Basic Information

Faculty

• 理学部

Academic Degree

• Master of Science(Tohoku University)
• PhD(Tohoku University)

Academic Resume (Graduate Schools)

• Tohoku University, Faculty of Science, Department of Physics II, 修了
• Tohoku University, Faculty of Science, Department of Physics II, 修了

Academic Resume (Undergraduate School/Majors)

• Tohoku University, Faculty of Science, Department of Physics, 卒業

Research History

• From 2008, To 2009

  京都大学 理学(系)研究科(研究院), 准教授
• From 2001, To 2007

  京都大学 理学(系)研究科(研究院), 助教授
• From 1989, To 2001

  Kyoto University, Faculty of Science, 助教授
• From 1987, To 1988

  Kyoto University, Faculty of Science, 助手

Language of Instruction

• English

ID,URL

• ORCID ID

  0000-0002-5745-7736

• J-Global ID

  200901033599595493

• External link

  https://sites.google.com/view/shigerushinomoto/

Website(s) (URL(s))

• http://www.ton.scphys.kyoto-u.ac.jp/~shino/

researchmap URL

• https://researchmap.jp/shigerushinomoto

Research

Research Topics, Overview of the research

• Research Topics

  Theoretical Neuroscience

Research Interests

• 包括脳ネットワーク
• 統合脳・脳の高次機能学
• 三次元図形の短期記憶
• LTD
• 相対的位置識別
• 引き込み現象
• コインシデンスディテクタ系
• 秩序パラメター
• フェイズダイナミクス
• 乱流ゆらぎ
• くり込まれた輸送係数
• 神経回路網
• 時空間学習則
• 回転子模型
• 海馬
• 時間依存ポアソン過程
• MT野
• MST野
• 神経スパイク時系列
• 時間依存ポアソンモデル
• 細胞内通電
• スパイク生成モデル
• 細胞分類
• 待ち時間課程
• スパイク統計
• 神経スパイクコード
• 時間コード
• 前頭連合野
• 細胞内通電実験
• 待ち時間課題
• 神経スパイク信号
• 局所変動係数

Research Areas

• Informatics, Intelligent informatics
• Informatics, Sensitivity (kansei) informatics
• Informatics, Soft computing

Papers

• 神経科学の梁山泊

  篠本 滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Dec. 2002

  
• 旅行記 : アンニョンハセヨ

  篠本 滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Jun. 2000

  
• 旅行記 : ビバ・エスパーニア

  篠本 滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Sep. 1999

  
• 旅行記 : 神の国イスラエル

  篠本 滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Sep. 1998

  
• これからどこをめざすか

  篠本 滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Jun. 1998

  
• Mutual Entrainment in Oscillator Lattices with Nonvariational Type Interaction : General and Mathematical Physics

  Hidetsugu SAKAGUCHI; Shigeru SHINOMOTO; Yoshiki KURAMOTO; Department of Physics Kyoto University; Department of Physics Kyoto University; Department of Physics Kyoto University

  Progress of theoretical physics = Progress of theoretical physics, 25 May 1988

  
• Phase Transitions and Their Bifurcation Analysis in a Large Population of Active Rotators with Mean-Field Coupling : General and Mathematiccal Physics

  Hidetsugu SAKAGUCHI; Shigeru SHINOMOTO; Yoshiki KURAMOTO; Department of Physics Kyoto University; Department of Physics Kyoto University; Department of Physics Kyoto University

  Progress of theoretical physics = Progress of theoretical physics, 25 Mar. 1988

  
• Statistical Properties of Neural Networks : Condensed Matter and Statistical Physics

  Shigeru SHINOMOTO; Research Institute for Fundamental Physics Kyoto University

  Progress of theoretical physics = Progress of theoretical physics, 25 Jun. 1986

  
• Phase Transitions in Active Rotator Systems : Condensed Matter and Statistical Physics

  Shigeru SHINOMOTO; Yoshiki KURAMOTO; Research Institute for Fundamental Physics Kyoto University; Department of Physics Kyoto University

  Progress of theoretical physics = Progress of theoretical physics, 25 May 1986

  
• Random Frequency Modulation of a Forced Nonlinear Oscillator : Condensed Matter and Statistical Physics

  Yoshiki KURAMOTO; Shigeru SHINOMOTO; Research Institute for Fundamental Physics Kyoto University; Research Institute for Fundamental Physics Kyoto University

  Progress of theoretical physics = Progress of theoretical physics, 25 Mar. 1985

  
• Note on the Hole Theory of Liquids : Free Volume of the Hard Particle Systems and Its Relation to Melting : Condensed Matter and Statistical Physics

  Shigeru SHINOMOTO; Department of Physics Tohoku University

  Progress of theoretical physics = Progress of theoretical physics, 25 Jun. 1984

  
• Hole Model Approach to Melting : Hard Sphere System

  Shigeru SHINOMOTO; Department of Physics Tohoku University

  Progress of theoretical physics = Progress of theoretical physics, 25 Sep. 1983

  
• Estimating Nonstationary Inputs from Firing Rate and Non-Poisson Irregularity in a Single Spike Train

  Kim, Hideaki; Shinomoto, Shigeru

  Neural Coding 2012, 2012

  
• Inferring nonstationary input activities from non-Poisson firing of a neuron

  Shinomoto, Shigeru

  Neural Coding 2012, 2012

  
• A Bayesian approach for estimating time-varying input signals from membrane potential of a neuron

  Kobayashi, Ryota; Shinomoto, Shigeru; Lansky, Pe

  Neural Coding 2012, 2012

  
• Estimating the firing rate

  Shinomoto, Shigeru

  Analysis of Parallel Spike Trains, 2010

  
• Predicting spike times of any cortical neuron

  Kobayashi, R.; Tsubo, Y.; Shinomoto, S.

  Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience. doi, 2009

  
• Histogram binwidth and kernel bandwidth selection for the spike-rate estimation

  Shimazaki, Hideaki; Shinomoto, Shigeru

  BMC Neuroscience, 2009

  
• Online Learning of a Decision Boundary with and Without Queries

  Shinomoto, Shtgeru

  On-Line Learning in Neural Networks, 2009

  
• Neuronal firing patterns and cerebral cortical functions

  Shinomoto, Shigeru; Kim, Hideaki; Shimokawa; Takeaki

  BMC Neuroscience, 2009

  
• A fast-computational spiking neuron model adaptable to any cortical neuron

  Kobayashi, Ryota; Tshubo, Yasuhiro; Shinomoto, Shigeru

  BMC Neuroscience, 2009

  
• Kernel width optimization in the spike-rate estimation

  Shimazaki, Hideaki; Shinomoto, Shigeru

  Neural coding, 2007

  
• READING NEURONAL SPIKE SEQUENCES

  Shinomoto, S.

  KOREAN JOURNAL OF PHYSIOLOGY AND PHARMACOLOGY, 2006

  
• Preface (Oscillation, Chaos and Network Dynamics in Nonlinear Science)

  Kuramoto, Yoshiki; Shinomoto, Shigeru; Nakao, Hiroya; Ohta, Takao

  Progress of theoretical physics. Supplement, 2006

  
• Oscillation, chaos and network dynamics in nonlinear science: OCNN 2004; proceedings of the International Symposium on Nonlinear Oscillations; Kyoto University; November 25-28, 2004

  Shinomoto, Shigeru; International; Symposium on Nonlinear Oscillations

  2006

  
• Search for optimal measure for discriminating spike trains with different randomness

  Miura, Keiji; Okada, M.; Shinomoto, S.

  arXiv preprint q-bio/0505024, 2005

  
• Fac. of Science

  Shinomoto, Shigeru; Sakaguchi, H.; Kuramoto, S.; Kuramoto, Y.

  Massively Parallel, Optical, and Neural Computing in Japan: A Survey of Major Research Activities, 1992

  
• Information classification acquired by organization of neuronic connections

  Shinomoto, Shigeru

  Relaxation in complex systems and related topics, 1990

  
• Reconstruction of Images in the Visual Cortex

  Shinomoto, S.

