Hideaki Shimazaki, Ph.D
Associate Professor
Graduate School of Informatics
Kyoto University, 606-8501 Japan
Phone: +81-75-753-4906
E-Mail: h.shimazaki@i.kyoto-u.ac.jp
URL: http://www.neuralengine.org

Research agenda Humans and animals recognize the world and act on it for their survival. Our lab aims to provide
a unifying and testable theory on how nervous systems realize this remarkable capacity and propose new machine
intelligence grounded on it. While we broadly explore mathematical approaches to different levels of questions,
including (i) circuit mechanisms, (ii) nonlinear computation, and (iii) information coding, to seek principles ofinference, action selection, and learning, we put forward using canonical models commonly found in statistics,
physics, and AI in modeling and analyzing neural data, and further constructing intelligent agents based on them topromote a unified view on these topics.
Education
Doctor of Philosophy, Kyoto University

2004 Apr – 2007 Mar

Department of Physics, Graduate School of Science, Kyoto University, Kyoto, Japan.
Supervisor: Prof. Shigeru Shinomoto
Thesis title: Recipes for selecting the bin size of a histogram.
Master of Arts (Neuroscience), Johns Hopkins University

2000 Aug – 2003 Nov

Department of Neuroscience, School of Medicine, Baltimore, MD, USA.
Supervisor: Prof. Ernst Niebur
Thesis title: Communication over distributed neural systems.
Bachelor of Engineering, Keio University

1996 Apr – 2000 Mar

Department of Applied Physics and Physico-Informatics, Yokohama, Japan.
Supervisor: Prof. Yutaka Tomita, Prof. Takeshi Aihara, and Prof. Minoru Tsukada
Dissertation title: Identification of characteristic curve of synaptic modificationby spike timing: approach with optical recording method.
Research Experiences
Kyoto University, Associate Professor

2022 Sep – present

Hokkaido University, Specially-appointed Associate Professor

2020 Apr – 2022 Sep

Kyoto University, Project-specific Associate Professor

2017 Apr – 2020 Mar

Honda Research Institute Japan Co., Ltd., Senior Scientist

2016 Aug – 2020 Mar

RIKEN Brain Science Institute, Research Scientist

2011 Apr – 2016 Jul

Postdoctoral researcher at Taro Toyoizumi lab
Massachusetts Institute of Technology, Visiting Researcher

2009 Dec – 2011 Mar

Postdoctoral researcher at Emery N. Brown lab
RIKEN Brain Science Institute, Visiting Researcher
Postdoctoral researcher at Sonja Gruen lab

2007 Aug – 2011 Mar

Hideaki Shimazaki, Ph.D
JSPS* Research Fellow at RIKEN Brain Science Institute2008 Apr – 2011 Mar

JSPS* Research Fellow at Kyoto University

2006 Apr – 2008 Mar* Japan Society for the Promotion of Science
Teaching Experiences
Lectures* Lecture slides are available at http://www.neuralengine.org/res/lecture.
Hokkaido University, Sapporo, Japan

2020 Apr – 2022 Sep

A lecture course (90 min x 16 times) on theoretical neuroscience and machine intelligence titled “Introduction to thetheory of brains and machines”. Additionally, I give introductory lectures on neuroscience and machine learning
within the omnibus course offered by CHAIN, titled “Introduction to Interdisciplinary Studies on Humanity”.
Kyoto University, Kyoto, Japan2017 Apr – 2020 Mar

Spot lectures on computational neuroscience in “Computational and cognitive neuroscience” course for graduate
students, organized by Prof. Kumada and in “Proseminar on neuroinformatics” organized by Prof. Kamitani.
RIKEN Brain Science Institute, Saitama, Japan

2014 Dec – 2020 Feb

An introductory course on statistics titled “Statistics for Neuroscience” as a part of lecture courses of the RIKEN
Brain Science Training Program for selected graduate students. The course covers classical statistical tests andmultivariate analysis.
International Christian University, Tokyo, Japan

