Profile

Kazuhiro SATO

Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
Lecturer

Room 434, 7-3-1 6 Hongo, Bunkyo-ku, Tokyo 113-8656
Tel: +81-03-5841-6934

E-mail：kazuhiro@mist.i.u-tokyo.ac.jp

[Personal Site]

Biography

Research Topics

I am especially interested in solving control systems problems using methods of different fields such as optimization and machine learning. The following three topics are main research subjects.

1. Applications of optimization theory to control systems theory: We study what control systems problems can be solved using Riemannian optimization, proximal algorithm, submodular optimization, and so on.
2. Applications of control systems theory to optimization theory: We study what optimization problems can be efficiently solved using control systems theories such as hybrid systems, passivity, and so on.
3. Data-driven modeling for controlling systems: We study efficient modeling methods for controlling systems from time series data using optimization, machine learning, numerical analysis, and so on.

Selected Publications

• K. Sato:Riemannian optimal model reduction of linear port-Hamiltonian systems,
  Automatica, Vol. 98, pp. 428–434, 2018.
• K. Sato and H. Sato: Structure preserving H^2 optimal model reduction based on Riemannian trust-region method, IEEE Transactions on Automatic Control, Vol. 63, No. 2, pp. 505-511, 2018.
• K. Sato: Riemannian optimal control and model matching of linear port-Hamiltonian systems,
  IEEE Transactions on Automatic Control, Vol. 62, No. 12, pp. 6575-6581, 2017.

Profile

Yuki IZUMIDA

Graduate School of Frontier Sciences
The University of Tokyo
Lecturer

5-1-5 Kashiwanoha, Kashiwa-shi, Chiba-ken 277-8561
Tel: +81-4-7136-3934

E-mail：izumida@k.u-tokyo.ac.jp

[Personal Site]

Biography

Research Topics

I am studying fundamental aspects of complex and dynamical systems described by nonequilibrium statistical mechanics and nonlinear dynamics through mathematical modeling of specific systems in physics, engineering, and biology.
The research subjects I have being working on include theory of thermodynamic efficiency of nonequilibrium heat engines, dynamical modeling of a low-temperature-differential Stirling engine and elucidation of its rotational mechanism, and
construction of energetics of synchronization in coupled oscillators.

Selected Publications

• Y. Izumida: “Nonlinear dynamics analysis of a low-temperature-differential kinematic Stirling heat engine”, EPL(Europhysics Letters) 121, 50004 (2018)
• Y. Izumida, H. Kori, U. Seifert: “Energetics of synchronization in coupled oscillators rotating on circular trajectories”, Phys. Rev. E 94, 052221 (2016)
• Y. Izumida, K. Okuda: “Work Output and Efficiency at Maximum Power of Linear Irreversible Heat Engines Operating with a Finite-Sized Heat Source”,
  Phys. Rev. Lett. 112, 180603 (2014)
• Y. Izumida, K. Okuda: “Onsager coefficients of a finite-time Carnot cycle”,
  Phys. Rev. E 80, 021121 (2009)
• Y. Izumida, K. Okuda: “Molecular kinetic analysis of a finite-time Carnot cycle”,
  EPL(Europhysics Letters) 83, 60003 (2008)

Profile

Satoru Iwata　

Department of Mathematical Informatics
Graduate School of Information Science and Technology
The University of Tokyo
Professor

Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
Tel: +81 3 5841 7430 (ext. 27430) 

E-mail：iwata＠mist.i.u-tokyo.ac.jp

[ Personal Site ]

Biography

Research Topics

Solving Fundamental Problems in Mathematical Engineering
– Discrete Optimization: Design and Analysis of Efficient Algorithms on Matroids and Submodular Functions
– Discrete Mathematical Engineering: Engineering Applications of Discrete Optimization Methods (Systems Analysis and Chemoinformatics)

Selected Publications

S. Iwata and M. Takamatsu: Index minimization of differential-algebraic equations in hybrid analysis for circuit simulation, Mathematical Programming, 103 (2010), 105-121.