  Cooperative Dynamics in Complex Physical Systems, 1989

  
• A memory with cognitive ability

  Shinomoto, Shigeru

  Dynamic Interactions in Neural Networks: Models and Data, 1989

  
• Active rotator model for large populations of oscillatory and excitable elements

  Kuramoto, Yoshiki; Shinomoto, Shigeru; Sakaguchi, Hidetsugu

  Mathematical topics in population biology, morphogenesis and neurosciences, 1987

  
• Cooperative phenomena in two-dimensional active rotator systems

  Shinomoto, Shigeru; Kuramoto, Yoshiki

  Progress of theoretical physics, 1986

  
• Cooperative phenomena in two-dimensional active rotator systems

  Shinomoto, Shigeru; Kuramoto, Yoshiki

  Progress of theoretical physics, 1986

  
• Mechanical optimization of skateboard pumping

  Florian Kogelbauer; Shinsuke Koyama; Daniel E. Callan; Shigeru Shinomoto

  Physical Review Research, 05 Aug. 2024

  
• Inference of Monosynaptic Connections from Parallel Spike Trains: A Review

  Ryota Kobayashi; Shigeru Shinomoto

  Neuroscience Research, Aug. 2024

  
• Detection and categorization of severe cardiac disorders based solely on heart period measurements.

  Shigeru Shinomoto; Yasuhiro Tsubo; Yoshinori Marunaka

  Scientific reports, 11 Oct. 2022

  
• Statistical physics of discovering exogenous and endogenous factors in a chain of events

  Shinsuke Koyama; Shigeru Shinomoto

  Physical Review Research, 14 Dec. 2020

  
• A convolutional neural network for estimating synaptic connectivity from spike trains

  Daisuke Endo; Ryota Kobayashi; Ramon Bartolo; Bruno B. Averbeck; Yasuko Sugase-Miyamoto; Kazuko Hayashi; Kenji Kawano; Barry J. Richmond; Shigeru Shinomoto

  Scientific Reports, Dec. 2021

  
• Estimating the time-varying reproduction number of COVID-19 with a state-space method.

  Shinsuke Koyama; Taiki Horie; Shigeru Shinomoto

  PLoS computational biology, Jan. 2021, Peer-reviewed

  
• Improved hyperacuity estimation of spike timing from calcium imaging.

  Huu Hoang; Masa-Aki Sato; Shigeru Shinomoto; Shinichiro Tsutsumi; Miki Hashizume; Tomoe Ishikawa; Masanobu Kano; Yuji Ikegaya; Kazuo Kitamura; Mitsuo Kawato; Keisuke Toyama

  Scientific reports, 20 Oct. 2020, Peer-reviewed

  
• Reconstructing neuronal circuitry from parallel spike trains

  Kobayashi, R.; Kurita, S.; Kurth, A.; Kitano, K.; Mizuseki, K.; Diesmann, M.; Richmond, B.J.; Shinomoto, S.

  Nature Communications, 2019, Peer-reviewed

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• Inferring objects from a multitude of oscillations

  Furukawa, M.; Shinomoto, S.

  Neural Computing and Applications, 2018, Peer-reviewed

  
• Identifying exogenous and endogenous activity in social media

  Fujita, K.; Medvedev, A.; Koyama, S.; Lambiotte, R.; Shinomoto, S.

  Physical Review E, 2018, Peer-reviewed

  
• Computational Neuroscience: Mathematical and Statistical Perspectives

  Kass, R.E.; Amari, S.-I.; Arai, K.; Brown, E.N.; Diekman, C.O.; Diesmann, M.; Doiron, B.; Eden, U.T.; Fairhall, A.L.; Fiddyment, G.M.; Fukai, T.; Grün, S.; Harrison, M.T.; Helias, M.; Nakahara, H.; Teramae, J.-N.; Thomas, P.J.; Reimers, M.; Rodu, J.; Rotstein, H.G.; Shea-Brown, E.; Shimazaki, H.; Shinomoto, S.; Yu, B.M.; Kramer, M.A.

  Annual Review of Statistics and Its Application, 2018, Peer-reviewed

  
• Correlations and forecast of death tolls in the Syrian conflict

  Fujita, K.; Shinomoto, S.; Rocha, L.E.C.

  Scientific Reports, 2017, Peer-reviewed

  
• Emergence of event cascades in inhomogeneous networks

  Onaga, T.; Shinomoto, S.

  Scientific Reports, 2016, Peer-reviewed

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• Efficient information transfer by poisson neurons

  Kostal, L.; Shinomoto, S.

  Mathematical Biosciences and Engineering, 2016, Peer-reviewed

  
• Similarity in neuronal firing regimes across mammalian species

  Mochizuki, Y.; Onaga, T.; Shimazaki, H.; Shimokawa, T.; Tsubo, Y.; Kimura, R.; Saiki, A.; Sakai, Y.; Isomura, Y.; Fujisawa, S.; Shibata, K.-I.; Hirai, D.; Furuta, T.; Kaneko, T.; Takahashi, S.; Nakazono, T.; Ishino, S.; Sakurai, Y.; Kitsukawa, T.; Lee, J.W.; Lee, H.; Jung, M.W.; Babul, C.; Maldonado, P.E.; Takahashi, K.; Arce-McShane, F.I.; Ross, C.F.; Sessle, B.J.; Hatsopoulos, N.G.; Brochier, T.; Riehle, A.; Chorley, P.; Grün, S.; Nishijo, H.; Ichihara-Takeda, S.; Funahashi, S.; Shima, K.; Mushiake, H.; Yamane, Y.; Tamura, H.; Fujita, I.; Inaba, N.; Kawano, K.; Kurkin, S.; Fukushima, K.; Kurata, K.; Taira, M.; Tsutsui, K.-I.; Ogawa, T.; Komatsu, H.; Koida, K.; Toyama, K.; Richmond, B.J.; Shinomoto, S.

  Journal of Neuroscience, 2016, Peer-reviewed

  
• Estimation of Neuronal Firing Rate

  Shinomoto, Shigeru

  Encyclopedia of Computational Neuroscience, 2015, Peer-reviewed

  
• Microscopic instability in recurrent neural networks

  Yamanaka, Y.; Amari, S.-I.; Shinomoto, S.

  Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2015, Peer-reviewed

  
• Analog and digital codes in the brain

  Yasuhiro Mochizuki; Shigeru Shinomoto

  PHYSICAL REVIEW E, Feb. 2014, Peer-reviewed

  
• Bursting activity spreading through asymmetric interactions

  Tomokatsu Onaga; Shigeru Shinomoto

  10TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY AND INTERNET-BASED SYSTEMS SITIS 2014, 2014, Peer-reviewed

  
• Bursting transition in a linear self-exciting point process

  Onaga, T.; Shinomoto, S.

  Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2014, Peer-reviewed

  
• Analog and digital codes in the brain

  Mochizuki, Y.; Shinomoto, S.

  Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 2014, Peer-reviewed

  
• Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation

  Kim, H.; Shinomoto, S.

  Mathematical Biosciences and Engineering, 2014, Peer-reviewed

  
• " Research Institute for Fundamental Physics, Kyoto University, Kyoto 606, Japan

  KURAMOTO, Yoshiki; SHINOMOTO, Shigeru; SAKAGUCHI, Hidetsugu

  Mathematical Topics in Population Biology, Morphogenesis and Neurosciences: Proceedings of an International Symposium held in Kyoto, November 10--15, 1985, 2013, Peer-reviewed

  
• Difference in modes of firing rate modulation between cortical areas

  Mochizuki, Yasuhiro; Shinomoto, Shigeru

  BMC Neuroscience, 2013, Peer-reviewed

  
• Information transmission using non-poisson regular firing

  Koyama, Shinsuke; Omi, Takahiro; Kass, Robert E; Shinomoto, Shigeru

  Neural computation, 2013, Peer-reviewed

  
• Estimating nonstationary input signals from a single neuronal spike train

  Hideaki Kim; Shigeru Shinomoto

  PHYSICAL REVIEW E, Nov. 2012, Peer-reviewed

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• Estimating nonstationary input signals from a single neuronal spike train.

  Hideaki Kim; Shigeru Shinomoto

  Physical review. E, Statistical, nonlinear, and soft matter physics, Nov. 2012, Peer-reviewed

  
• Detection limit for rate fluctuations in inhomogeneous Poisson processes.

  Toshiaki Shintani; Shigeru Shinomoto

  Physical review. E, Statistical, nonlinear, and soft matter physics, Apr. 2012, Peer-reviewed

  
• Neurons as ideal change-point detectors.