2014 Dec – 2017 Mar

An undergraduate course (140 min x 9 times) on “Statistical Physics” at the Department of Material Science of
the International Christian University. The course provides concepts in statistical physics including laws ofthermodynamics, state functions, and the canonical ensemble formalism.
Society for Young Researchers on Neuroscience, Kanagawa, Japan2017 MarAn invited lecturer of two-days workshop organized by Society for Young Researchers on Neuroscience (in Japan).
The course titled “Introduction to time-series modeling of neural activity data” offered basic analysis skills on
spiking neural data by point process models. The course included tutorials on generation, estimation, andvalidation of event data, using Julia language.
The Institute of Statistics and Mathematics, Tokyo, Japan

2016 Feb

A fee-charging extension course for businesspersons and scholars on “Introduction to statistical analysis ofpoint process networks” at the Institute of Statistics and Mathematics with Prof. Shinsuke Koyama. More than 72
people participated in this course.
Graduate School of Science and Technology at Meiji University, Tokyo, Japan

2013 May

Introductory courses of Neuroscience. Course titles: “Introduction to sensory systems, perception and neuralcoding studies” and “Introduction to neural decoding”.
The Institute for Research in Fundamental Sciences (IPM), Tehran, Iran2013 Mar

Invited two-week lecture course titled “Introduction to statistical models of neural spike train data” at School of
Cognitive Sciences, IPM in Iran. This course covered recent modeling approaches for neuroscientific data with an
introduction to a point process theory, generalized linear modeling, and adaptive filtering theory.
Supervision of postdoctoral researchers
Dr Ulises Rodriguez Dominguez (CMAT, Mexico)

2020 Sep – present

Bayesian modeling of the closed loop systems of the hippocampal formation and entorhinal cortex.
Dr Anuj Mishra (Pune University, India)

2020 Mar – present

Sequential Bayesian prediction by the projected nonlinear state-space model for large-scale data analysis.

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Hideaki Shimazaki, Ph.D
Dr Safura Rashid Shomali (IPM, Iran)2017 Dec – present

Identification of local circuitry of in-vivo neurons from higher-order statistics of population activity.
Dr Amin Sayed Moosavi (Kyoto University)

2018 Mar – 2020 Mar

Dr Amin is interested in emergence of critical phenomena from neural networks, and tries to elucidate its
information theoretic implications and the underlying mechanisms.
Dr Miguel Aguilera (University of the Basque Country, Spain)2018 Jul – 2018 Aug

Dr Aguilera is a visiting scholar to my lab at Kyoto University, and worked on mean-field approximation methods for
kinetic Ising systems.
Supervision of graduate students
Ken Ishihara (Hokkaido University)

2020 Oct – present

Large-scale analysis of neural data by the state-space kinetic Ising model.
Masanao Igarashi (Hokkaido University)

2020 Oct – 2022 Mar

Entropy production for discrete-time Markov processes.
Safura Rashid Shomali (IPM, Tehran)2013 Dec – 2017 Sep

Co-supervision of Ph.D. thesis with Prof. Majid Nili-Ahmadabadi. Ms. Shomali was a graduate student at IPM in
Tehran, Iran. I met her when I gave a lecture course at her institute in March 3-12, 2013. Later she asked for mysupervision, and I was appointed as a co-supervisor for her PhD thesis with Prof. Majid Nili-Ahmadabadi on Dec 4,
2013. She visited RIKEN during Jul 23- Sep 23 in 2014. I supervised her project on theoretical analysis of the
impact of a presynaptic input on a leaky integrate-and-fire neuron receiving noisy balanced inputs.
Christian Donner (BCCN Berlin)

2014 Jun – 2015 Apr

Superviser of a summer-intern project at RIKEN Brain Science Institute and co-superviser of his master thesis atBernstein Center for Computational Neuroscience (BCCN) Berlin with Prof. Klaus Obermayer. During 5/26-8/29 in2014, Mr. Donner stayed at RIKEN BSI to study the dynamic model of neural interactions, as one of three labrotation projects required for his Master’s degree at BCCN Berlin. During his stay, he introduced an analytical
approximation method to make the model applicable to large-scale analysis.

