HIDEAKI SHIMAZAKI, Ph.D.

Associate Professor, Graduate School of Informatics, Kyoto University

Visiting Associate Professor, Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN), Hokkaido University, Japan

Curriculum Vitae (a full CV pdf)

Program-specific Associate Professor at Kyoto University (2017-2020)
Senior Scientist at Honda Research Institute Japan (2016-2020)

Researcher at RIKEN BSI (2012-2016)
Visiting researcher at RIKEN BSI / MIT (2008-2011)
JSPS Research Fellow (2006/4-2008/3, 2008/4-2011/3)

Ph.D. (Physics), 2007 Dept. of Physics, Kyoto University, Japan
M.A. (Neuroscience), 2004 Dept. of Neuroscience, Johns Hopkins University
B.E., 2000 Dept. of Applied Physics and Physico-Informatics, Keio University, Japan

[Google Scholor] [Researchmap] [ResearchGate] [ORCID]

Email: h.shimazakii.kyoto-u.ac.jp

Histogram Optimization | Kernel Optimization | State-space Ising model
Lecture notes | Group Photos | CHAIN web site

Upcoming seminars and workshops

■ 2022 Sep 13-15 International Workshop on Active Inference (IWAI 2023)

■ 2022 July 27,28 Salon du Brain Internaltional

■ CHAIN seminar series at Hokkaido University

■ Salon du Brain at Kyoto University

■ Mathematical Modeling Study Group

Past seminars and symposiums

■ 2021 Dec 13, 14 The free energy principle of the brain: experiments and verification

■ 2017-2020 Collaborative Intelligence Research Seminar (Completed)

■ 2019Aug 31-Sep 1 Tutorial workshop of the free energy principle of the brain by Christopher L. Buckley and Alexander Tschantz

■ 2019 Sep 2 Workshop on Advances in cognitive neuroscience: From the Theory of the Brain to the Body and the World (English and Japanese)

■ 2017/08/25 Japanese Neural Network Society Workshop 2017 From the Theory of the Brain to the Body and the World: Rechallenge for Action and Cognition (In Japanese) link

■ 2017/11/14 Mathematical Modeling Study Group Fluctuations of event occurrences in a variety of networks.

Link to lecture materials

CHAIN starts a new interdisciplinary graduate program in July 2020. 

Upcoming Lectures

2022/4 Introduction to the theories of brains and machines (In Japanese)

Past Lectures

2021/6/22 CHAIN Lecture Series: Introduction to Neuroscience: Measurement and methodology

2020/7 CHAIN Lecture Series: An invitation to the theory of the brain 1: A theoretical approach to cognition (In Japanese)

2020/7 CHAIN Lecture Series: An Invitation to the Theory of the Brain 2: Circuits, Computation, and Information (In Japanese)

2020/9-2021/2 Introduction to the theories of brains and machines syllabus

2019/12/19, 2020/1/9 Hierarchical Models in Brain Informatics course (Prof. Kamitani), The Faculty of Integrated Human Studies, Kyoto Unviersity

2020/2/18 Statistics for Neuroscience RIKEN Brain Science Training Program, link to a course website

2019/6/5, 6/19 Learning hierarchical models and computational theory of the brain at Graduate school of Informatics, Kyoto University, Computational cognitive neuroscience:

2019/1/29 Statistics for Neuroscience at RIKEN Brain Science Training Program, link to a course website

2018/6/6, 7/17 Learning hierarchical models and computational theory of the brain in Computational cognitive neuroscience course, the Graduate school of Informatics, Kyoto University

2017/11/27 Statistics for Neuroscience at RIKEN Brain Science Training Program:link to a course website

2017/10/26，12/21，2019/1/11 Introduction to Principal Component Analysis, Introduction to the Mathematics of Boltzmann Machines, Hierarchical Models, Brain Informatics course (Prof Kamitani), The Faculty of Integrated Human Studies, Kyoto Unviersity (In Japanese)

2017/6/21 Computational cognitive neuroscience, Graduate school of Informatics, Kyoto University (In Japanese)

2017/3/4, 5 2-days tutorial workshop on the point process modeling at Society for young researchers in Neuroscience .

2016/12-3 Statistical Physics at International Christian University　Dai Hirashima, Hideaki Shimazaki

2016/12 Statistics for Neuroscience I and II at RIKEN Brain Science Training Program, Hideaki Shimazaki, Shigeyoshi Fujisawa (In English) link to a course website

2016/2/10 An introduction to the statistical analysis of point process networks An extension lecture at the Institute of Statistics and Mathematics with Prof. Shinsuke Koyama. (In Japanese) link

2015/12-3 Statistical Physics at International Christian University, Dai Hirashima, Hideaki Shimazaki (In Japanese)

2015 Statistics for Neuroscience I and II at RIKEN Brain Science Training Program, Hideaki Shimazaki, Shigeyoshi Fujisawa (In English) link to a course website

2014/12-3 Statistical Physics at International Christian University　Dai Hirashima, Hideaki Shimazaki

2014/12/15, 22 Statistics for Neuroscience I and II at RIKEN Brain Science Training Program, Hideaki Shimazaki, Shigeyoshi Fujisawa (In English) link to a course website

2014/5/10 "Introduction to Perception/Cognition and Neural Coding Research", "Deciphering neural code" at Meiji University (In Japanese)

2013 Mar 3-12 Introduction to statistical models of neural spike train data Two weeks lecture courses at the Institute for Research in Fundamental Sciences. (In English)

Toward mathmatical understanding of the brain: circuit, information, and priciples

Humans and animals recognize the world and act on it for their survival. To understand how nervous systems realize this remarkable capacity, we need different levels of mathematical descriptions. These are (i) circuitries and mechanisms, (ii) statistics of neuronal activity and representation, and (iii) principles of computation and adaptation. Importantly, we need to understand how these different levels of descriptions are related. While the central approach of our lab is to analyze spiking data recorded from neurons using methods of machine learning and statistical physics, we broadly explore mathematical approaches to Neuroscience, from an attempt to theoretically define the adaptive principles of the brains to the realization of them by spiking networks.

Thermodynamics and the bayesian brain: The principles of adaptation and computation

• Hideaki Shimazaki.The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamics. (2019) arXiv:1902.11233 [pdf]
• The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain. (2020) arXiv:2006.13158
• Shimazaki H. Neurons as an Information-theoretic Engine. arXiv:1512.07855 (2015) [pdf, link]
• Shimazaki H. Neural Engine Hypothesis. In: Chen Z., Sarma S. (eds) Dynamic Neuroscience. (2018) Springer, Cham link

Statistical structure of neural activity and information representation

• Seyedamin Moosavi, Magalie Tatischeff, Bingyue Zhu, Hideaki Shimazaki. Effects of structured neural correlations in population coding: beneficial or detrimental? Computational and Systems Neuroscience (Cosyne) 2020. Feb 27 - Mar 1. Dember, USA POSTER
• Shimazaki H, Sadeghi K, Ishikawa T, Ikegaya Y, Toyoizumi T, Simultaneous silence organizes structured higher-order interactions in neural populations. Scientific Reports (2015) 5, 9821 . [link]