J. F. Geelen, S. Iwata, and K. Murota: The linear delta-matroid parity problem, Journal of Combinatorial Theory, B88 (2003), 377-398.

S. Iwata, L. Fleischer, and S. Fujishige: A combinatorial strongly polynomial algorithm for minimizing submodular functions, Journal of the ACM, 48 (2001), 761-777.

Profile

Andrea Benucci

Adjunct Professor,
Department of Mathematical Informatics, Graduate School of Information
Science and Technology, University of Tokyo

RIKEN Center for Brain Science, Team Leader

2-1 Hirosawa, Wako-shi, Saitama 351-0198
Tel: +81-48-467-5203
Fax:

E-mail：andrea.benucci@riken.jp

[Home Page]

略歴

Research Themes

Computations in biological neural networks, in particular linear and non-linear analyses of large-scale neuronal recordings.



My research aims at linking neural architectures to the underlying computations. To do so, I integrate experimental methods for all-optical dissection of neuronal circuits with large-scale dynamical network models based on artificial neural networks (aNNs). The connectivity architecture of aNNs closely mirrors that of biological neural networks, thus representing an effective theoretical framework to unify computational, algorithmic, and implementation levels of analysis.

Selected Publications

Aoki, R., Tsubota, T., Goya, Y., Benucci, A., An automated platform for high-throughput mouse behavior and physiology with voluntary head-fixation. Nature Comms., 8:1196, (2017)

Benucci, A., Saleem, A.B., Carandini, M. Adaptation maintains population homeostasis in primary visual cortex. Nature Neurosci., Jun; 16(6):724-9, (2013)

Pearson R.A., Barber A.C., Rizzi M., Xue T., West E.L., Duran Y., Smith A.J., Chuang J.Z., Azam S.A., Luhmann U.F.O., Benucci A., Sung C.H., Carandini M., Yau K.W., Sowden J.C., Ali R.R. Restoration of vision after transplantation of photoreceptors. Nature, 485(7396):99-103, (2012)

Benucci, A., Ringach, D.L., Carandini, M. Coding of stimulus sequences by population responses in visual cortex. Nature Neurosci., 12(10):1317-24, (2009)

Profile

Taro Toyoizumi（豊泉 太郎）

Adjunct Professor,
Department of Mathematical Informatics, Graduate School of Information
Science and Technology, University of Tokyo

RIKEN Center for Brain Science, Team Leader

2-1 Hirosawa, Wako-shi, Saitama 351-0198
Tel: +81-48-467-9644
Fax: +81-48-467-9670

E-mail：taro.toyoizumi@riken.jp

[Home Page]

Curriculum Vitae

Research Themes

Computational Neuroscience, Theory of Neural Adaptation using
Statistical Physics and Information Theory Tools

Our research is in the field of Computational Neuroscience. Computer
models are used to study how information is processed in the brain and
how the brain circuits adapt to and learn from the environment. We
employ analytical techniques from statistical physics and information
theory to investigate key functional properties for neuronal circuits.
We use these techniques to reduce diverse experimental findings into a
few core concepts that robustly explain the phenomena of interest.
We are particularly interested in activity-dependent forms of
plasticity in the brain, which are known to have large impacts on
learning, memory, and development. With the aid of mathematical
models, we seek a theory that unites the cellular level plasticity
rules and the circuit level adaptation in different brain areas and
animal species. Efficacy of neurons to represent and retain
information is estimated from the structure and behavior of resulting
circuits.