Supervision of undergraduate students (Internship students)
Bingyue Zhu (University of British Columbia, Canada)2018 Jun 20 – 2019 Apr 26

Sequential Bayes estimation and anomaly detection method for physiological time-series signals
Magalie Tatischeff (University of British Columbia, Canada)2018 Jun 20 – 2019 Apr 26

Data analysis and development of statistical models for spiking activity of neural populations
Shukla Shashwat (IIT Bombay, India)

2018 May 14 – 2018 Jul 13

Investigation of advanced time-series models for neural and behavioral activities
Krishna Subramani (IIT Bombay, India)

2018 May 14 – 2018 Jul 13

Learning a hierarchical model for spatial phases in natural scenes.
Brinti Datta (IIT Bombay, India)

2017 Jul 5 – 2017 Jul 14

Filtering methods for time-series analysis
Jimmy Gaudreault (Polytechnique Montreal)

2017 May 15 – 2018 Apr 27

Data analysis of neural population activities by the dynamic Ising model
Arunabh Saxena (IIT Bombay, India)

2017 May 8 – 2017 July 14

Studying and extending the state-space analysis methods for neural populations

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Hideaki Shimazaki, Ph.D
Publications
Journal*IF, Citation # as of Apr 2021

1. Aguilera M, Igarashi M, Shimazaki H. Nonequilibrium thermodynamics of the asymmetric SherringtonKirkpatrick model. Nature Communications (2023) 10.1038/s41467-023-39107-y
2. Shomali SR, Ahmadabadi MN, Rasuli SN, Shimazaki H. Uncovering hidden network architecture fromspiking activities using an exact statistical input-output relation of neurons. Communications biology(2023) 6, 169 s42003-023-04511-z
3. Tatsumi K, Inamura Y, Kofu M, Kiyanagi R, & Shimazaki H. Optimization and inference of bin widths forhistogramming inelastic neutron scattering spectra. Journal of Applied Crystallography (2022) 55(3)533-543.
4. Isomura T, Shimazaki H, Friston K. Canonical neural networks perform active inference.
Communications biology (2022) 5(1), 1-15
5. Torigoe M, Islam T, Kakinuma H, Fung CCA, Isomura T, Shimazaki H, Aoki T, Fukai T, Okamoto H.
Zebrafish capable of generating future state prediction error show improved active avoidance behavior in
virtual reality. Nature Communications (2021) 12, 5712
6. Aguilera M, Moosavi SA, Shimazaki H. A unifying framework for mean field theories of asymmetric kinetic
Ising systems. Nature Communications (2021) 12, 1197
7. Nakajo H, Chou C, Kinoshita M, Appelbaum L, Shimazaki H, Tsuboi T, Okamoto H. 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
8. Shomali SR, Ahmadabadi MN, Shimazaki H, Rasuli SN. How does transient signaling input affect the
spike timing of postsynaptic neuron near the threshold regime: an analytical study. Journal ofComputational Neuroscience (2018) 44(2), 147-171
9. 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, SheaBrown E, Shimazaki H, Shinomoto S, Yu BM, Kramer MA. Computational Neuroscience: Mathematicaland statistical perspectives. Annual Review of Statistics and Its Application (2018) 5:8.1–8.32
10. 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 memorytasks of varied complexities. PLoS Computational Biology (2017) 13(6): e1005551.
11. Donner C, Obermeyer K, Shimazaki H. Approximate inference for time-varying interactions andmacroscopic dynamics of neural populations. PLoS Computational Biology (2017) 13(1): e1005309
12. 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, andShinomoto S. Similarity in neuronal firing regimes across mammalian species. Journal of Neuroscience(2016) 36:5736-5747.
13. Chou MY, Amo R, Kinoshita M, Cherng BW, Shimazaki H, Agetsuma M, Shiraki T, Aoki T, Takahoko M,
Yamazaki M, Higashijima S, and Okamoto H. Social conflict resolution regulated by two dorsal habenularsubregions in zebrafish. Science (2016) 352(6281) 87-90
14. MaBouDi H, Shimazaki H, Amari S, Soltanian-Zadeh H, Representation of higher-order statisticalstructures in natural scenes via spatial phase distributions. Vision Research (2016) 120, 61-73