Neural mechanisms and statistcs of their activity

• Safura Rashid Shomali, Majid Nili Ahmadabadi, Seyyed Nader Rasuli, Hideaki Shimazaki. Uncovering network architecture using an exact statistical input-output relation of a neuron model. bioRxiv. (2018) doi: https://doi.org/10.1101/479956
• Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, Seyyed Nader Rasuli. How does transient signaling input affect the spike timing of postsynaptic neuron near the threshold regime: an analytical study. Journal of Computational Neuroscience (2018) 44(2), 147-171[link, pdf]

Understanding the brain as a generatuve model of the world

• Shashwat Shukla, Hideaki Shimazaki, Udayan Ganguly. Structured mean-field variational inference and learning in winner-take-all spiking neural networks. (2019)
• MaBouDi H, Subramani K, Soltanian-Zadeh H, Amari S, Shimazaki H. Learning complex representations from spatial phase statistics of natural scenes. bioRxiv (2017) doi: https://doi.org/10.1101/112813
• MaBouDi H, Shimazaki H, Amari S, Soltanian-Zadeh H, Representation of higher-order statistical structures in natural scenes via spatial phase distributions. Vision Research (2016) 120, 61–73 [pdf, link]

Development of statistical analysis methods for neural spiking dynamics

Multiple Neurons: Dynamics of neural interactions and behavior

Classical neurophysiological studies are based on the idea that stimulus and behavioral information is encoded in the firing rates of single neurons. At the same time, precise spike coordination in the spikes of neuronal population is discussed as an indication of coordinated network activity relevant for information processing. Thus accurate assessment of neural correlations is a key to understand their coding principles.

Statistical structure of neural correlations may change while an animal is engaged in behavioral tasks. We suggest estimating the dynamics of neural correlations using a state-space method, and further developed a method to test their significance. With this method, we are investigating behavior-dependent interactions in neural populations.

Paper and proceedings

• Aguilera M, Moosavi SA,Shimazaki H. A unifying framework for mean-field theories of asymmetric kinetic Ising systems. (2021) Nature Commun 12 (1), 1197 link
• Gaudreault J, Saxena A, Shimazaki H. Online estimation of multiple dynamic graphs in pattern sequences. (2019) IJCNN arXiv:1901.07298 [pdf]
• Donner C., Obermeyer K., Shimazaki H. Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations. PLoS Comp Biol (2017) 13(1): e1005309 [link, code]
• Shimazaki H., Amari S., Brown E. N., and Gruen S., State-space Analysis of Time-varying Higher-order Spike Correlation for Multiple Neural Spike Train Data. PLoS Comput Biol 8(3): e1002385 (2012) link
• Shimazaki H., Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study. J. Phys.: Conf. Ser. (2013)
• Shimazaki H., Amari S., Brown E. N., and Gruen S. State-space Analysis on Time-varying Correlations in Parallel Spike Sequences, Proc. IEEE ICASSP2009, 3501-3504.

Invited talks

• ICSG2013. ELC International Meeting on ''Inference, Computation, and Spin Glasses'', Sapporo, Japan.
• NIPS2008 Workshop. "Statistical Analysis and Modeling of Response Dependencies in Neural Populations:, Whistler, Canada
• IEEE ICASSP2009. Special Session on 'Signal Processing for Neural Spike Trains', Taipei, Taiwan

Award

• Best student paper award at IJCNN2019 (Gaudreault J, Saxena A, Shimazaki H)
• Excellent paper award at ICONIP2016 (Donner C, Obermeyer K, and Shimazaki H)
• Japanese Neural Network Society Research Award 2009 (Shimazaki H, Amari S, Brown EN, and Gruen S)

Single Neurons: Optimizing Spike-rate Estimation

Neurons process sensory information using event sequences called action potentials or spikes. Much of the neurophysiological literature relies on the interpretation of the spike data by making a histogram or kernel density estimation of the spike-rate.

However, the quality of rate estimation is critically determined by the choice of bin-width or kernel bandwidth. We theoretically investigated accuracy and limitation in estimating the time-dependent spike-rate from finite samples, and developed methods to optimize the histogram bin-width and kernel bandwidth under objective error criteria.

These optimization mehtods are being used in labs of leading neuroscientists. Examples are Hill, Fried & Koch J Neurophysiol2015. Ito, Zhang, Witter, Moser & Moser Nature 2015. Manita, Suzuki, ..., Larkum & Murayama Neuron 2015.

Histogram Optimization

• [1] Shimazaki and Shinomoto, A method for selecting the bin size of a time histogram, Neural Computation (2007) Vol. 19, No. 6, 1503-1527.
• [2] Shimazaki and Shinomoto: A recipe for optimizing a time-histogram. Advances in NIPS 2007 Vol. 19, 1289-1296.
• Award Japanese Neural Network Society Young Researcher Award 2007

Kernel Optimization

• [3] Shimazaki and Shinomoto: Kernel Bandwidth Optimization in Spike Rate Estimation Journal of Computational Neuroscience (2010) Vol. 29 (1-2) 171-182.
• [4] Shimazaki and Shinomoto: Kernel Width Optimization in the Spike-rate Estimation. Neural Coding 2007 Abstract p120-123, Montevideo, Uruguay.
• [5] Spike-rate Estimation with Locally Adaptive Kernel Method. JNNS2008 p186-187 in Japanese.
• Award Best Poster Award at Neural Coding 2007

• Please contact me if you want to apply for funding opportunities in Japan from our institute. See JSPS Postdoctral Fellowship for Research in Japan.

Postdoc and visiting scholar

	Dr Ulises Rodriguez Dominguez	2023 Apr – present (Kyoto U) 2022 Apr 19 – 2022 Jun 30 (Hokkaido U) 2020 Sep – present (online)
	Dr. Anuj Mishra	2023 Apr – present (Kyoto U) 2020 Mar – 2021 Aug (online)
	Dr. Sayed Amin Moosavi (Kyoto University, Japan)	2018 Mar – 2020 Mar
	Dr. Miguel Aguilera (University of the Basque Country, Spain)	2018 Jul – 2018 Aug

Graduate students

	Alfi Sajeev (IISER, India)	2024 June - present (online)
	Theo Marshal (ENS-Paris-Saclay, France)	2023 Apr 17 – 2023 Aug 21
	Sai Sumedh (IIT Bombay, India)	2020 Sep – present (online) 2022 July 12 – 2022 July 25 (Hokkaido U)
	Masanao Igarashi (Hokkaido U, Japan)	2020 Oct - 2022 Mar
	Ken Ishihara (Hokkaido U, Japan)	2020 Oct - present
	Safura Rashid Shomali (IPM, Iran)	2013 Dec – 2017 Sep
	Christian Donner (BCCN Berlin, Germany)	2014 Jun – 2015 Apr 2018 Mar 18 – Apr 6

Undergraduate students (Internship)

	Bingyue Zhu (University of British Columbia, Canada) Sequential Bayes estimation and anomaly detection method for physiological time-series signals	2019 July – 2019 Aug 2018 Jun 20 – 2019 Apr 26
	Magalie Tatischeff (University of British Columbia, Canada) Data analysis and development of statistical models for spiking activity of neural populations	2018 Jun 20 – 2019 Apr 26
	Shukla Shashwat (IIT Bombay, India) Investigation of advanced time-series models for neural and behavioral activities	2018 May 14 – 2018 Jul 13
	Krishna Subramani (IIT Bombay, India)  Learning a hierarchical model for spatial phases in natural scenes	2018 May 14 – 2018 Jul 13
	Brinti Datta (IIT Bombay, India)                            Filtering methods for time-series analysis	2017 Jul 5 – 2017 Jul 14
	Jimmy Gaudreault (Polytechnique Montreal) Data analysis of neural population activities by the dynamic Ising model	2017 May 15 –2018 Apr 27
	Arunabh Saxena (IIT Bombay, India)                       　 Studying and extending the state-space analysis methods for neural populations	2017 May 8 – 2017 July 14