  Hideaki Kim; Barry J Richmond; Shigeru Shinomoto

  Journal of computational neuroscience, Feb. 2012, Peer-reviewed

  
• Optimizing time histograms for non-Poissonian spike trains.

  Takahiro Omi; Shigeru Shinomoto

  Neural computation, Dec. 2011, Peer-reviewed

  
• Estimation of time-dependent input from neuronal membrane potential.

  Ryota Kobayashi; Shigeru Shinomoto; Petr Lansky

  Neural computation, Dec. 2011, Peer-reviewed

  
• Optimal observation time window for forecasting the next earthquake.

  Takahiro Omi; Ido Kanter; Shigeru Shinomoto

  Physical review. E, Statistical, nonlinear, and soft matter physics, Feb. 2011, Peer-reviewed

  
• Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times.

  Satoshi Yamauchi; Hideaki Kim; Shigeru Shinomoto

  Frontiers in computational neuroscience, 2011, Peer-reviewed

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• Deciphering elapsed time and predicting action timing from neuronal population signals.

  Shigeru Shinomoto; Takahiro Omi; Akihisa Mita; Hajime Mushiake; Keisetsu Shima; Yoshiya Matsuzaka; Jun Tanji

  Frontiers in computational neuroscience, 2011, Peer-reviewed

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• A characterization of the time-rescaled gamma process as a model for spike trains

  Takeaki Shimokawa; Shinsuke Koyama; Shigeru Shinomoto

  JOURNAL OF COMPUTATIONAL NEUROSCIENCE, Aug. 2010, Peer-reviewed

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• Kernel bandwidth optimization in spike rate estimation

  Hideaki Shimazaki; Shigeru Shinomoto

  JOURNAL OF COMPUTATIONAL NEUROSCIENCE, Aug. 2010, Peer-reviewed

  
• Fitting a stochastic spiking model to neuronal current injection data

  Shigeru Shinomoto

  NEURAL NETWORKS, Aug. 2010, Peer-reviewed

  
• A non-universal aspect in the temporal occurrence of earthquakes

  Xiaoxue Zhao; Takahiro Omi; Nanae Matsuno; Shigeru Shinomoto

  NEW JOURNAL OF PHYSICS, Jun. 2010, Peer-reviewed

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• Bayesian estimation of the time-varing rate and irregularity of neuronal firing

  Takeaki Shimokawa; Shigeru Shinomoto

  BMC NEUROSCIENCE, Jul. 2009, Peer-reviewed

  
• Estimating Instantaneous Irregularity of Neuronal Firing

  Takeaki Shimokawa; Shigeru Shinomoto

  NEURAL COMPUTATION, Jul. 2009, Peer-reviewed

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• Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold

  Ryota Kobayashi; Yasuhiro Tsubo; Shigeru Shinomoto

  FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, Jul. 2009, Peer-reviewed

  
• Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex

  Shigeru Shinomoto; Hideaki Kim; Takeaki Shimokawa; Nanae Matsuno; Shintaro Funahashi; Keisetsu Shima; Ichiro Fujita; Hiroshi Tamura; Taijiro Doi; Kenji Kawano; Naoko Inaba; Kikuro Fukushima; Sergei Kurkin; Kiyoshi Kurata; Masato Taira; Ken-Ichiro Tsutsui; Hidehiko Komatsu; Tadashi Ogawa; Kowa Koida; Jun Tanji; Keisuke Toyama

  PLOS COMPUTATIONAL BIOLOGY, Jul. 2009, Peer-reviewed

  
• JASMINE - Japan Astrometry Satellite Mission for INfrared Exploration: Data Analysis and Accuracy Assessment with a Kalman Filter

  Yoshiyuki Yamada; Takeaki Shimokawa; Shigeru Shinomoto; Taihei Yano; Naoteru Gouda

  ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XVIII, 2009, Peer-reviewed

  
• A benchmark test for a quantitative assessment of simple neuron models

  Renaud Jolivet; Ryota Kobayashi; Alexander Rauch; Richard Naud; Shigeru Shinomoto; Wulfram Gerstner

  JOURNAL OF NEUROSCIENCE METHODS, Apr. 2008, Peer-reviewed

  
• Can distributed delays perfectly stabilize dynamical networks?

  Takahiro Omi; Shigeru Shinomoto

  PHYSICAL REVIEW E, Apr. 2008, Peer-reviewed

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• Erratum: 'A solution to the controversy between rate and temporal coding' (Statistics in Medicine 10.1002/SIM.2932)

  Shigeru Shinomoto; Shinsuke Koyama

  Statistics in Medicine, 10 Nov. 2007, Peer-reviewed

  
• Reverberating activity in a neural network with distributed signal transmission delays

  Takahiro Omi; Shigeru Shinomoto

  PHYSICAL REVIEW E, Nov. 2007, Peer-reviewed

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• A solution to the controversy between rate and temporal coding (vol 26, pg 4686, 2007)

  Shigeru Shinomoto; Shinkuke Koyama

  STATISTICS IN MEDICINE, Nov. 2007, Peer-reviewed

  
• A solution to the controversy between rate and temporal coding

  Shigeru Shinomoto; Shinsuke Koyama

  STATISTICS IN MEDICINE, Sep. 2007, Peer-reviewed

  
• A method for selecting the bin size of a time histogram

  Hideaki Shimazaki; Shigeru Shinomoto

  NEURAL COMPUTATION, Jun. 2007, Peer-reviewed

  
• MST neurons code for visual motion in space independent of pursuit eye movements

  Naoko Inaba; Shigeru Shinomoto; Shigeru Yamane; Aya Takemura; Kenji Kawano

  JOURNAL OF NEUROPHYSIOLOGY, May 2007, Peer-reviewed

  
• Inference of intrinsic spiking irregularity based on the Kullback-Leibler information

  Shinsuke Koyama; Shigeru Shinomoto

  BIOSYSTEMS, May 2007, Peer-reviewed

  
• Phase transitions in the estimation of event rate: a path integral analysis

  S. Koyama; T. Shimokawa; S. Shinomoto

  JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, May 2007, Peer-reviewed

  
• Predicting spike times from subthreshold dynamics of a neuron

  R. Kobayashi; S. Shinomoto

  Advances in Neural Information Processing Systems, 2007, Peer-reviewed

  
• A recipe for optimizing a time-histogram with variable bin sizes

  Shimazaki, Hideaki; Shinomoto, Shigeru

  Advances in Neural Information Processing Systems, 2007, Peer-reviewed

  
• State space method for predicting the spike times of a neuron

  Ryota Kobayashi; Shigeru Shinomoto

  PHYSICAL REVIEW E, Jan. 2007, Peer-reviewed

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• Inhibitory neurons can facilitate rhythmic activity in a neural network

  Takeaki Shimokawa; Shigeru Shinomoto

  PHYSICAL REVIEW E, Jun. 2006, Peer-reviewed

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• Progress of Theoretical Physics Supplement: Preface

  Kuramoto, Y.; Shinomoto, S.; Nakao, H.; Ohta, T.

  Progress of Theoretical Physics Supplement, 2006, Peer-reviewed

  
• Faithful and unfaithful students in time series learning

  R Kobayashi; Y Miyazaki; S Shinomoto

  IMA JOURNAL OF APPLIED MATHEMATICS, Oct. 2005, Peer-reviewed

  
• Empirical Bayes interpretations of random point events

  S Koyama; S Shinomoto

  JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, Jul. 2005, Peer-reviewed

  
• Regional and laminar differences in in vivo firing patterns of primate cortical neurons

  S Shinomoto; Y Miyazaki; H Tamura; Fujita, I

  JOURNAL OF NEUROPHYSIOLOGY, Jul. 2005, Peer-reviewed

  
• A measure of local variation of inter-spike intervals

  S Shinomoto; K Miura; S Koyama

  BIOSYSTEMS, Jan. 2005, Peer-reviewed

  
• Histogram bin width selection for time-dependent Poisson processes

  S Koyama; S Shinomoto

  JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, Jul. 2004, Peer-reviewed

  
• Predicting spike timings of current-injected neurons

  Y Tsubo; T Kaneko; S Shinomoto

  NEURAL NETWORKS, Mar. 2004, Peer-reviewed

  
• Differences in spiking patterns among cortical neurons

  S Shinomoto; K Shima; J Tanji

  NEURAL COMPUTATION, Dec. 2003, Peer-reviewed

  
• Learning of time series through neuron-to-neuron instruction

  Y Miyazaki; W Kinzel; S Shinomoto

  JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, Feb. 2003, Peer-reviewed

  
• New classification scheme of cortical sites with the neuronal spiking characteristics

