We have also carried out modeling of infection disease [2,3] and proposed a simulation system for analysis of the infection spread in the society. +A. Miyamura, G. Tanaka, T. Takeuchi, and K. Aihara, METR, University of Tokyo, 2006-32 (2006). +K. Ohtsuka, N. Konno, N. Masuda, and K. Aihara, Int. J. Bifurcation and Chaos, Vol.16, pp.3687-3693 (2006). +N. Sugimine, N. Masuda, N. Konno, and K. Aihara, Mathematical Biosciences, in press (2007). We have been studied mathematical modeling of diseases that should be addressed emergently. Especially, we aim at understanding the essential mechanism and the origin of a disease and proposing a possible effective approach to avoid or treat the disease. We have constructed mathematical models of prostate cancer and discussed the efficacy of intermittent hormone therapy compared with conventional continuous one [1-6]. We have also developed mathematical methods to predict clinical time series data of the biomarker [7] and optimize the schedule of treatments [8] towards practical applications. Infectious diseases like pandemic flu are the other major topic of our research, for which we have approached with mathematical model analyses [9] and large-scale simulations based on individual-based-models by using person trip data. + A. Ideta, G. Tanaka, T. Takeuchi, and K. Aihara, J. Nonlinear Sci. Vol.18, No.6, 593-614 (2008). + G. Tanaka, K. Tsumoto, S. Tsuji, and K. Aihara, Physica D , Vol.237, No.20, 2616-2627 (2008). + N. Shimada and K. Aihara, Math. Biosci. Vol.214, No.1/2, 134-139 (2008). + Q. Guo, Y. Tao, and K. Aihara, Int. J. Bifur. Chaos, Vol.18, No.12, 3789-3797 (2008). + Y. Tao, Q. Guo, and K. Aihara, J. Nonl. Sci., Vol.20, No.2, pp.219-240 (2010). + G. Tanaka, Y. Hirata, N. Bruchovsky, S. Goldenberg, and K. Aihara, Phil. Trans. R. Soc. A, Vol.368, pp.5029-5044 (2010). + Y. Hirata, N. Bruchovsky, and K. Aihara, J. Theor. Biol., Vol.264, No.2, pp.517-527 (2010). + T. Suzuki, N. Bruchovsky, and K. Aihara, Phil. Trans. R. Soc. A, Vol.368, pp.5045-5059 (2010). + B. Wang, K. Aihara, and B. J. Kim, Phys. Lett. A, Vol.373, pp.3877-3882 (2009).