□ Preprint ■ Under Review ■ Published / In Press

[Google Scholor] [Researchmap] [ORCID] [Kyoto University DB]

Papers and Proceedings

■ Hideaki Shimazaki. Neural coding: Foundational concepts, statistical formulations, and recent advances. (2025) Neuroscience Research link

□ Ken Ishihara, Hideaki Shimazaki. State-space kinetic Ising model reveals task-dependent entropy flow in sparsely active nonequilibrium neuronal dynamics. (2025) arXiv:2502.15440 link

□ S. Amin Moosavi, Sai Sumedh, R. Hindupur, Hideaki Shimazaki. Population coding under the scale-invariance of high-dimensional noise. (2024) bioRxiv link

□ Miguel Aguilera, Pablo A. Morales, Fernando E. Rosas, Hideaki Shimazaki. Explosive neural networks via higher-order interactions in curved statistical manifolds. (2024) arXiv:2408.02326 link

■ Christian Donner, Anuj Mishra, Hideaki Shimazaki, A projected nonlinear state-space model for forecasting time series signals. (2025) International Journal of Forecasting. link (arXiv:2311.13247 link)

□ Ulises Rodríguez-Domínguez, Hideaki Shimazaki. Modeling higher-order interactions in sparse and heavy-tailed neural population activity. (2023) arXiv:2308.13257 link

■ Miguel Aguilera, Masanao Igarashi, Hideaki Shimazaki. Nonequilibrium thermodynamics of the asymmetric Sherrington-Kirkpatrick model. (2023) Nature Communications 14, 3685 10.1038/s41467-023-39107-y (arXiv:2205.09886)

■ Safura Rashid Shomali, Majid Nili Ahmadabadi, Seyyed Nader Rasuli, Hideaki Shimazaki.Uncovering hidden network architecture from spiking activities using an exact statistical input-output relation of neurons. (2023) Communications Biology. 6, 169 s42003-023-04511-z link

■ Kazuyoshi Tatsumi, Yasuhiro Inamura, Maiko Kofu, Ryoji Kiyanagi and Hideaki Shimazaki. Optimization and inference of bin widths for histogramming inelastic neutron scattering spectra. (2022) Journal of Applied Crystallography. 55, 533-543 link

■ Takuya Isomura, Hideaki Shimazaki, Karl Friston. Canonical neural networks perform active inference. (2022) Communications Biology 5, 55. link

■ Makio Torigoe, Tanvir Islam, Hisaya Kakinuma, Chi Chung Alan Fung, Takuya Isomura, Hideaki Shimazaki, Tazu Aoki, Tomoki Fukai, Hitoshi Okamoto. Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality. (2021) Nature Communications 12, 5712 10.1038/s41467-021-26010-7

■ Miguel Aguilera, S. Amin Moosavi, Hideaki Shimazaki. A unifying framework for mean-field theories of asymmetric kinetic Ising systems. (2021) Nature Communications 12, 1197 10.1038/s41467-021-20890-5 (arXiv:2002.04309)

■ Donner C, Tagasovska N, He G, Mulleners K, Shimazaki H, Obozinski G. Learning interpretable latent dynamics for a 2D airfoil system. International Conference on Learning Representations (ICLR2021) Workshop: Robust and reliable machine learning in the real world. 2021. link

□ Hideaki Shimazaki. The principles of adaptation in organisms and machines II: Thermodynamics of the Bayesian brain. (2020) arXiv:2006.13158

■ Haruna Nakajo, Ming-Yi Chou, Masae Kinoshita, Lior Appelbaum, Hideaki Shimazaki, Takashi Tsuboi, Hitoshi Okamoto. Hunger potentiates the habenular winner pathway for social conflict by orexin-promoted biased alternative splicing of the AMPA receptor gene. Cell Reports (2020) 31, 107790 [link]

□ Shashwat Shukla, Hideaki Shimazaki, Udayan Ganguly. Structured mean-field variational inference and learning in winner-take-all spiking neural networks. (2019) arXiv:1911.05943 [pdf]

□ Hideaki Shimazaki.The principles of adaptation in organisms and machines I: machine learning, information theory, and thermodynamics. (2019) arXiv:1902.11233 [pdf]

■ Jimmy Gaudreault, Arunabh Saxena, Hideaki Shimazaki. Online estimation of multiple dynamic graphs in pattern sequences. (2019) IJCNN2019 arXiv:1901.07298 [pdf]

■ Jimmy Gaudreault and Hideaki Shimazaki. State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons. Lecture Notes in Computer Science (2018) 11141, 641–651 [link]

■ 島崎秀昭. 認識と行動の適応原理. 日本神経回路学会誌．解説論文 In press (2018)

■ Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, Seyyed Nader Rasuli. How does transient signaling input affect the spike timing of postsynaptic neuron near the threshold regime: an analytical study. Journal of Computational Neuroscience (2018) 44(2), 147-171 [link, pdf]

■ Kass RE, Amari S, Arai K, Diekman CO, Diesmann M, Doiron B, Fairhall A, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, Teramae J, Thomas PJ, Reimers M, Rodu J, Rotstein HG, Shea-Brown E, Shimazaki H, Shinomoto S, Yu BM, Kramer MA. Computational Neuroscience: Mathematical and Statistical Perspectives. Annual Review of Statistics and Its Application. in press [link]

■ MaBouDi H, Shimazaki H, Giurfa M, Chittka L. Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities. PLoS Computational Biology (2017) 13(6): e1005551. [link]

□ MaBouDi H, Subramani K, Soltanian-Zadeh H, Amari S, Shimazaki H. Learning complex representations from spatial phase statistics of natural scenes. bioRxiv (2017) doi: https://doi.org/10.1101/112813

■ Donner C, Obermeyer K, Shimazaki H. Approximate inference for time-varying interactions and macroscopic dynamics of neural populations. PLoS Computational Biology (2017) 13(1): e1005309 [link, code]

■ Donner C and Shimazaki H. Approximate inference method for dynamic interactions in larger neural populations. The 23rd International Conference on Neural Information Processing (ICONIP 2016) LNCS (2016) 9949, 104–110

■ Mochizuki Y, Onaga T, Shimazaki H, Shimokawa T, Tsubo Y, Kimura R, Saiki A, Sakai Y, Isomura Y, Fujisawa S, Shibata K, Hirai D, Furuta T, Kaneko T, Takahashi S, Nakazono T, Ishino S, Sakurai Y, Kitsukawa T, Lee JW, Lee H, Jung MW, Babul C, Maldonado PE, Takahashi K, Arce-McShane FI, Ross CF, Sessle BJ, Hatsopoulos NG, Brochier T, Riehle A, Chorley P, Gruen 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, Ogawa T, Komatsu H, Koida K, Toyama K, Richmond BJ, and Shinomoto S. Similarity in Neuronal firing regimes across mammalian species. Journal of Neuroscience (2016) 36:5736-5747. [link]