  S Shinomoto; K Shima; J Tanji

  NEURAL NETWORKS, Dec. 2002, Peer-reviewed

  
• Recording site dependence of the neuronal spiking statistics

  S Shinomoto; Y Sakai; H Ohno

  BIOSYSTEMS, Oct. 2002, Peer-reviewed

  
• Modeling spiking behavior of neurons with time-dependent Poisson processes

  S. Shinomoto; Y. Tsubo

  Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Oct. 2001, Peer-reviewed

  
• Modeling spiking behavior of neurons with time-dependent Poisson processes

  S Shinomoto; Y Tsubo

  PHYSICAL REVIEW E, Oct. 2001, Peer-reviewed

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• Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons

  Y Sakai; S Funahashi; S Shinomoto

  NEURAL NETWORKS, Oct. 1999, Peer-reviewed

  
• The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex

  S Shinomoto; Y Sakai; S Funahashi

  NEURAL COMPUTATION, May 1999, Peer-reviewed

  
• Inter-spike interval statistics of cortical neurons

  S Shinomoto; Y Sakai

  FOUNDATIONS AND TOOLS FOR NEURAL MODELING, PROCEEDINGS, VOL I, 1999, Peer-reviewed

  
• Spiking mechanisms of cortical neurons

  S Shinomoto; Y Sakai

  PHILOSOPHICAL MAGAZINE B-PHYSICS OF CONDENSED MATTER STATISTICAL MECHANICS ELECTRONIC OPTICAL AND MAGNETIC PROPERTIES, May 1998, Peer-reviewed

  
• Spiking mechanisms of cortical neurons

  Shigeru Shinomoto; Yutaka Sakai

  Philosophical Magazine B: Physics of Condensed Matter; Statistical Mechanics, Electronic, Optical and Magnetic Properties, 1998, Peer-reviewed

  
• Stimulus-dependent induction of long-term potentiation in CA1 area of the hippocampus: Experiment and model

  T Aihara; M Tsukada; MAC Crair; S Shinomoto

  HIPPOCAMPUS, 1997, Peer-reviewed

  
• Spatial-temporal aspects of learning and memory in the hippocampal-corticalmemory system

  M.Tsukada; T. Aihara; H.Saito; H.Kato; M.C.Crair; S.Shinomoto

  Recent Res. Devel. in Biological Cybernetics, 1996

  
• LEARNING A DECISION BOUNDARY FROM STOCHASTIC EXAMPLES - INCREMENTAL ALGORITHMS WITH AND WITHOUT QUERIES

  Y KABASHIMA; S SHINOMOTO

  NEURAL COMPUTATION, Jan. 1995, Peer-reviewed

  
• LEARNING-CURVES FOR ERROR MINIMUM AND MAXIMUM-LIKELIHOOD ALGORITHMS

  Y KABASHIMA; S SHINOMOTO

  NEURAL COMPUTATION, Sep. 1992, Peer-reviewed

  
• 4 TYPES OF LEARNING-CURVES

  S AMARI; N FUJITA; S SHINOMOTO

  NEURAL COMPUTATION, Jul. 1992, Peer-reviewed

  
• ASYMPTOTIC DEPENDENCE OF THE RESIDUAL ENERGY ON ANNEALING TIME

  Y KABASHIMA; S SHINOMOTO

  JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, Dec. 1991, Peer-reviewed

  
• FINITE-TIME SCALING OF ENERGY IN SIMULATED ANNEALING

  S SHINOMOTO; Y KABASHIMA

  JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, Feb. 1991, Peer-reviewed

  
• Information classification scheme of feedforward networks organised under unsupervised learning

  Shigeru Shinomoto

  NETWORK-COMPUTATION IN NEURAL SYSTEMS, 1990, Peer-reviewed

  
• MEMORY MAINTENANCE IN NEURAL NETWORKS

  S SHINOMOTO

  JOURNAL OF PHYSICS A-MATHEMATICAL AND GENERAL, Dec. 1987, Peer-reviewed

  
• LOCAL AND GLOBAL SELF-ENTRAINMENTS IN OSCILLATOR LATTICES

  H SAKAGUCHI; S SHINOMOTO; Y KURAMOTO

  PROGRESS OF THEORETICAL PHYSICS, May 1987, Peer-reviewed

  
• A COGNITIVE AND ASSOCIATIVE MEMORY

  S SHINOMOTO

  BIOLOGICAL CYBERNETICS, 1987, Peer-reviewed

  
• FREE VOLUME OF THE HOLE THEORY OF FLUIDS.

  Shinomoto, Shigeru; Morita, Tohru

  Technology Reports of the Tohoku University, 1984, Peer-reviewed

  
• APPLICATION OF THE CLUSTER VARIATION METHOD TO THE HOLE THEORY OF FLUIDS

  S SHINOMOTO; T MORITA

  PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 1984, Peer-reviewed

  
• EQUILIBRIUM-THEORY FOR THE HARD-CORE SYSTEMS

  S SHINOMOTO

  JOURNAL OF STATISTICAL PHYSICS, 1983, Peer-reviewed

  
• DIRECT PROOF OF THE H-THEOREM FOR THE (2-BODY) BOGOLUBOV-GREEN-COHEN EQUATION

  S SHINOMOTO

  PHYSICA A, 1982, Peer-reviewed

  
• ELEMENTARY THEORY OF THE EQUATION OF STATE AND THE PAIR DISTRIBUTION FUNCTION FOR THE HARD-SPHERE SYSTEM

  S SHINOMOTO

  PHYSICS LETTERS A, 1982, Peer-reviewed

  

Misc.

• ニューラルネットワーク, 別冊 数理科学

  篠本滋

  脳科学の前線 数理モデルを中心として, 1997

  
• 事象発生ゆらぎの要因推定

  藤田和樹; 小山慎介; 篠本滋

  日本物理学会講演概要集(CD-ROM), 23 Mar. 2018

  
• 一般化線形モデル形式の自己励起過程におけるStationary‐Nonstationary転移

  藤田和樹; 篠本滋

  日本物理学会講演概要集(CD-ROM), 21 Mar. 2017

  
• ネットワーク上のイベント発生バーストの制御

  翁長朝功; 篠本滋

  日本物理学会講演概要集(CD-ROM), 22 Mar. 2016

  
• スペクトルを分類する学習装置の開発

  古川雅博; 篠本滋

  日本物理学会講演概要集(CD-ROM), 22 Mar. 2016

  
• 非対称相互作用のネットワークにおけるバースト現象

  翁長朝功; 篠本滋

  日本物理学会講演概要集(CD-ROM), 24 Mar. 2015

  
• DCMによる脳領野間の有効結合の推定

  新谷俊了; 太田真理; 酒井邦嘉; 篠本滋

  日本物理学会講演概要集(CD-ROM), 24 Mar. 2015

  
• リカレントニューラルネットワークの微視的不安定性

  山中譲; 甘利俊一; 篠本滋

  日本物理学会講演概要集(CD-ROM), 24 Mar. 2015

  
• Self‐exciting Point Processにおける不安定性について

  翁長朝功; 篠本滋

  日本物理学会講演概要集, 05 Mar. 2014

  
• 神経回路シミュレーションデータを用いた結合推定

  栗田修平; 小林亮太; 北野勝則; 篠本滋

  日本物理学会講演概要集, 05 Mar. 2014

  
• 神経結合とスパイク発火の集団的活動変動との関連性

  新谷俊了; 翁長朝功; 篠本滋

  日本物理学会講演概要集, 05 Mar. 2014

  
• 27pAJ-11 Bursting transition in a linear self-exciting point process

  Onaga T.; Shinomoto S.

  Meeting abstracts of the Physical Society of Japan, 05 Mar. 2014

  
• 29pXR-4 Estimating non-stationary input signals from a single neuronal spike train

  Kim Hideaki; Shinomoto Shigeru

  Meeting abstracts of the Physical Society of Japan, 26 Mar. 2013

  
• Estimating inputs and an internal neuronal parameter from a single spike train.

