Satoru Iwata

Profile

Satoru Iwata 
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

March 1991 Bachelor of Engineering, Department of Mathematical Engineering and Information Physics, School of Engineering, The University of Tokyo
March 1993 Master of Engineering, Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, The University of Tokyo
April 1994 Research Associate, Research Institute for Mathematical Sciences, Kyoto University
April 1997 Lecturer, Graduate School of Engineering Science, Osaka University
April 2000 Associate Professor, Department of Mathematical Engineering and Information Physics, Graduate School of Engineering, The University of Tokyo
April 2001 Associate Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo
April 2006 Associate Professor, Research Institute for Mathematical Sciences, Kyoto University
April 2008 Professor, Research Institute for Mathematical Sciences, Kyoto University
February 2013 Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo

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.

 

CBS – Benucci

Profile

Andrea Benucci
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

Nov. 1998 B.S., Physics, University of Padova, IT
Sept. 1999 M.S., Computational Neuroscience, International School for Advanced Studies, IT
Nov. 2003 Ph.D., Neuroscience, ETH/University of Zurich, CH
July 2003 Postdoctoral Fellow, Smith-Kettlewell Eye Research Institute, US
Sept. 2005 Research Associate, Smith-Kettlewell Eye Research Institute, US
Sept. 2008 Senior Research Associate, University College London, UK
Sept. 2013 Team Leader, RIKEN Brain Science Institute, JP
April 2018 Team Leader, RIKEN Center for Brain Science, JP

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)

 

Mathematical Informatics 4th Laboratory

Statistical Informatics Laboratory(Mathematical Informatics 4th Laboratory)
– Deep Theory and Wide Applications. That’s Statistics –
HomePage of Lab.→
Fumiyasu Komaki
Fumiyasu Komaki

Professor
Tomonari Sei
Tomonari Sei

Associate Professor
Hiromichi Nagao
Hiromichi Nagao

Associate Professor
Teppei Ogihara
Teppei Ogihara

Associate Professor
Theoretical Statistics
We establish the basis of statistical methods. A wide range of mathematical tools such as information geometry, algebraic methods and algorithms play an essential role as well as probability theory.

Statistical Modeling
Statistical methods are used in various fields such as brain science, geoscience, finance, medical science, quantum information and sports science. We are developing specific statistical models for analyzing complex phenomena in the real world.

Data Assimilation
Data assimilation integrates large-scale numerical simulation models and observational big data based on Bayesian statistics. We develop algorithms of data assimilation towards applications to practical problems.

Understanding, Predicting and Controlling Dynamics
Dynamics are hidden in a lot of things in the real world such as weather, renewable energy, earthquakes, economics, brain, lives and diseases. We are studying time series analysis for understanding, predicting and/or controlling them.

Neuroinformatics and Computational Neuroscience

Neuroinformatics and Computational Neuroscience (RIKEN)
Benucci
Andrea Benucci

Adjunct Professor
Toyoizumi
Taro Toyoizumi

Adjunct Professor
Prediction and verification of decision by sense
We are interested in computational principles of brains from the viewpoint of decision by sense. We use artificial neural networks as models of neuronal circuits. In particular, by using state-of-the-art techniques of imaging and optical genetics for mouses, we verify the prediction derived from the models by experiments.
 
Computational Neuroscience
We study the mechanism how brain circuits adapt to and learn from the environment. By combining theoretical techniques from statistical physics and information theory and analysis of experimental data, we understand how the information processing of brains changes by learning, and find out the fundamental principle explaining the changes.

CBS – Toyoizumi

Profile

Taro Toyoizumi(豊泉 太郎)
toyoizumi

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

Mar. 2001 Graduated from the Department of Physics, School of Science, Tokyo Institute of Technology
Mar. 2003 Graduated from the Master Course of the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
Mar. 2006 Graduated from the Doctor Course of the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
Apr. 2006 – Feb. 2008 Japan Society for the Promotion of Science Postdoctoral Fellow (RIKEN Brain Science Institute, Center for Theoretical Neuroscience, Columbia University)
Mar. 2008 – Feb. 2010 The Robert Leet and Clara Guthrie Patterson Trust Postdoctoral Fellow (Center for Theoretical Neuroscience, Columbia University)
Apr. 2010 – Mar. 2011 Special Postdoctoral Researcher, RIKEN Brain Science Institute
Apr. 2011 – Mar. 2018 Team Leader, RIKEN Brain Science Institute
Apr. 2018 Team Leader, RIKEN Center for Brain Science
Apr. 2019 Adjunct Professor, Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo

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