■ Chou M.Y., Amo R., Kinoshita M., Cherng B.W., Shimazaki H., Agetsuma M., Shiraki T., Aoki T., Takahoko M., Yamazaki M., Higashijima S., and Okamoto H. Social conflict resolution regulated by two dorsal habenular subregions in zebrafish. Science, (2016) Vol. 352(6281) 87-90 [link]

■ MaBouDi H, Shimazaki H, Amari S, Soltanian-Zadeh H, Representation of higher-order statistical structures in natural scenes via spatial phase distributions. Vision Research (2016) 120, 61–73 [pdf, link]

□ Shimazaki H, Neurons as an Information-theoretic Engine. arXiv:1512.07855 (2015) [pdf, link]

■ Shimazaki H, Sadeghi K, Ishikawa T, Ikegaya Y, Toyoizumi T, Simultaneous silence organizes structured higher-order interactions in neural populations. Scientific Reports (2015) 5, 9821. [link]

■ Shimazaki H, Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking activity of multiple neurons: a simulation study. J. Phys.: Conf. Ser. (2013) 473 012009. [link, pdf]

■ Shimazaki H, Amari S, Brown EN, and Gruen S, State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLOS Computational Biology (2012) 8(3): e1002385. [link, pdf, support page]

■ Shimazaki H, Analysis of multiple neural spike train data using the log-linear model : From stationary to time-varying spike correlation. The Brain & neural networks (2011) 8(4) 194-203 (In Japanese) [link, pdf]

■ Shimazaki H and Shinomoto S, Kernel bandwidth optimization in spike rate estimation. Journal of Computational Neuroscience (2010) Vol. 29 (1-2) 171-182 [link, pdf, support page]

■ Shimazaki H, Amari S, Brown EN, and Gruen S, State-space analysis on time-varying correlations in parallel spike sequences. Proc. IEEE ICASSP2009 (2009) 3501-3504 [link]

■ Shimazaki H and Shinomoto S, A method for selecting the bin size of a time histogram, Neural Computation (2007) Vol. 19(6), 1503-1527 [link, pdf, support page]

■ Shimazaki H and Shinomoto S, A recipe for optimizing a time-histogram, Advances in Neural Information Processing Systems (2007) Vol. 19, 1289-1296 [pdf, support page]

■ Shimazaki H and Niebur E, Correlated multiplicative modulation in coupled oscillator systems: a model of selective attention, Progress of Theoretical Physics Supplement (2006) No.161 336-339 [link]

■ Shimazaki H and Niebur E, Phase transitions in multiplicative competitive processes, Physical Review E (2005) 72(1), 011912 [link, pdf]

■ Tsukada M, Aihara T, Kobayashi Y, Shimazaki H., Spatial analysis of spike-timing-dependent LTP and LTD in the CA1 area of hippocampal slices using optical imaging, Hippocampus (2005) 15(1), 104-109. [link]

■ Kobayashi Y, Shimazaki H., Aihara T, Tsukada M. Spatial distributions of hippocampal LTP/LTD induced electrically from schaffer collaterals and stratum oriens with relative timing, The Brain & neural networks (2001) Vol8,No.2 p.57-64. (In Japanese) [link]

Ph.D. Thesis

■ Recipes for Selecting the Bin Size of a Histogram link

Book chapter

■ Neural Engine Hypothesis. In: Chen Z., Sarma S. (eds) Dynamic Neuroscience. (2018) Springer, Cham link

Translation

■ Principles of Neural Science 5th edition, Kandel et al.; McGraw-Hill Professional (2012) "Appendix F, Theoretical Approaches to Neuroscience: Examples from SingleNeurons to Networks" by Abbott, Fusi, and Miller. [link]

Conference, Workshop, Seminar (Those written in Japanese were presented in Japanese.)

■ 2023

巽 一厳 , 松浦 直人 , 山田 武 , 川北 至信 , 島崎 秀昭．中性子準弾性散乱解析への可変幅カーネル密度推定の適用　日本中性子科学会　札幌　link POSTER

石原憲*, 島崎秀昭．非定常・非対称な機能的結合の推定に基づく神経スパイク活動の不可逆性の検証．東京大学 本郷キャンパス 2023年9月5日 POSTER

島崎秀昭．神経符号化研究の歴史と最前線．第33回 日本神経回路学会全国大会 サテライトシンポジウム 「理論と実験の融合のための神経科学チュートリアル」　東京大学 本郷キャンパス 2023年9月3日 link LECTURE

Hideaki Shimazaki. Introduction to computational theories of the brain: from the efficient coding hypothesis to the Bayesian brain and free energy principle．The 46th Annual Meeting of the Japan Neuroscience Society (NEURO2023), Sendai, Japan. 2023 Aug 4 link INVITED LECTURE (In Japanese)

Deciphering hidden circuits from higher-order statistics of neural activity CSN Virtual Seminars at Julich Research Center, Online Jul 5, 2023 link

石原憲*, 島崎秀昭．状態空間–キネティックイジングモデルによる非平衡神経スパイク時系列 の解析ニューロコンピューティング研究会 沖 縄 2023年7月1日 TALK

島崎秀昭．大脳皮質局所回路の結合推定と符号化方式．第37回 全脳アーキテクチャ勉強会 オンライン 2023年2月17日 INVITED TALK link　

Ken Ishihara, Hideaki Shimazaki. Analysis of nonequilibrium neuronal dynamics by the state-space kinetic model. Winter Workshop on Mechanism of Brain and Mind 2023. Rusutsu, Japan. Jan 6, 2023 link POSTER

■ 2022

島崎秀昭．脳の認識のダイナミクスと時間遅れ．2022年度 RIMS 共同研究（公開型）「時間遅れ系と数理科学：理論と応用の新たな展開に向けて」 京都大学数理解析研究所 2022年11月17日 link

島崎秀昭．神経活動の高次統計量から回路構造を読み解く．日本神経回路学会 オータムスクールASCONE2022 グランポート木更津, 千葉 2022年11月14日 link

島崎秀昭．脳の自由エネルギー原理：背景と応用． 応用脳科学コンソーシアム アドバンスコース「脳に学ぶAI」 2022年9月9日 NTTデータ経営研究所, 東京 link

島崎秀昭．神経細胞集団活動の数理とデータ解析．日本応用数理学会2022年度年会． 2022年9月8日 北海道大学 link

Hideaki Shimazaki. Consciousness and the thermodynamics of the Bayesian brain. International Symposium on Artificial Intelligence and Brain Science 2022, Jul 5, 2022 link

Ken Ishihara, Hideaki Shimazaki. The state-space kinetic Ising model for nonequilibrium neuronal dynamics. NEURO2022, Jul 2, 2022

Hideaki Shimazaki, Ken Ishihara, Ulises Rodriguez Dominguez, Sai Sumedh Hindupur, Miguel Aguilera, S. Amin Moosavi, Magalie Tatischeff, Jimmy Gaudreault, Christian Donner. State-space analysis for neural population dynamics. Neuro2022, Jun 30, 2022

■ 2021

島崎秀昭．標準リカレントネットワークモデルでつなぐ皮質回路の構造・機能・作動原理. 　大脳皮質を中心とした神経回路： 構造と機能、その作動原理． 令和3年度生理学研究所研究会 2021年12月3日 岡崎カンファレンスセンター INVITED TALK link