  Shigeru Shinomoto; Hideaki Kim

  Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2013, Peer-reviewed

  
• 25aAG-1 Optimizing Time Histograms for non-Poissonian Spike Trains

  Omi Takahiro; Shinomoto Shigeru

  Meeting abstracts of the Physical Society of Japan, 05 Mar. 2012

  
• 25aAG-2 Inference of time-dependent input signals from a single voltage trace of a neuron

  Kobayashi R.; Lansky Petr; Shinomoto S.

  Meeting abstracts of the Physical Society of Japan, 05 Mar. 2012

  
• Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron

  R. Kobayashi; Y. Tsubo; P. Lansky; S. Shinomoto

  Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011, 2011, Peer-reviewed

  
• 地震の発生間隔の相関について 2

  近江崇宏; 篠本滋

  日本物理学会講演概要集, 01 Mar. 2010

  
• スパイクニューロンモデルの分類

  山内智; 篠本滋

  日本物理学会講演概要集, 01 Mar. 2010

  
• 単一神経細胞による変化点検出問題

  KIM Hideaki; RICHMOND Barry J; 篠本滋

  日本物理学会講演概要集, 01 Mar. 2010

  
• Dynamical behavior of multi-timescale adaptive threshold model

  Satoshi Yamauchi; Hideaki Kim; Shigeru Shinomoto

  NEUROSCIENCE RESEARCH, 2010

  
• Characterizing neuronal firing with the rate and the irregularity

  Shigeru Shinomoto; Hideaki Shimazaki; Takeaki Shimokawa

  NEUROSCIENCE RESEARCH, 2010

  
• A Method to Estimate Synaptic Input from a Voltage Trace

  Ryota Kobayashi; Shigeru Shinomoto; Petr Lansky

  NEUROSCIENCE RESEARCH, 2010

  
• 空間認知および時間認知の神経回路モデルの構築

  篠本滋

  脳の高次機能システム 平成16-21年度 No.16068101, 2010

  
• 30pVC-9 変動閾値モデルによるスパイク時系列の予測(30pVC 生物物理,領域12(ソフトマター物理,化学物理,生物物理))

  小林亮太; 坪泰宏; 篠本滋

  日本物理学会講演概要集, 03 Mar. 2009

  
• Detecting a change point by a single neuron

  Hideaki Kim; Barry J. Richmond; Shigeru Shinomoto

  NEUROSCIENCE RESEARCH, 2009

  
• A simple model for predicting spike times of a variety of neurons

  Ryota Kobayashi; Yasuhiro Tsubo; Shigeru Shinomoto

  NEUROSCIENCE RESEARCH, 2009

  
• Bayesian estimation of nonstationary parameters of a single spike train

  Takeaki Shimokawa; Shigeru Shinomoto

  NEUROSCIENCE RESEARCH, 2009

  
• ニューラルネットワーク研究との遭遇

  篠本滋

  日本神経回路学会誌, 2009

  
• ベイズ統計を用いたスパイク発火特性の抽出 : 時間変化する発火率と不規則性の同時推定(非線形科学と統計科学の対話,研究会報告)

  下川丈明; 篠本滋

  物性研究, 20 Nov. 2008

  Access this misc :

  
• Dynamic threshold model may produce a variety of neuronal firing types

  Ryota Kobayashi; Yasuhiro Tsubo; Tomoki Fukai; Shigeru Shinomoto

  NEUROSCIENCE RESEARCH, 2008

  
• 23pTL-3 伝達遅延に分布のある神経回路の集団振動(ニューラルネットワーク,領域11,統計力学,物性基礎論,応用数学,力学,流体物理)

  近江崇宏; 篠本滋

  日本物理学会講演概要集, 21 Aug. 2007

  
• A Bayesian Analysis of the Time Dependent Irregularity of Neuronal Firing

  SHIMOKAWA Takeaki; SHINOMOTO Shigeru

  IEICE technical report, 17 Jul. 2007

  
• 村と世界をつなぐ

  篠本滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Dec. 2006

  
• 26pXD-14 相空間の情報を利用したスパイク予測法の提案(26pXD ニューラルネットワーク(神経系のモデルを含む),領域11(統計力学,物性基礎論,応用数学,力学,流体物理))

  小林亮太; 篠本滋

  日本物理学会講演概要集, 18 Aug. 2006

  
• 座談会 神経科学と理論研究の交流から生まれたもの

  外山敬介; 川人光男; 櫻井芳雄; 金子武嗣; 篠本滋

  日本神経回路学会誌 = The Brain & neural networks, 05 Jun. 2006

  
• Search for Optimal Measure to Discriminate Two Spike Trains Having Different Randomness

  MIURA Keiji; OKADA Masato; SHINOMOTO Shigeru

  The IEICE transactions on information and systems Pt. 2, 01 Sep. 2005

  
• What is Resulted from Neuro-Computing Research?

  AMARI Shun-ichi; NIKI Kazuhisa; SHINOMOTO Shigeru; OKADA Masato; ASOH Hideki

  The Journal of the Institute of Electronics, Information and Communication Engineers, 01 Apr. 2005

  
• What is Resulted from Neuro-Computing Research?

  AMARI Shun-ichi; NIKI Kazuhisa; SHINOMOTO Shigeru; OKADA Masato; ASOH Hideki

  The Journal of the Institute of Electronics, Information and Communication Engineers, 01 Apr. 2005

  
• 24pYB-7 神経細胞の電気的入出力関係の同定(ニューラルネットワーク,領域11(統計力学,物性基礎論,応用数学,力学,流体物理))

  小林亮太; 篠本滋

  日本物理学会講演概要集, 04 Mar. 2005

  
• 13pTC-9 点過程統計モデルによる神経スパイク時系列の解析(ニューラルネットワーク : 神経系のモデルを含む, 領域 11)

  小山慎介; 篠本滋

  日本物理学会講演概要集, 25 Aug. 2004

  
• 13pTC-7 ランダムなスパイク列と規則的なスパイク列を判別するための最適な指標の探索(ニューラルネットワーク : 神経系のモデルを含む, 領域 11)

  三浦佳二; 岡田真人; 篠本滋

  日本物理学会講演概要集, 25 Aug. 2004

  
• 13pTC-6 大域結合した Resonate-and-Fire モデル(ニューラルネットワーク : 神経系のモデルを含む, 領域 11)

  三浦佳二; 岡田真人; 篠本滋

  日本物理学会講演概要集, 25 Aug. 2004

  
• Search for optimal measure to discriminate random and regular spike trains

  MIURA Keiji; OKADA Masato; SHINOMOTO Shigeru

  IEICE technical report. Neurocomputing, 20 Jul. 2004

  
• A parameter estimation method for hidden Markov models with point process observations

  KOYAMA Shinsuke; SHINOMOTO Shigeru

  IEICE technical report. Neurocomputing, 18 Jun. 2004

  
• 21aTQ-6 スパイク時系列に基づく発火確率の時間変動の推定

  小山慎介; 篠本滋

  日本物理学会講演概要集, 15 Aug. 2003

  
• 28pWJ-7 スパイク発生パターンによる神経細胞の分類

  三浦佳二; 篠本滋

  日本物理学会講演概要集, 06 Mar. 2003

  
• 18aRD-10 Interval statistics of time-dependent Poisson processes and spiking data of biological neurons

  Tsubo Yasuhiro; Shinomoto Shigeru

  Meeting abstracts of the Physical Society of Japan, 03 Sep. 2001

  
• サービスとしての科学 (特集 あなたが考える科学とは)

  篠本滋

  科学, Apr. 2001

  
• サービスとしての科学

  篠本 滋

  岩波・科学,71/4-5,533-536, 2001

  
• 脳科学大辞典(編集幹事)

  篠本 滋

  朝倉書店,, 2000

  
• 脳のデザイン(第44回 物性若手夏の学校(1999年度),講義ノート)

  篠本滋

  物性研究, 20 Nov. 1999

  Access this misc :

  
• 26pU-17 Random Spike Generator Modelの自己整合性とスパイクデータとの整合性

  酒井裕; 篠本滋

  日本物理学会講演概要集, 03 Sep. 1999

  
• 皮質ニューロンの入出力機構(基研研究会「ニューラルネットワーク〜これからの統計力学的アプローチ〜」,研究会報告)

  酒井裕; 篠本滋

  物性研究, 20 Jun. 1998

  
• Spiking mechanisms of cortical neurons

  SAKAI Yutaka; SHINOMOTO Shigeru

  日本神経回路学会全国大会講演論文集 = Annual conference of Japanese Neural Network Society, 05 Nov. 1997

  
• 6p-YD-8 スパイク時系列データにもとづいたニューロンモデルの検討

  酒井裕; 篠本滋

  日本物理学会講演概要集, 16 Sep. 1997

  
• 脳の世紀への手紙-第3回-記憶の情報表現

  篠本 滋; 奥野 浩行

  科学, Sep. 1997

  
• Statistical Mechanical Theory of Learning

  SHINOMOTO Shigeru

  システム/制御/情報 : システム制御情報学会誌 = Systems, control and information, 15 Apr. 1996

  
• New Applications of Stochastic Approach. Statistical Mechanical Theory of Learning.