Selected Publications

Isomura T and Toyoizumi T.: “Error-Gated Hebbian Rule: A Local
Learning Rule for Principal and Independent Component Analysis”
Scientific Reports , 8, 1835 (2018), doi:10.1038/s41598-018-20082-0

Buckley C L and Toyoizumi T.: “A theory of how active behavior
stabilizes neural activity: neural gain modulation by closed-loop
environmental feedback”
PLOS Computational Biology , 14, e1005926 (2018), doi:
10.1371/journal.pcbi.1005926

Kuśmierz Ł and Toyoizumi T.: “Emergence of Lévy walks from
second-order stochastic optimization”
Physical Review Letters, 119, 250601 (2017), doi: 10.1103/PhysRevLett.119.250601

Tajima S, Mita T, Bakkum D, Takahashi H, and and Toyoizumi T.:
“Locally embedded presages of global network bursts”
Proc. Natl. Acad. Sci, 114, 9517-9522 (2017), doi: 10.1073/pnas.1705981114

Huang H and Toyoizumi T.: “Clustering of neural code words revealed by
a first-order phase transition”
Physical Review E, 93, 062416 (2016), doi: 10.1103/PhysRevE.93.062416

Shimazaki H, Sadeghi K, Ishikawa T, Ikegaya Y, and Toyoizumi T.:
“Simultaneous silence organizes structured higher-order interactions
in neural populations.”
Sci Rep, 5, 9821 (2015), doi: 10.1038/srep09821

Toyoizumi T, Kaneko M, Stryker MP, and Miller KD.: “Modeling the
dynamic interaction of Hebbian and homeostatic plasticity”
Neuron, 84(2), 497-510 (2014), doi: 10.1016/j.neuron.2014.09.036

Toyoizumi T, Miyamoto H, Yazaki-Sugiyama Y, Atapour N, Hensch TK, and
Miller KD.: “A theory of the transition to critical period plasticity:
inhibition selectively suppresses spontaneous activity” Neuron, 80(1),
51-63 (2013), doi: 10.1016/j.neuron.2013.07.022

Toyoizumi T and Abbott LF.: “Beyond the edge of chaos: Amplification
and temporal integration by recurrent networks in the chaotic regime”
Physical Review, E 84(5), 051908 (2011), doi: 10.1103/PhysRevE.84.051908

Toyoizumi T, Aihara K, and Amari S.: “Fisher information for
spike-based population decoding.”
Phys Rev Lett, 97(9), 98102 (2006), doi: 10.1103/PhysRevLett.97.098102

Toyoizumi T, Pfister JP, Aihara K, and Gerstner W.: “Generalized
Bienenstock-Cooper-Munro rule for spiking neurons that maximizes
information transmission.”
Proc Natl Acad Sci U S A, 102(14), 5239-44 (2005), doi: 10.1073/pnas.0500495102

Profile

Kantaro Fujiwara

Project Associate Professor

International Research Center for Neurointelligence, The University of Tokyo

Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

 

7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan

Tel: +81-3-5841-8247 (Ext. 28247)

Fax:

 

E-mail：fujiwara＠mist.i.u-tokyo.ac.jp

 

[Home page]

C.V.

Research Themes

The main topics are computational neuroscience and data analysis of neural systems.

1. Computational Neuroscience

Mathematical modeling of neural networks. Modeling various neuronal phenomena such as learning and adaptation.

2. Data Analysis of Neural Systems

Establishing mathematical theories and novel analysis method of neuronal data.

3. Biological Information Processing

Mathematical modeling of pancreatic beta cell and diabetes.

Selected Publications

– R. Nomura , Y-Z Liang, K. Morita, K. Fujiwara and T. Ikeguchi,
Threshold-varying integrate-and-fire model reproduces distributions of spontaneous blink intervals,
PLOS ONE 13, 10, e0206528 (2018)

– T. Kobayashi, Y. Shimada, K. Fujiwara and T. Ikeguchi,
Reproducing Infra-Slow Oscillations with Dopaminergic Modulation,
Scientific Reports, 7, 2411 (2017)

– H. Ando and K. Fujiwara,
Numerical analysis of bursting activity in an isolated pancreatic β-cell model,
Nonlinear Theory and its Applications, 7, pp. 217-225 (2016)

– K. Fujiwara, H. Suzuki, T. Ikeguchi and K. Aihara,
Method for analyzing time-varying statistics on point process data with multiple trials,
Nonlinear Theory and its Applications, 6, pp. 38-46 (2015)