Miguel Aguilera*, S. Amin Moosavi, Hideaki Shimazaki. An information geometry approach for unifying mean field theories of asymmetric kinetic Ising systems. Entropy 2020: The Scientific Tool of the 21st Century May 7, 2021. Online. *TALK link

島崎秀昭．非定常・非平衡イジングモデルによる神経細胞集団活動の解明. データ駆動生物学ワークショップ. MACS教育プログラム（京都大学 理学研究科）2021年3月23日 オンライン INVITED TALK link

Safura Rashid Shomali*, S. Nader Rasuli, Hideaki Shimazaki. Revealing hidden microcircuits using higher-order interactions of neuronal activity. The 3rd Sharif Neuroscience Symposium Mar 4, 2021. Online *TALK

島崎秀昭．脳への計算論的アプローチ概説: 視覚野の理論を中心に．日本視覚学会2021年冬季大会 企画セッション「視覚・脳科学への計算論的アプローチの最前線」 2021年1月21日 オンライン INVITED TALK link

■ 2020

Safura Rashid Shomali, Seyyed Nader Rasuli, Hideaki Shimazaki. Higher-order interactions induced by strong shared inputs. 29th Annual Computational Neuroscience Meeting: CNS*2020. Dec 21 BMC Neuroscience 2020, 21(Suppl 1): P185 link

島崎秀昭．ヒストグラム・カネール密度推定の最適化法の紹介．先進計算環境関連研究会 J-PARC 中性子散乱への情報科学の応用 2020年10月14日 オンライン INVITED TALK

島崎秀昭．神経活動の数理モデリングで回路・情報，そして意識へ迫る. CHAIN セミナー 2020年8月3日 オンライン TALK link

Seyedamin Moosavi, Magalie Tatischeff, Bingyue Zhu, Hideaki Shimazaki. Effects of structured neural correlations in population coding: beneficial or detrimental? Computational and Systems Neuroscience (Cosyne) 2020. Feb 27 - Mar 1. Dember, USA POSTER

Safura Rashid Shomali, Majid Nili Ahmadabadi, Seyyed Nader Rasuli, Hideaki Shimazaki. Inferring network motifs from neural activity using analytic input-output relation of LIF neurons.Computational and Systems Neuroscience (Cosyne) 2020. Feb 27 - Mar 1. Dember, USA POSTER

Hideaki Shimazaki. Thermodynamics of the Bayesian brain: A new paradigm for quantifying perceptual capacity of neural dynamics. Computational Principles in Active Perception and Reinforcement Learning in the Brain. Feb 13-14, 2020. Kyoto University. INVITED TALK link

Hideaki Shimazaki. The brain as an information-theoretic engine: A new paradigm for quantifying perceptual capacity of neural dynamics. Combining Information theoretic Perspectives on Agency. Jan 28-29, 2020. The University of Tokyo, Japan. INVITED TALK link

■ 2019

島崎 秀昭. 脳の理論の過去・未来：ベイズ脳仮説からニューラルエンジンへ． シンギュラリティサロン東京．大手町サンケイプラザ　2019年12月7日TALK link

島崎 秀昭. 脳の理論の過去・未来：ベイズ脳仮説からニューラルエンジンへ シンギュラリティサロン大阪．グランフロント大阪・ナレッジサロン・プレゼンラウンジ　2019年11月16日 TALK link

Hideaki Shimazaki*. Dynamic neural interactions revealed by the state-space Ising model. The 7th International Congress on Cognitive Neurodynamics. Sep 29. Alghero, Italy. TALK link

Safura Rashid Shomali*, Majid Nili Ahmadabadi, Seyyed Nader Rasuli, Hideaki Shimazaki
Judging between Excitation and Inhibition: Identifying Local Network Architecture by an Analytic Pre-Post Relation. Bernstein Conference 2019. Sep 18, Berlin, Germany. POSTER link

Miguel Aguilera*, Amin Seyed Moosavi, Hideaki Shimazaki. Unifying framework for mean field theories of asymmetric kinetic Ising systems . 15th Granada Seminar on Computational and Statistical Physics: Stochastic and Collective Effects in Neural Systems. Sep 17. Universidad de Granada, Spain. TALK link

島崎 秀昭. 学習と認識の熱力学：ニューラルエンジンとはなにか？ 生理研研究会2019 認知神経科学の先端 「脳の理論から身体・世界へ」 岡崎 2019年9月2日 TALK

Hideaki Shimazaki. Visualizing dynamics of cooperative activities of neurons for neural coding studies. Data Science, Statistics, & Visualisation (DSSV2019) Aug. 13. Doshisha University, Kyoto, Japan. TALK link

島崎 秀昭. 神経細胞集団活動の統計解析：脳の熱力学に向けて. サロン・ド・脳.　京都大学　2019年6月7日 TALK

Hideaki Shimazaki. Network architecture underlying sparse neural activity characterized by structured higher-order interactions. Okinawa Institute of Technology (OIST). Okinawa, Japan. Mar 26 2019 TALK link

S. Amin Moosavi, Hideaki Shimazaki. Role of gain-control and neural correlations in efficient stimulus coding. Consciousness Research Network (CoRN 2019) Okazaki, Japan. Feb 23 2019 POSTER

Magalie Tatischeff, Jimmy Gaudreault, Christian Donner, Hideaki Shimazaki. Thermodynamic analysis of neural populations by the state-space Ising model. Consciousness Research Network (CoRN 2019). Okazaki, Japan. Feb 23 2019 POSTER

■ 2018

Hideaki Shimazaki, Magalie Tatischeff, Jimmy Gaudreault, Christian Donner. Thermodynamic analyses of neural populations. Analysis and Synthesis for Human/Artificial Cognition and Behaviour, A satellite workshop of JNNS2018. Okinawa Institute of Science and Technology Graduate University. Oct 22, 2018 POSTER link

Jimmy Gaudreault and Hideaki Shimazaki. State-space analysis of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons. The 27th International Conference on Artificial Neural Networks (ICANN2018) Oct 5, 2018 POSTER

島崎秀昭 スパースな集団活動を生み出す神経ネットワーク構造. 大規模ネットワーク上のイベント生起ダイナミクスの予測と制御 軽井沢 2018年7月6日 TALK link

Jimmy Gaudreault, Arunabh Saxena, Hideaki Shimazaki. State-space analys of an Ising model reveals contributions of pairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons. 脳と心のメカニズム第17回冬のワークショップ 北海道 2018年1月9日 POSTER

■ Seminar, Conference, Workshop in 2017

Shimazaki H. Network architectures underlying variable, sparse population activity of neurons. Fluctuations of event occurrences in a variety of networks. Kyoto, Japan. Nov 15, 2017 TALK link

島崎秀昭 神経細胞集団活動の統計数理 玉川大学脳科学ワークショップ 石和温泉 2017年9月28日 TALK (Invited)

Makio Torigoe, Islam Tanvir, Hisaya Kakinuma, Hideaki Shimazaki, Chi Chung Alan Fung, Tazu Aoki, Tomoki Fukai, Hitoshi Okamoto In-vivo imaging of telencephalic neural activities in adult zebrafish performing decision making task in the closed-loop virtual reality environment. Annual Meeting of the Japan Neuroscience Society (Neuro2017). Makuhari Messe, Japan. Jul 23 2017. TALK by Torigoe