  篠本滋

  システム/制御/情報, Apr. 1996

  
• ニュ-ラルネットワ-ク (現代の応用数理<特集>)

  篠本滋

  数理科学, Apr. 1994

  
• 学習の理論--誤りを減らすためにはどの程度学んだらよいか

  篠本滋

  科学, May 1993

  
• 学習の理論

  篠本 滋

  岩波・科学,63,294-302, 1993

  
• Learning Theory

  篠本 滋

  IWANAMI・KAGAKU,63,294-302, 1993

  
• Incremental Learning with and without Queries in Binary Choice Problems

  篠本 滋

  Proceedings of 1993 IJCNN,, 1993, Peer-reviewed

  
• Acceleration of Learning in Binary Choice Problems

  篠本 滋

  Proceedings of 1993 ACM conference on COLT,, 1993, Peer-reviewed

  
• 25p-ZA-6 Prediction error for unrealizable rules

  Kabashima Yoshiyuki; Shinomoto Shigeru

  秋の分科会講演予稿集, 14 Sep. 1992

  
• 27p-PS-134 Learning Curves for Space Separation Problems

  Takamura Koji; Shinomoto Shigeru

  年会講演予稿集, 12 Mar. 1992

  
• 27p-PS-133 Prediction Error of Unrealizable Rules

  Kabashima Yoshiyuki; Shinomoto Shigeru

  年会講演予稿集, 12 Mar. 1992

  
• タイトル無し

  篠本滋

  情報の統計力学, 1992

  
• 学習の数理 (ニュ-ラルネットの数理--脳の解明に向けて<特集>)

  篠本滋

  数理科学, Aug. 1991

  
• 緩和アルゴリズムの応用 (ニュ-ラルネット<特集>)

  篠本滋

  計測と制御, Apr. 1991

  
• 物理の"複雑なシステム"と脳 (脳の高次機能<特集>)

  篠本滋

  科学, Apr. 1991

  
• 27p-H-2 Time scaling of energy in simulated annealing

  Kabashima Yoshiyuki

  春の分科会講演予稿集, 11 Mar. 1991

  
• 27p-H-1 Unsupervised Learning and inforation classification scheme

  Shinomoto Shigeru

  春の分科会講演予稿集, 11 Mar. 1991

  
• (1) D. J. Amit, Modeling Brain Function, Combridge Univ. Press, New York, 1989, 504p., 23.5×15.5 cm, (2) D. E. Rumelhart, J. L. McClleland and PDP Research Group, Parallel Distributed Processing, Vols. 1 and 2, MIT Press, Massachusetts, 1986, 2冊 (547p, 61

  篠本滋

  日本物理學會誌, 05 Sep. 1990

  
• 神経回路網モデル(基研短期研究会「動的脳観」,研究会報告)

  篠本滋

  物性研究, 20 Nov. 1989

  Access this misc :

  
• 大脳の数理モデルを目指して (大脳/神経科学からのアプロ-チ<特集>)

  篠本滋

  生体の科学, Apr. 1989

  
• 脳の情報処理と物理

  篠本滋

  科学, Mar. 1989

  
• 2. 神経回路網とその自己組織化(基研短期研究会「スピングラスを中心とした新しい秩序相」報告,研究会報告)

  篠本滋

  物性研究, 20 Jan. 1988

  Access this misc :

  
• 26p-W-6 回路の自己組織と保持

  篠本滋

  秋の分科会講演予稿集, 16 Sep. 1987

  
• 神経網モデルのダイナミクス--物理学との接点 (ニュ-ラル・ネット--脳科学の前線<特集>)

  篠本滋

  数理科学, Jul. 1987

  
• 神経回路網とスピングラス(スピングラス(リエントラント転移を中心として),研究会報告)

  篠本滋

  物性研究, 20 Apr. 1987

  Access this misc :

  
• 物性基礎論II : Beyond Simple Chaos(サブゼミ,1986年度物性若手夏の学校の報告)

  佐野雅己; 池上高志; 武本真二; 篠本滋

  物性研究, 20 Jan. 1987

  Access this misc :

  
• 8.Oscillator Latticeの協力現象(パターン形成の運動及び統計,研究会報告)

  坂口英継; 藤本由紀; 篠本滋

  物性研究, 20 Sep. 1986

  Access this misc :

  
• 7.無限レンジ神経ネットワークの動的性質(パターン形成の運動及び統計,研究会報告)

  篠本滋

  物性研究, 20 Sep. 1986

  Access this misc :

  
• 30p-QC-5 神経グラスのオートマトンモデルI

  篠本滋

  秋の分科会講演予稿集, 12 Sep. 1986

  
• Oscillator Latticeの協力現象(非線形揺動と秩序化過程,科研費研究会報告)

  篠本滋; 坂口英継; 蔵本由紀

  物性研究, 20 Mar. 1986

  Access this misc :

  
• 3p-D-10 Oscillator Latticeの協力現象 I

  蔵本由紀; 篠本滋; 坂口英継

  秋の分科会講演予稿集, 13 Sep. 1985

  
• 2p-I-2 引きこみ現象に対するノイズの効果

  篠本滋; 蔵本由紀

  秋の分科会講演予稿集, 10 Sep. 1984

  
• 13p-L-13 三相の格子模型

  篠本滋

  秋の分科会講演予稿集, 13 Sep. 1983

  
• 27p-S-8 単純な融解の理論-剛体球,円板系のAlder転移-

  篠本滋

  年会講演予稿集, 11 Mar. 1983

  
• 31a-R-9 剛体球系の二体相関関数と状態方程式

  篠本滋

  秋の分科会講演予稿集, 15 Mar. 1982

  

Books and Other Publications

• 力学 (基幹講座物理学)

  篠本滋; 坂口英継; 益川敏英; 植松恒夫; 青山秀明, Joint work

  東京図書, 10 Oct. 2013, Not refereed

  Find Kyoto University's collection of books :

  
• Mechanics

  Shigeru Shinomoto, Joint work

  東京図書, Oct. 2013, Not refereed

  
• "Analysis of Parallel Spike Train Data" (eds. S. Gruen and S. Rotter)

  S Shinomoto

  Springer, 2010, Not refereed

  
• 脳科学のテーブル (学術選書)

  日本神経回路学会; 外山 敬介; 篠本 滋; 甘利 俊一, Joint editor

  京都大学学術出版会, 01 Apr. 2008, Not refereed

  Find Kyoto University's collection of books :

  
• 脳科学のテーブル

  外山敬介; 甘利俊一; 篠本滋

  京都大学学術出版会, 2008, Not refereed

  
• 情報処理概論―予測とシミュレーション

  篠本 滋, Single work

  岩波書店, 18 Dec. 2002, Not refereed

  Find Kyoto University's collection of books :

  
• 脳のデザイン

  篠本 滋, Single work

  岩波書店, 15 Mar. 1996, Not refereed

  Find Kyoto University's collection of books :

  
• Design principles of the brain

  篠本 滋

  Iwanami Shoten Publisher,, 1996, Not refereed

  
• 科学 1993年5号◆アルツハイマー病/宮武正◆全地球史解読に向けて/熊沢峰雄◆イオントラップと時間標準/志水忠雄

  岩波書店; 科学; 編集部; 池内了; 宮武正; 篠本滋; 河村悟

  岩波書店, 01 May 1993, Not refereed

  
• Cognition and Learning

  篠本 滋

  Maruzen,, 1993, Not refereed

  
• 認知と学習(共著)

  篠本 滋

  九善出版,, 1993, Not refereed

  
• ニューラルネットの新展開(共著)

  篠本 滋

  サイエンス社,, 1993, Not refereed

  
• 情報の統計力学 (パリティ物理学コース―クローズアップ)