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Hideaki Shimazaki, Ph.D
15. 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.
16. Shimazaki H, Amari S, Brown EN, and Gruen S, State-space analysis of time-varying higher-order spikecorrelation for multiple neural spike train data. PLOS Computational Biology (2012) 8(3): e1002385.
17. Shimazaki H and Shinomoto S. Kernel bandwidth optimization in spike rate estimation. Journal ofComputational Neuroscience (2010) 29 (1-2) 171-182 [384 citations in Google Scholar]
18. Shimazaki H and Shinomoto S. A method for selecting the bin size of a time histogram. NeuralComputation (2007) 19(6), 1503-1527. [606 citations in Google Scholar]
19. Shimazaki H and Niebur E. Phase transitions in multiplicative competitive processes. Physical Review E(2005) 72(1), 011912.
20. Tsukada M, Aihara T, Kobayashi Y, and 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.
Refereed conference proceedings
21. 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. accepted
22. Gaudreault J, Saxena A, Shimazaki H. Online estimation of multiple dynamic graphs in pattern sequences.
2019 International Joint Conference on Neural Networks (IJCNN2019). 10.1109/IJCNN.2019.8852467
BEST STUDENT AWARD
23. Gaudreault J and Shimazaki H. State-space analysis of an Ising model reveals contributions of pairwiseinteractions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons. The 27th International
Conference on Artificial Neural Networks (ICANN2018), Lecture Notes in Computer Science 11141, 641–
651, 201824. Donner C and Shimazaki H. Approximate inference method for dynamic interactions in larger neural
populations. The 23rd International Conference on Neural Information Processing (ICONIP2016), Lecture
Notes in Computer Science 9949, 104–110, 2016 EXCELLENT PAPER AWARD
25. Shimazaki H Single-trial estimation of stimulus and spike-history effects on time-varying ensemble spiking
activity of multiple neurons: a simulation study. ELC International Meeting on ''Inference, Computation, andSpin Glasses'' (ICSG2013). Journal of Physics: Conference Series, 473 012009, 2013.
26. Shimazaki H, Amari S, Brown EN, and Gruen S. State-space analysis on time-varying correlations inparallel spike sequences. The 34th International Conference on Acoustics, Speech, and Signal Processing(ICASSP2009). Proc. IEEE ICASSP, 3501-3504, 2009.
27. Shimazaki H and Shinomoto S. A recipe for optimizing a time-histogram. Neural Information Processing
Systems (NeurIPS2006). Advances in NeurIPS, Vol. 19, 1289-1296, 2007. SPOTLIGHT POSTER
28. Shimazaki H and Niebur E. Correlated multiplicative modulation in coupled oscillator systems: a model of
selective attention. Progress of Theoretical Physics Supplement, No.161 336-339, 2006.
Review papers
29. (In Japanese) Shimazaki H. Bayesian and thermodynamic view on dynamics of learning and recognition inorganisms. The brain & neural networks (2019) 26(3) 72-98
30. (In Japanese) Shimazaki H. The principles of adaptation in recognition and behavior. The brain & neuralnetworks (2018) 25(3) 86-103

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Hideaki Shimazaki, Ph.D
31. (In Japanese) Shimazaki H. Analysis of multiple neural spike train data using the log-linear model: Fromstationary to time-varying spike correlation. The brain & neural networks, 8(4) 194-203, 2011
Preprints
32. Shimazaki H. The principles of adaptation in organisms and machines II: Thermodynamics of theBayesian brain. (2020) arXiv:2006.13158
33. Shukla S, Shimazaki H, Ganguly U. Structured mean-field variational inference and learning in winnertake-all spiking neural networks. (2019) arXiv:1911.05943
34. MaBouDi H, Shimazaki H, Soltanian-Zadeh H, Amari S. Learning Complex Representations from Spatial
Phase Statistics of Natural Scenes. bioRxiv. (2019) https://doi.org/10.1101/112813 under revision
35. Shimazaki H. The principles of adaptation in organisms and machines I: machine learning, information
theory, and thermodynamics. (2019) arXiv:1902.11233
Book Chapter
36. Shimazaki H. Neural Engine Hypothesis. p267-291 In: Chen Z., Sarma S. (eds) Dynamic Neuroscience:
Statistics, Modeling, and Control. (2018) Springer, Cham (This is an extended manuscript of “Neurons as
an Information-theoretic Engine” arXiv:1512.07855, 2015)37. Shimazaki H. Japanese translation of Principles of Neural Science 5th ed. Kandel E.R. et al. editors,
Appendix F: Theoretical Approaches to Neuroscience: Examples from Single Neurons to Networks by
Abbott L.F., Fusi S., and Miller K.D., p1567-1583, Medical Science International Inc., 2014. Original:p1601-1618, McGraw-Hill, 2012.
Awards
1. Excellent Paper Award at The 23rd International Conference on
Neural Information Processing (ICONIP2016)2016 Oct