島崎秀昭 神経活動データの時系列モデリング入門 第8回脳科学若手の会合宿 ホテルウィングインターナショナル相模原 2017年3月4, 5日 Lecture (Invited)

■ 2017

Shimazaki H. Network architectures underlying variable, sparse population activity of neurons. Fluctuations of event occurrences in a variety of networks. Kyoto, Japan. Nov 15, 2017 TALK link

島崎秀昭 神経細胞集団活動の統計数理 玉川大学脳科学ワークショップ 石和温泉 2017年9月28日 TALK (Invited)

Makio Torigoe, Islam Tanvir, Hisaya Kakinuma, Hideaki Shimazaki, Chi Chung Alan Fung, Tazu Aoki, Tomoki Fukai, Hitoshi Okamoto In-vivo imaging of telencephalic neural activities in adult zebrafish performing decision making task in the closed-loop virtual reality environment. Annual Meeting of the Japan Neuroscience Society (Neuro2017). Makuhari Messe, Japan. Jul 23 2017. TALK by Torigoe

島崎秀昭 神経活動データの時系列モデリング入門 第8回脳科学若手の会合宿 ホテルウィングインターナショナル相模原 2017年3月4, 5日 Lecture (Invited)

■ Seminar, Conference, Workshop in 2016

Donner C. and Shimazaki H., Estimating dynamic functional networks of larger neural populations. Society for Neuroscience 2016. Nov 16, 2016 POSTER

Shomali S.R., Ahmadabadi M.N., Rasuli N. S., Shimazaki H., Exact analysis of spike­timing and higher­order interactions of neurons at the threshold regime suggests network architecture underlying sparse population activity. Society for Neuroscience 2016. Nov 14, 2016 POSTER

Donner C. and Shimazaki H., Large-scale inference of time-varying neural interactions. The 23rd International Conference on Neural Information Processing (ICONIP 2016). Kyoto, Japan. Oct 16, 2016 (Talk by Donner) EXCELLENT PAPER AWARD

MaBouDi H., Shimazaki H., Lars Chittka L. Modelling elemental learning of honeybees by spiking neural networks. EURBEE 2016. Cluj-Napoca, Romania. Sep 7, 2016 (Talk by MaBouDi)

Shimazaki H. Toward thermodynamic principles of consciousness. University of Sussex, UK. Jul 7, 2016. TALK

Shimazaki H. Higher-order interactions of neural populations. University College London, UK. Jul 5, 2016. TALK

Shimazaki H. Population coding of neurons: Dynamics, higher-order interactions, and mechanisms. Queen Mary University of London, UK. Jul 4, 2016. TALK

Shomali S.R., Ahmadabad M.N., Shimazaki H., Rasuli SN. Exact spike-timing distribution reveals higher-order interactions. CNS*2016. Jeju, South Korea. Jul 2, 2016 (Poster by Shomali) POSTER (Reviewed) links

島崎秀昭, Andrew J Peters, 小宮山尚樹, 豊泉太郎. Redundant spatiotemporal coding by layer 2/3 neurons of motor cortex during initial motor learning. 第1７回ノンパラメトリック統計解析とベイズ統計 慶應義塾大学三田キャンパス 2016年3月29・30日 TALK

島崎秀昭 神経ネットワークの情報コーディング：ダイナミクス・高次相関・メカニズム. 第4回東工大若手物性セミナー 東京工業大学すずかけ台キャンパス 2016年2月18日 TALK (Invited)

島崎秀昭 動的イジングモデルを用いた神経回路網活動の解析 NS Forum 国際基督教大学 2016年2月2日 TALK (Invited)

島崎秀昭, Andrew J Peters, 小宮山尚樹, 豊泉太郎 Redundant coding by layer 2/3 neurons of motor cortex during initial motor learning. 脳と心のメカニズム　第16回冬のワークショップ　北海道ルスツ 2016年1月6,7,8日 POSTER [link]

■ Seminar, Conference, Workshop in 2015

Christian Donner and Hideaki Shimazaki. Approximation methods for inferring time-varying interactions of a large neural population. NIPS 2015 Workshop on `Statistical Methods for Understanding Neural Systems'. Montreal, Canada. Dec 11, 2015 POSTER [link1, link2]

島崎秀昭 神経回路網活動の熱力学的考察 数理モデリング研究会 滋賀 2015年11月28,29日 TALK [link]

Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations. Juelich Research Center. Juelich, Germany. May 5, 2015 Seminar TALK [link]

Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations. Bernstein Center of Computational Neuroscience Berlin. Berlin, Germany. Apr 28, 2015 TALK [link]

島崎秀昭 Simultaneous silence explains structured higher-order interactions of neural populations. 第16回ノンパラメトリック統計解析とベイズ統計 慶應義塾大学 2015年3月25日 TALK

島崎秀昭 神経細胞の集団活動の高次統計とメカニズム 第３回ヘテロ・ニューロ・アナリシス研究会 玉川大学 2015年3月18日 TALK

■ Seminar, Conference, Workshop in 2014

Thomas Sharp, Hideaki Shimazaki, Yoshikazu Isomura and Tomoki Fukai. State-space analysis of behaviour- and layer-dependent synchrony in motor cortex during volitional arm movement. Society for Neuroscience (SfN) 2014, Washinton DC, USA. Nov 16, 2014 POSTER

Thomas Sharp, Hideaki Shimazaki, Yoshikazu Isomura and Tomoki Fukai. Behaviour- and layer-dependent synchrony in motor cortex during volitional arm movement, Neuro2014, Yokohama, Japan. Sep 11, 2014 POSTER

Safura Rashid Shomali, Majid Nili Ahmadabadi, Hideaki Shimazaki, S Nader Rasuli. Theoretical study on spike-timing probability in a pair of pre-post synaptic neurons. Neuro2014, Yokohama, Japan. Sep 11, 2014 POSTER

Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations. June 3 11:00-12:00 Place: CiNet 1F Conference Room A, TALK

Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations. 京都大学セミナー, June 2 TALK

Tom Sharp*, Hideaki Shimazaki, Yoshikazu Isomura and Tomoki Fukai. Behaviour- and layer-dependent synchrony in motor cortex during volitional arm movement. Workshop on data mining in neuroscience. National Institute of Informatics, Tokyo, Japan. May 28-29, 2014 TALK

HaDi MaBouDi, Hideaki Shimazaki, Hamid Soltanian-Zadeh, Shun-ichi Amari. Bimodal Distributions of Local Phase Variables in Natural Images. The 2014 VSS Annual Meeting, St. Pete Beach, Florida. May 18, 2014 POSTER

HaDi MaBouDi, Hideaki Shimazaki, Mehdi Abouzari, Shun-ichi Amari, Hamid Soltanian-Zadeh, Statistical inference for directed phase coupling in neural oscillators. Computational and Systems Neuroscience (Cosyne) 2014. Feb 27 Salt Lake City, USA. POSTER

島崎秀昭. State-space analysis of higher-order interactions in parallel event sequences 第15回ノンパラメトリック統計解析とベイズ統計 慶応義塾大学三田キャンパス 2014年3月19日 TALK