  篠本 滋, Single work

  丸善, 01 Jun. 1992, Not refereed

  Find Kyoto University's collection of books :

  
• Computational Statistical Mechanics

  篠本 滋

  ,, 1992, Not refereed

  
• 情報の統計力学

  篠本 滋

  丸善出版,, 1992, Not refereed

  
• パターン形成(共著)

  篠本 滋, Joint work

  朝倉書店, 01 Dec. 1991, Not refereed

  Find Kyoto University's collection of books :

  
• Pattern Formation

  篠本 滋

  ,, 1991, Not refereed

  
• Prediction and Simulation

  篠本 滋

  Iwanami-Shoten Publishers,, Not refereed

  

Awards

• 1993

  Japanese Neural Network Society, Best paper award
• 2008

  The EPFL-Brain Mind Institute, Neuron Modeling Award
• 2010

  Japanese Neural Network Society, Best paper award

External funds: Kakenhi

• ソーシャルメディアにおける将来のトレンドを予測する時系列モデルの開発

  Grant-in-Aid for Scientific Research (C)

  Basic Section 62020:Web informatics and service informatics-related

  The University of Tokyo;National Institute of Informatics

  小林 亮太

  From 01 Apr. 2018, To 31 Mar. 2024, Granted

  ソーシャルメディア分析;Webデータ分析;時系列モデル;フェークニュース;情報拡散;社会ネットワーク分析;計算社会科学;時系列分析;トピックモデル;トレンド;ウェブマイニング;時系列解析;機械学習

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18K11560/
• Inferring network structures and activity patterns from neuronal signals

  Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

  Complex systems

  Kyoto University

  篠本 滋

  From 01 Apr. 2017, To 31 Mar. 2019, Project Closed

  神経信号相関;生体生命情報学

  https://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-17H06028/
• Constructing state-space models that fit hierarchical networks to multitude of event sequences

  Grant-in-Aid for Scientific Research (B)

  Kyoto University

  Shigeru Shinomoto

  From 01 Apr. 2014, To 31 Mar. 2020, Project Closed

  神経スパイク;状態空間法;大規模データ;統計的因果性の解析;統計学;階層ネットワーク;微視的不安定性;ビッグデータ解析;ネットワーク;時系列解析;神経データ

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-26280007/
• Prefrontal neural networks for performing executive functions

  Grant-in-Aid for Scientific Research (A)

  Kyoto University

  Shintaro Funahashi

  From 01 Apr. 2013, To 31 Mar. 2018, Project Closed

  前頭連合野;遂行機能;記憶想起;意思決定;トップダウン制御信号;自由選択;対連合課題;サル;トップダウン信号;ニホンザル;単一ニューロン活動;トップダウン制御;対連合学習;前頭葉眼窩部

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-25240021/
• 神経細胞集団を階層的組織にクラスタリングする方法の開発と応用

  Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2013, To 31 Mar. 2015, Project Closed

  イベント時系列解析;神経スパイクデータ

  https://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-25115718/
• 神経信号からメゾスコピック神経回路の働きを同定する大規模データ解析手法の確立

  Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2011, To 31 Mar. 2013, Project Closed

  状態空間解析;隠れマルコフモデル;国際研究者交流・米国;国際研究者交流・ドイツ;国際研究者交流・イタリア;国際研究者交流・チェコ;神経信号解析;状態空間法;ベイズ推定

  https://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-23115510/
• Development of elemental models for the brain simulator

  Grant-in-Aid for Scientific Research (B)

  Kyoto University

  Shigeru SHINOMOTO

  From 01 Apr. 2008, To 31 Mar. 2013, Project Closed

  神経スパイク;スパイク予測;統計推定;国際研究者交流：チェコ;国際研究者交流：米国;国際研究者交流：ドイツ;国際研究者交流：イタリア;脳シミュレータ;神経スパイクモデル

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20300083/
• 空間認知および時間認知の神経回路モデルの構築

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2008, To 31 Mar. 2010, Project Closed

  空間コーディング;時間コーディング;神経スパイク統計;脳シミュレータ;神経スパイクモデル;スパイク予測

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20020012/
• 時間認識の神経メカニズムの実証的理論研究

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2006, To 31 Mar. 2008, Project Closed

  スパイク統計;待ち時間課程;局所変動係数;待ち時間課題

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18020015/
• 時間予測判断を行うための神経ネットークモデルの構築

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2005, To 31 Mar. 2006, Project Closed

  時間コード;神経スパイクコード

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17022022/
• Neuronal dynamics relating to the time coding in the brain

  Grant-in-Aid for Scientific Research (B)

  Kyoto University

  Shigeru SHINOMOTO

  From 01 Apr. 2004, To 31 Mar. 2008, Project Closed

  スパイク統計;局所変動係数;待ち時間課題;時間コード;神経スパイクコード, spiking statistics;local variation;waiting period task

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16300068/
• 新統計測度による神経信号時系列パターンの分類と脳内信号の流れの解明

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2004, To 31 Mar. 2005, Project Closed

  神経スパイク時系列;局所変動係数

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16015263/
• 神経スパイク特性に基づいた細胞分類法の確立

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2003, To 31 Mar. 2004, Project Closed

  神経スパイク信号;局所変動係数;細胞分類

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15016059/
• 神経情報コードの解明に向けた神経細胞モデル比較実験

  Grant-in-Aid for Scientific Research on Priority Areas (C)

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2001, To 31 Mar. 2002, Project Closed

  神経スパイク信号;時間依存ポアソン過程;細胞内通電実験;前頭連合野

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-13210073/
• Spike coding of the memory.

  Grant-in-Aid for Scientific Research (C)

  Kyoto University

  Shigeru SHINOMOTO

  From 01 Apr. 2000, To 31 Mar. 2004, Project Closed

  神経スパイク信号;細胞内通電;スパイク生成モデル;局所変動係数;時間依存ポアソンモデル, spike signal;current injection;neurospiking model;local variation

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12680382/
• 神経情報コードの解明に向けた神経細胞モデル比較実験

  Grant-in-Aid for Scientific Research on Priority Areas (C)

  Biological Sciences

  Kyoto University

  篠本 滋

  From 01 Apr. 2000, To 31 Mar. 2001, Project Closed

  神経スパイク信号;細胞内通電実験;前頭連合野;MT野;MST野

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12210086/
• Formation and storage of memory

  Grant-in-Aid for Scientific Research on Priority Areas

  The University of Tokyo

  Kazuyuki AIHARA

  From 01 Apr. 1996, To 31 Mar. 2000, Project Closed

  脳磁図;三次元図形の短期記憶;時空間学習則;エピソード記憶;海馬;コインシデンスディテクタ系;動的セル・アセンプリー仮説;神経スパイク統計;相対的位置識別;LTP;LTD;意味記憶;過渡的シンクロニー;脳磁図(MEG);テクスチャーの勾配;人名の想起;fMRI;ユニタリー事象;神経スパイク時系列;コインシデンスディテクタ;長期増強(LTP);光学的計測;平面方位識別ニューロン;PET;コインシデンス・ディテクタ;動的細胞集成体;頭頂葉連合野;前頭前野;光計測;カオスニューラルネット;PET(positron emission tomography);コインシデンス・ディテクター;動的細胞集成体仮説, High spatial resolution MEG;Short-term memory of 3D shape;Spatio temporal learning rule;Episodic memory;Hippocampus;Coincidence detector system;Dynamical cell assembly hypothesis;Spike statistics

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-08279103/
• Theory of Dynamical Contraction Applicable to Nonlinear Dissipative Systems

  Grant-in-Aid for General Scientific Research (C)

  Kyoto University

  KURAMOTO YOSHIKI

  From 01 Apr. 1987, To 31 Mar. 1989, Project Closed

  大自由度散逸力学系;動的縮約法;引き込み現象;フェイズダイナミクス;回転子模型;神経回路網;秩序パラメター;振動子集団;位相乱流;実効発展方程式;乱流ゆらぎ;くり込まれた輸送係数, dissipative dynamical systems with many degrees of freedom;dynamical contraction;entrainment;phasedynamics;rotator model

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-62540280/
• Standardization of behavior change based on brain dynamics

  Grant-in-Aid for Transformative Research Areas (A)

  Transformative Research Areas, Section (III)