2. Letter of Appreciation from the RIKEN president

2009 Oct

3. Japanese Neural Network Society Research Award 20092009 Sep 26

4. Japanese Neural Network Society Young Researcher Award 2007

2007 Nov 15

Awards to supervised students
5. IJCNN 2019 Best Student Award: Jimmy Gaudreault
6. ICONIP 2016 Excellent Paper Award: Christian Donner
Research Grants and Fellowships
1. 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
2. New Energy and Industrial Technology Development Organization (NEDO): Human-centered Artificial
Intelligence Embedded in the Real World
2020 Apr – 2025 Mar
3. 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
4. Japanese Neural Network Society 30th anniversary grant2020 Apr – 2021 Mar

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

6Hideaki Shimazaki, Ph.D

6. The grant of Joint Research by the National Institutes of Natural Sciences: Tutorial workshop for the freeenergy principle.
2019 Apr – 2020 Mar
7. The Cooperative Study Program of National Institute for Physiological Sciences 2019 Apr – 2020 Mar
8. Japanese Neural Network Society workshop grant2018 Apr – 2019 Mar

9. Excellent Young Researchers Overseas Visit Program by JSPS

2009 Dec – 2011 Mar

10. Research Fellowship for Young Scientists by JSPS PD

2008 Apr – 2011 Mar

11. Research Fellowship for Young Scientists by JSPS DC
12. Japan Student Services Organization (Fellowship)2006 Apr – 2008 Mar
2004 – 2006

13. Solo Snyder Foundation, Johns Hopkins University (Fellowship)

2002 – 2004

14. Murata Overseas Scholarship (Fellowship and Research Grant)

2000 – 2002

Academic service
1. Organisers, International Workshop on Active Inference

2022 Feb – present

2. Action editor, Neural Networks

2020 Jan – present

3. Board member, Japanese Neural Network Society

2018 Mar – 2020 Mar

Selected invited talks
1. Vision Society of Japan: Special session of ‘Front of computational approaches to vision and brain science’.
Jan 21, 2021. Online. Title: Introduction to theoretical approaches to the brain. (in Japanese)
2. Combining Information theoretic Perspectives on Agency. Jan 28-29, 2020. The University of Tokyo, Japan.
Title: The brain as an information-theoretic engine: A new paradigm for quantifying perceptual capacity ofneural dynamics.
3. Data Science, Statistics, & Visualisation (DSSV2019) Aug. 13. Doshisha University, Kyoto, Japan. Title:
Visualizing dynamics of cooperative activities of neurons for neural coding studies.
4. Brain Science Workshop of Tamagawa Unviersity, Japan. Sep 28, 2017. Host Prof. Yutaka Sakai and Prof
Hideo Komatsu. Title: Mathematical and statistical approaches to neural population activity
5. Queen Mary University of London, UK. Jul 4, 2016. Host Prof. Lars Chittka. Title: Population coding of
neurons: Dynamics, higher-order interactions, and mechanisms.
6. Bernstein Center of Computational Neuroscience Berlin, Berlin, Germany. Apr 2015. Host Prof. Klaus
Obermayer. Title: Simultaneous silence explains structured higher-order interactions of neural populations.
7. ELC International Meeting on ''Inference, Computation, and Spin Glasses'' (ICSG2013), Sapporo, Japan.
Jul 2013. Title: State-space analysis of time-varying higher-order interactions: its applications toneuroscience.
8. The 3rd Mathematical Neuroscience Workshop in School of Mathematics, Institute for Research inFundamental Sciences (IPM). Tehran, Iran. Mar 2013. Title: The simultaneous silence of neurons explains
higher-order interactions in ensemble spiking activity.
9. Neurostatistics Working Group Seminar, Dept. of Biostatistics, Harvard University, Dec 2010. Title:
Analysis of dynamic neural spike data: from firing rates to spike correlations.