島崎秀昭. 高次相関を伴う神経回路網活動：局所回路の計算原理を求めて. 神経科学と統計科学の対話４. 統計数理研究所 2013年3月17日 TALK

Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. Structured higher-order interactions explain the simultaneous silence of neural populations. 脳と心のメカニズム 第14回 冬のワークショップ 2014年1月8日(水)-10日(金)　ポスター発表

島崎秀昭「留学という選択」 大学院留学・ポスドク留学・語学留学・サマースクールなど異なるタイプの留学についてお話します．また日本に来る留学生への対応や海外での講義体験などの国際交流についてもご紹介します．脳科学若手の会 2014年1月25日(日)

■ Seminar, Conference, Workshop in 2013

Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T., The simultaneous silence of neurons explains structured higher-order interactions in ensemble spiking activity, SfN2013. POSTER

Shimazaki H., TBA, ELC International Meeting on ''Inference, Computation, and Spin Glasses'' (ICSG2013), Sapporo, Japan. July 28-30, 2013 TALK (Invited)

Shimazaki H., Higher-order interactions in population activity of hippocampal CA3 neurons, Workshop on statistical analysis of neurophysiological and clinical data, Kyoto, Japan. TALK

Shimazaki H., Estimating Time-varying Higher-order Neuronal Interactions in Awake Behaving Animals, Modeling Neural Activity: Statistics, Dynamical Systems, and Networks, Lihue, Hawaii, USA. 2013 June 26-28 TALK (Selected)

Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T., The simultaneous silence of neurons explains higher-order interactions in ensemble spiking activity, Neuro2013, Kyoto, Japan. Jun. 20 POSTER

Shimazaki H. Estimating dynamic neural interactions in awake behaving animals. The 3rd Mathematical Neuroscience Workshop in School of Mathematics, Institute for Research in Fundamental Sciences (IPM). Tehran, Iran. Mar. 13 TALK (Invited)

Shimazaki H. The Simultaneous Silence of Neurons Explains Higher-Order Interactions in Ensemble Spiking Activity. The 3rd Mathematical Neuroscience Workshop in School of Mathematics, Institute for Research in Fundamental Sciences (IPM). Tehran, Iran. Mar. 13 TALK (Invited)

島崎秀昭. 高次相関を伴う神経回路網の活動と情報コーディング 玉川大学脳科学若手の会 第88回談話会 Feb 22 TALK (Invited)

Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. The simultaneous silence of neurons explains structured higher-order interactions in spontaneous spiking activity. 神経科学と統計科学の対話３. 統計数理研究所 Feb 18-19 TALK

■ Seminar, Conference, Workshop in 2012

Shimazaki H., Kolia Sadeghi, Yuji Ikegaya, Taro Toyoizumi. Joint inactivation statistics of population spiking activities. RIKEN BSI Retreat 2012, Nov 12-13, Karuizawa, Japan

Shimazaki H. The simultaneous silence of neurons explains structured higher-order interactions in ensemble spiking activity. BSI Lunch Seminar 2012 Nov. 8

Shimazaki H., Kolia Sadeghi, Yuji Ikegaya, Taro Toyoizumi. Joint inactivation statistics of population spiking activities. Workshop on statistical aspects of neural coding. Nov 1-2. Kyoto University & Ritsumeikan University.

Shimazaki H. Tracking Dynamic Neural Interactions in Awake Behaving Animals. Workshop on neural information flow, Kyoto University, Kyoto Jun 20 2012 TALK (Invited)

島崎秀昭, 篠本滋. ヒストグラム・カーネル密度推定の神経スパイクデータへの適用：理論と実践 第13回ノンパラメトリック統計解析とベイズ統計 2012年3月29・30日 慶應義塾大学三田キャンパス　TALK (Invited)

Shimazaki H., Kolia Sadeghi, Yuji Ikegaya, Taro Toyoizumi, The simultaneous silence of neurons explains higher-order interactions in ensemble spiking activity. Computational and Systems Neuroscience (Cosyne) 2012. Salt Lake City, USA. 2012 Feb 23-26 POSTER (Reviewed)

Shimazaki H. 非線形セミナー 神経細胞の高次スパイク相関：状態空間モデルによる解析. 京都大学理学部物理学第一教室 2012 Feb 2 TALK (Invited)

■ Seminar, Conference, Workshop in 2011

島崎秀昭, 小山慎介. 神経スパイク解析における状態空間モデル，GLMの応用. 統計科学と神経科学の対話２ 2011年 12月26-27日 [TALK, Invited]

Shimazaki H., Amari S., Brown E. N., and Gruen S. Dynamics of Higher-order Spike Correlation in an Awake Behaving Monkey: Analysis by a State-space Model. RIKEN BSI Retreat, Karuizawa, Japan. Oct 31 POSTER

Shimazaki H., Ikegaya Y., and Toyoizumi T. A New Sparse Log-linear Model for Simultaneously Active and Inactive Neurons. RIKEN BSI Retreat, Karuizawa, Japan. Oct 31 POSTER

Shimazaki H. and Brown E.N. Copula-based Mixture Time-series Model of Continuous and Point Processes for Synthetic Analysis of Neural Signals. RIKEN BSI Retreat, Karuizawa, Japan. Oct 31 POSTER

島崎秀昭 脳の高次機能と動的高次スパイク相関：状態空間モデルによる解析, 統計神経科学ミニワークショップ 統計数理研究所 9月8日 [TALK, Invited]

Shimazaki H. and Brown E. N. Constructing a joint time-series model of continuous and Bernoulli/Poisson processes using a copula Computational and Systems Neuroscience 2011 Salt Lake City, USA. Feb 24-27 POSTER (Reviewed)

■ Seminar, Conference, Workshop in 2010

Shimazaki H. Analysis of Dynamic Neural Spike Data: From Firing Rates to Spike Correlations. Neurostatistics Working Group Seminar, Dept. of Biostatistics, Harvard University, Dec 1 [TALK, Invited] link

Shimazaki H. Detection of dynamic cell assemblies by the Bayes Factor. Workshop on spatio-temporal neuronal computation, Kyoto University, Japan Sep 6-7 [TALK, Invited]

Shinomoto S*, Shimazaki H, and Shimokawa T. Characterizing neuronal firing with the rate and the irregularity. Neuro2010, Kobe, Japan Sep. 2 S3-10-1-3 [abst]

Shimazaki H., Gruen S., and Amari S. Analysis of subsets of higher-order correlated neurons based on marginal correlation coordinates. Cosyne 2010, Salt Lake City, USA. 1-63 Feb 25-28 [abst]

■ Seminar, Conference, Workshop in 2009

Shimazaki H., Amari S., Brown E. N., and Gruen S. State-space Model of Dynamic Spike Correlation. Japanese Neural Network Society 2009. Sendai, Japan. Sept. 24-26. Talk+Poster [Japanese Neural Network Society 2009 Distinguished Research Award]

Shimazaki H. Analysis of Dynamic Spike Data: From Spike Rate to Multiple Neuron Spike Correlation. The 6th Brain Lunch Seminar. RIKEN Brain Science Institute, Saitama, Japan. Sep. 8. TALK

Shimazaki H., Amari S., Brown E. N., and Gruen S. Bayes Factor Analysis for Detection of Time-dependent Higher-order Spike Correlations. CNS2009 in Berlin, Germany. Jul 18-23 P99 POSTER [abstract]

