Laboratories for Mathematics, Lifesciences, and Informatics

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  • 1 (2007-04-20 (金) 17:48:10)
  • 2 (2007-04-28 (土) 17:34:49)
  • 3 (2007-07-08 (日) 19:38:43)
  • 4 (2010-09-06 (月) 10:17:29)
  • 5 (2010-09-08 (水) 09:42:56)
  • 6 (2010-12-15 (水) 17:07:46)
  • 7 (2012-05-30 (水) 17:24:02)
  • 8 (2013-07-09 (火) 11:07:10)
  • 9 (2013-07-18 (木) 11:16:03)
  • 10 (2013-12-06 (金) 09:33:33)
  • 11 (2013-12-06 (金) 18:06:32)
  • 12 (2014-01-29 (水) 10:53:13)
  • 13 (2015-02-26 (木) 15:17:45)
  • 14 (2016-02-09 (火) 10:39:43)
  • 15 (2016-03-02 (水) 20:24:38)
  • 16 (2016-03-04 (金) 15:53:29)
  • 17 (2016-05-26 (木) 15:40:09)
  • 18 (2016-07-13 (水) 09:28:08)
  • 19 (2016-08-29 (月) 17:09:51)
  • 20 (2016-10-14 (金) 11:19:48)
  • 21 (2016-11-07 (月) 13:05:45)
  • 22 (2017-01-18 (水) 10:37:13)
  • 23 (2017-02-01 (水) 17:52:42)
  • 24 (2017-03-31 (金) 13:18:36)
  • 25 (2017-04-11 (火) 08:32:14)

_ Research topics

_ Neuroscience

Our research aims at understanding various behaviors of the brain, and applying them to engineering. Our methods include construcing models of a neuron and their networks in the brain and extracting a non-evident mathematical structure from them. So far we have got into the problem of the information processing [1], the modeling study of neurotransmitter [2], and the theoretical framework for the learning rule [3]. In addition, we are implementing the neuron models using analog circuits.

1. N. Masuda and K. Aihara, "Bridging Rate Coding and Temporal Spike Coding by Effect of Noise," Physical Review Letters, 88(24), 248101 (2002).
2. Kenji Morita, Kunichika Tsumoto, and Kazuyuki Aihara, "Possible effects of depolarizing GABAA conductance on the neuronal input-output relationship: a modeling study," J. Neurophysiol. 93, 3504-3523 (2005).
3. T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner, "Generalized Bienenstock-Cooper-Munro Rule for Spiking Neurons that Maximizes Information Transmission," PNAS, 102, 5239-5244 (2005).
4. T. Kohno and K. Aihara, "A MOSFET-based model of a class 2 nerve membrane," IEEE Trans. Neural Networks, 16, 754-773, (2005).

_ Nonlinear Science

Our aim of studying nonlinear science is to undestand the essence of complex behaviors through analyses of various 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 [1,2]. Furhter, we carry out some application studies including information processing based on nonlinear dynamics [3], information extraction from biological data [4], and deterministic chaos in wind.

1. H. Suzuki, S. Ito, and K. Aihara, "Double rotations," Disc. Cont. Dyn. Syst. 13, 515-532, (2005).
2. G. Tanaka, M. A. F. Sanjuan, and K. Aihara, "Crisis-induced Intermittency in Two Coupled Chaotic Maps: Towards Understanding Chaotic Itinerancy," PRE, 71(1), 016219, (2005).
3. G. Tanaka and K. Aihara, "Multistate Associative Memory with Parametrically Coupled Map Networks," Int. J. Bifurcation Chaos, 15(4), 1395-1410, (2005).
4. Y. Hirata, K. Judd, and K. Aihara, ``Characterizing chaotic response of a squid axon through generating partitions,'' Physics Letters A, 346, 141 (2005).

_ Mathematical Biology and Sociology

Our society is a complex adaptive system. We choose actions by estimating others internal state from their actions or other external factors. In our laboratory, we make and analyze mathematical models of these phenomena using nonlinear dynamics, game theory, and multi-agent-system [1].

1. N. Masuda and K. Aihara, "Spatial Prisoner's Dilemma optimally played in small-world networks," Phys. Lett. A, 485-490, (2003).

_ Genome Science

We analyze the nonlinear dynamics and stochasticity in genetic regulatory networks [1,2]. The major contributions are the followings: (1) We have proposed a systematic method to design a synthetic genetic switch [3]. (2) We have analytically shown that nonspecific interactions between regulatory proteins and background molecules can attenuate stochastic fluctuations. Moreover, we have numerically revealed that a genetic switch model with this mechanism is more stable than the one without this mechanism [4]. (3) We have proposed a systematic method based on the stoichiometric matrices of genetic regulatory networks and analytically decomposed stochastic fluctuation into its components [5].

1. T. Zhou, L. Chen and K. Aihara, "Molecular Communication through Stochastic Synchronization Induced by Extracellular Fructuations," PRL, 95, 178103, (2005).
2. D. Battogtokh, K. Aihara and J.J. Tyson, "Synchronization of Eukaryotic Cells by Periodic Forcing," PRL, 96, 148102, (2006).
3. T. Kobayashi, L. Chen, and K. Aihara, "Modeling Genetic Switches with Positive Feedback Loops," J. Theor. Biol., 221(3), 379-399 (2003).
4. Y. Morishita and K. Aihara, "Noise-Reduction through Interaction in Gene Expression and Biochemical Reaction Processes," J. Theor. Biol., 228, 315-325 (2004).
5. R. Tomioka, H. Kimura, T.J. Kobayashi and K. Aihara, "Multivariate Analysis of Noise in Genetic Regulatory Networks," J. Theor. Biol., 229, 501-521 (2004).