  National Institutes of Natural Sciences;Kyoto University

  中江 健

  From 16 Jun. 2022, To 31 Mar. 2027, Granted

  行動変容;脳活動;3Dモデル;表情解析;神経データ解析;動画データ解析

  https://kaken.nii.ac.jp/grant/KAKENHI-PLANNED-22H05163/

External funds: others

• Computational Learning Theory
• Statistical analysis of neuronal spike trains
• 生物・社会・経済ネットワークに共通するイベント時系列データの数理モデリング （平成28年度分）

  二国間交流事業共同研究・セミナー

  From 01 Apr. 2016, To 31 Mar. 2017

  篠本　滋
• 生物・社会・経済ネットワークに共通するイベント時系列データの数理モデリング （平成29年度分）

  二国間交流事業共同研究・セミナー

  From 01 Apr. 2017, To 31 Mar. 2018

  篠本 滋

Education

Teaching subject(s)

• From 01 Apr. 2022, To 31 Mar. 2023

  Computational neuroscience

  3544, Year-long, Graduate School of Informatics, 1
• From Apr. 2011, To Mar. 2012

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2011, To Mar. 2012

  物理科学課題研究Ｑ９

  Year-long, 理学部
• From Apr. 2012, To Mar. 2013

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2012, To Mar. 2013

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2012, To Mar. 2013

  Non-Equilibrium Statistical

  Spring, 理学部
• From Apr. 2012, To Mar. 2013

  Nonlinear Science

  Fall, 理学部
• From Apr. 2013, To Mar. 2014

  Advanced Statistical Mechanics

  Spring, 理学部
• From Apr. 2013, To Mar. 2014

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2013, To Mar. 2014

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2014, To Mar. 2015

  Advanced Statistical Mechanics

  Spring, 理学部
• From Apr. 2014, To Mar. 2015

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2014, To Mar. 2015

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2014, To Mar. 2015

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2014, To Mar. 2015

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2014, To Mar. 2015

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科
• From Apr. 2014, To Mar. 2015

  Advanced Theoretical Physics II-1

  Fall, 理学研究科
• From Apr. 2014, To Mar. 2015

  Advanced Theoretical Physics II-2

  Fall, 理学研究科
• From Apr. 2014, To Mar. 2015

  Advanced Statistical Dynamics

  Spring, 理学研究科
• From Apr. 2014, To Mar. 2015

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2015, To Mar. 2016

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2015, To Mar. 2016

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2015, To Mar. 2016

  Advanced Theoretical Physics II-1

  Spring, 理学研究科
• From Apr. 2015, To Mar. 2016

  Advanced Theoretical Physics II-2

  Spring, 理学研究科
• From Apr. 2015, To Mar. 2016

  Advanced Statistical Mechanics

  Spring, 理学部
• From Apr. 2015, To Mar. 2016

  Advanced Statistical Dynamics

  Spring, 理学研究科
• From Apr. 2015, To Mar. 2016

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2015, To Mar. 2016

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2015, To Mar. 2016

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2015, To Mar. 2016

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科
• From Apr. 2016, To Mar. 2017

  ILAS Seminar

  Spring, 全学共通科目
• From Apr. 2016, To Mar. 2017

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2016, To Mar. 2017

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2016, To Mar. 2017

  Advanced Theoretical Physics II-1

  Fall, 理学研究科
• From Apr. 2016, To Mar. 2017

  Advanced Theoretical Physics II-2

  Fall, 理学研究科
• From Apr. 2016, To Mar. 2017

  Advanced Statistical Mechanics

  Fall, 理学部
• From Apr. 2016, To Mar. 2017

  Advanced Statistical Dynamics

  Fall, 理学研究科
• From Apr. 2016, To Mar. 2017

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2016, To Mar. 2017

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2016, To Mar. 2017

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2016, To Mar. 2017

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科
• From Apr. 2017, To Mar. 2018

  ILAS Seminar

  Spring, 全学共通科目
• From Apr. 2017, To Mar. 2018

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2017, To Mar. 2018

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2017, To Mar. 2018

  Advanced Theoretical Physics II-1

  Fall, 理学研究科
• From Apr. 2017, To Mar. 2018

  Advanced Theoretical Physics II-2

  Fall, 理学研究科
• From Apr. 2017, To Mar. 2018

  Advanced Statistical Mechanics

  Fall, 理学部
• From Apr. 2017, To Mar. 2018

  Advanced Statistical Dynamics

  Fall, 理学研究科
• From Apr. 2017, To Mar. 2018

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2017, To Mar. 2018

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2017, To Mar. 2018

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2017, To Mar. 2018

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科
• From Apr. 2018, To Mar. 2019

  ILAS Seminar

  Spring, 全学共通科目
• From Apr. 2018, To Mar. 2019

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2018, To Mar. 2019

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2018, To Mar. 2019

  Advanced Theoretical Physics II-1

  Fall, 理学研究科
• From Apr. 2018, To Mar. 2019

  Advanced Theoretical Physics II-2

  Fall, 理学研究科
• From Apr. 2018, To Mar. 2019

  Advanced Statistical Mechanics

  Fall, 理学部
• From Apr. 2018, To Mar. 2019

  Advanced Statistical Dynamics

  Fall, 理学研究科
• From Apr. 2018, To Mar. 2019

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2018, To Mar. 2019

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2018, To Mar. 2019

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2018, To Mar. 2019

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科
• From Apr. 2019, To Mar. 2020

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2019, To Mar. 2020

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2019, To Mar. 2020

  Advanced Theoretical Physics II-1

  Spring, 理学研究科
• From Apr. 2019, To Mar. 2020

  Advanced Theoretical Physics II-2

  Spring, 理学研究科
• From Apr. 2019, To Mar. 2020

  Advanced Statistical Mechanics

  Fall, 理学部
• From Apr. 2019, To Mar. 2020

  Introduction to Statistical Dynamics

  Fall, 理学研究科
• From Apr. 2019, To Mar. 2020

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2019, To Mar. 2020

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2019, To Mar. 2020

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2019, To Mar. 2020

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科
• From Apr. 2020, To Mar. 2021

  Fundamental Physics A

  Spring, 全学共通科目
• From Apr. 2020, To Mar. 2021

  Special study course II(Physical Science) Q9

  Year-long, 理学部
• From Apr. 2020, To Mar. 2021

  Advanced Theoretical Physics II-1

  Fall, 理学研究科
• From Apr. 2020, To Mar. 2021

  Advanced Theoretical Physics II-2

  Fall, 理学研究科
• From Apr. 2020, To Mar. 2021

  Advanced Statistical Mechanics

  Fall, 理学部
• From Apr. 2020, To Mar. 2021

  Introduction to Statistical Dynamics

  Fall, 理学研究科
• From Apr. 2020, To Mar. 2021

  Seminar on Nonlinear Dynamics A

  Spring, 理学研究科
• From Apr. 2020, To Mar. 2021

  Seminar on Nonlinear Dynamics B

  Fall, 理学研究科
• From Apr. 2020, To Mar. 2021

  Seminar on Nonlinear Dynamics C

  Spring, 理学研究科
• From Apr. 2020, To Mar. 2021

  Seminar on Nonlinear Dynamics D

  Fall, 理学研究科

Administration

Faculty management (title, position)

• From 01 Apr. 2014, To 31 Mar. 2015

  理学部・教育委員会委員
• From 01 Apr. 2014, To 31 Mar. 2015

  理学部・教育委員会　教務委員会委員
• From 01 Apr. 2015, To 31 Mar. 2016

  理学研究科図書・出版委員会委員
• From 01 Apr. 2016, To 31 Mar. 2017

  理学研究科図書・出版委員会委員
• From 01 Apr. 2016, To 31 Mar. 2017

  理学研究科セミナーハウス管理運営委員会委員
• From 01 Apr. 2017, To 31 Mar. 2018

  理学研究科図書・出版委員会委員
• From 01 Apr. 2018, To 31 Mar. 2019

  理学研究科図書・出版委員会委員
• From 01 Apr. 2019, To 31 Mar. 2020

  理学研究科SACRA広報・社会連携部門委員会　社会連携小委員会委員
• From 01 Apr. 2019, To 31 Mar. 2020

  理学研究科SACRA広報・社会連携部門委員会　基金運営小委員会委員
• From 01 Apr. 2020, To 31 Mar. 2021

  理学研究科SACRA広報・社会連携部門委員会　社会連携小委員会委員