Shimazaki H. and Shinomoto S. Histogram binwidth and kernel bandwidth selection for the Spike-rate estimation. CNS2009. Berlin, Germany. Jul 18-23 P116 POSTER [abstract]

Shimazaki H., Amari S., Brown E. N., and Gruen S. Estimating time-varying spike correlations from parallel spike sequences, German-Japanese Workshop "Computational and Systems Neuroscience", Berlin, May 25-28, 2009 POSTER

Shimazaki H., Amari S., Brown E. N., and Gruen S. State-space Analysis on Time-varying Correlations in Parallel Spike Sequences. IEEE ICASSP2009 Special Session on `Signal Processing for Neural Spike Trains', Taipei, Taiwan. Apr 24 SS-L10.4 [LECTURE (Invited)]

Shimazaki H., Amari S., Brown E. N., and Gruen S. Detection of non-stationary higher-order spike correlation. Cosyne 2009, Salt Lake City, USA. Feb 26 -Mar 3 POSTER II-62 [abstract]

■ Seminar, Conference, Workshop in 2008

Shimazaki H., Amari S., Brown E. N., and Gruen S. State-space Analysis on Time-varying Higher-order Spike Correlations. NIPS2008 Workshop on `Statistical Analysis and Modeling of Response Dependencies in Neural Populations', Whistler, Canada Dec 13, 17:00-17:30 [abstract] [TALK (Invited)]

Shimazaki H. and Gruen S. Selecting a state-space model of higher-order correlations in parallel spike trains from competing hierarchical log-linear models. RIKEN BSI Retreat 2008, Karuizawa, Japan. Nov 4-5 POSTER

Shimazaki H. and Gruen S. IBIS, Sendai, Japan. Oct 29 Poster. in Japanese.

Shimazaki H. and Shinomoto S. Spike-rate Estimation with Locally Adaptive Kernel Method . Japanese Neural Network Society 2008. Sep 24-26 Tsukuba, Japan. PS3-4, p186-187 in Japanese. [Spotlight Poster]

Shimazaki H. Smoothing methods for spike data: From single neurons rate estimation to multiple neurons correlation analysis. Joint Journal Club at RIKEN BSI. Jun 17

Shimazaki H. and Gruen S. Estimating time-dependent higher-order interactions in parallel spike trains. Neuro2008, Tokyo, Japan. Jul 9-11 POSTER P1-w04 [abstract]

Shimazaki H., Brown E. N. and Gruen S. State-space Analysis on Time-dependent Correlation in Parallel Spike Trains. Statistical Analysis of Neuronal Data (SAND4), Pittsburgh, PA, USA. May 29-31 POSTER [abstract]

■ Seminar, Conference, Workshop in 2007

Shimazaki H. and Gruen S. Estimation of Time-dependent Higher-Order Interactions in Parallel Spike Trains. Riken BSI Retreat, Karuizawa, Japan. Nov 26-28 POSTER

Shimazaki H. and Shinomoto S. Kernel Width Optimization in the Spike-rate Estimation. Neural Coding 2007, Montevideo, Uruguay. Nov 7 POSTER [pdf] [Best Poster Award]

Shimazaki H. and Shinomoto S. Optimization of a Histogram of Spike Data. Neuro2007, Yokohama, Japan. Sep 11 POSTER P2-k22

Shimazaki H. A Recipe for Optimizing a Time Histogram of Spike Data. Riken BSI Forums at RIKEN Brain Science Institute, Wako, Japan. Host: Sonja Gruen Apr 17 TALK

Shimazaki H. A recipe for constructing a Peri-stimulus Time Histogram. The Boadian Seminar at Mind/Brain Institute, Johns Hopkins University. Host: Ernst Niebur. Mar 1 TALK

Shimazaki H. and Shinomoto S. A recipe for optimizing a time-histogram with variable bin sizes. Computational and Systems Neuroscience 2007, Salt Lake City, Feb 22-27 POSTER

■ Seminar, Conference, Workshop in 2006

Shimazaki H. and Shinomoto S. A recipe for optimizing a time-histogram Neural Information Processing Systems　Dec 4-9, Whistler, B. C. POSTER [pdf] [Spotlight Poster]

Shimazaki H. A method to optimize a time histogram by correcting onset latencies of spike sequences Japanese Neural Network Society JNNS2006 Sep 1-21 Nagoya, Japan O4-1 ORAL＋POSTER [Japanese Neural Network Society 2007 Young Researcher Award]

Shimazaki H. Self-organized criticality by natural selection. Frontiers in Dynamics: Physical and Biological Systems Tokyo, Japan, May 22-24, 2006, Poster [abstract]

Shimazaki H. and Shinomoto S. Recipes for constructing an optimal time histogram. Statistical Analysis of Neuronal Data (SAND3), Pittsburgh, PA, May 12-13, 2006, Poster [abstract]

Award

■ Japanese Neural Network Society 2024 Excellent Research Award

■ ICONIP2016 Excellent paper award

■ Japanese Neural Network Society 2009 Research Award

■ Japanese Neural Network Society 2007 Young Researcher Award

Grant and Stipend

■ Grant-in-Aid for Transformative Research Areas A: Cell type census of adaptive neuronal circuits: biological mechanisms of structural and functional organization. 2021 Sep – 2026 Mar

■ New Energy and Industrial Technology Development Organization (NEDO): Human-centered Artificial Intelligence Embedded in the Real World 2020. Apr – 2025 Mar

■ MEXT/JSPS KAKENHI Kiban C: Development of statistical analysis methods for visualizing nonlinear activity of large-scale neural populations. Grant Number JP 20K11709 2020 Apr – 2024 Mar

■ Japanese Neural Network Society 30th anniversary grant 2020 Apr – 2021 Mar

■ The grant of Joint Research by the National Institutes of Natural Sciences: Free energy principle of the brain: Implementation and Verification. NINS Program No. 01112005 2019 Apr – 2021 Mar

■ The grant of Joint Research by the National Institutes of Natural Sciences: Tutorial workshop for the free energy principle 2019 Apr – 2020 Mar

■ The Cooperative Study Program of National Institute for Physiological Sciences 2019 Apr – 2020 Mar

■ Japanese Neural Network Society workshop grant 2018 Apr – 2019 Mar

■ 2010-2011 Excellent Young Researchers Overseas Visit Program by JSPS, Stipend

■ 2008-2011 Research Fellowship for Young Scientists by JSPS*, Stipend and Research Grant

■ 2006-2008 Research Fellowship for Young Scientists by JSPS*, Stipend and Research Grant

■ 2004-2006 Japan Student Services Organization, Stipend

■ 2002-2004 Johns Hopkins University, Dept. of Neuroscience (Solo Snyder Foundation), Tuition and Stipend

■ 2000-2002 Murata Overseas Scholarship, Tuition, Stipend, and Research Grant

* JSPS: Japan Society for the Promotion of Science

Letter

■ Letter of Appreciation from the RIKEN president Ryoji Noyori (2009/10)

Travel Award

■ 2006/12 The 21st Century COE Center for Diversity and Universality in Physics. NIPS2006 Travel Aid

■ 2006/5 Tamura Memorial Foundation, Frontiers in Dynamics: Physical and Biological Systems. Travel Aid

■ 2006/1 Japanese Neural Network Society. Travel Aid

Curriculum Vitae

Please click here for my full CV.