7Hideaki Shimazaki, Ph.D
10. IEEE ICASSP2009, Special Session on `Signal Processing for Neural Spike Trains', Taipei, Taiwan. Apr
2009. Title: State-space analysis on time-varying correlations in parallel spike sequences.
11. NIPS 2008, Workshop on `Statistical Analysis and Modeling of Response Dependencies in Neural
Populations', Whistler, Canada. Dec 2008. Title: State-space analysis on time-varying higher-order spikecorrelations.
12. RIKEN Brain Science Institute Forums, Wako, Japan. Host: Dr. Sonja Gruen. Apr 2007. Title: A recipe for
optimizing a time histogram of spike data.
13. The Boadian Seminar at Mind/Brain Institute, Johns Hopkins University. Baltimore, USA. Host: Dr. Ernst
Niebur. Mar 2007. Title: A recipe for constructing a peri-stimulus time histogram.
List of all presentations■ 2022
1. Hideaki Shimazaki. Consciousness and the thermodynamics of the Bayesian brain. International
Symposium on Artificial Intelligence and Brain Science 2022. OIST, Japan. Jul 5, 2022 INVITED TALK
2. Ken Ishihara, Hideaki Shimazaki. The state-space kinetic Ising model for nonequilibrium neuronal
dynamics. NEURO2022. Okinawa, Japan. Jul 2, 2022 POSTER
3. 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. Okinawa, Japan. Jun 30, 2022 INVITED TALK
4. Rodriguez Dominguez U and Shimazaki H. Bayesian computation of generic neural binary code by local
competition. The 44th Annual Meeting of the Japan Neuroscience Society (Neuro 2021), July 31, 2021.
Online. POSTER■ 2021
5. Aguilera M, Moosavi SA, Shimazaki H. 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
6. Shimazaki H. Revealing neuronal population activity by non-stationary and non-equilibrium Ising models.
Data-driven biology workshop, Kyoto University, Japan. Mar 23, 2021. Online. INVITED TALK (Japanese)
7. Shomali S.R., Rasuli S.N., Shimazaki H. Revealing hidden microcircuits using higher-order interactions ofneuronal activity. The 3rd Sharif Neuroscience Symposium Mar 4, 2021. Online. ORAL PRESENTATION
8. Shimazaki H. Introduction to theoretical approaches to the brain. Vision Society of Japan. Online. Jan 21,
2021. INVITED LECTURE (Japanese)■ 20209. Shomali S.R., Rasuli S.N., Shimazaki H. Higher-order interactions induced by strong shared inputs. 29th
Annual Computational Neuroscience Meeting: CNS*2020. Dec 21 POSTER
10. Moosavi S.A., Tatischeff M., Zhu B., Shimazaki H. Effects of structured neural correlations in population
coding: beneficial or detrimental? Computational and Systems Neuroscience (Cosyne) 2020. Dember,USA. Feb 27 - Mar 1. POSTER
11. Shomali S.R., Ahmadabadi M.N., Rasuli S.N., Shimazaki H. Inferring network motifs from neural activity
using analytic input-output relation of LIF neurons.Computational and Systems Neuroscience (Cosyne)2020. Dember, USA. Feb 27 - Mar 1. POSTER
12. Shimazaki H. Thermodynamics of the Bayesian brain: A new paradigm for quantifying perceptual capacityof neural dynamics. Computational Principles in Active Perception and Reinforcement Learning in the Brain.
Kyoto University, Japan. Feb 13-14, 2020. INVITED TALK

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Hideaki Shimazaki, Ph.D
13. Shimazaki H. The brain as an information-theoretic engine: A new paradigm for quantifying perceptual
capacity of neural dynamics. Combining Information theoretic Perspectives on Agency. The University ofTokyo, Japan. Jan 28-29, 2020. INVITED TALK
■ 2019
14. Shimazaki H. Dynamic neural interactions revealed by the state-space Ising model. The 7th International
Congress on Cognitive Neurodynamics. Alghero, Italy. Sep 29, 2019. TALK
15. Shomali S.R., Ahmadabadi M.N., Rasuli S.N., Shimazaki H. Judging between Excitation and Inhibition:
Identifying Local Network Architecture by an Analytic Pre-Post Relation. Bernstein Conference 2019. Berlin,
Germany. Sep 18, 2019. POSTER
16. Shimazaki H. Visualizing dynamics of cooperative activities of neurons for neural coding studies. Data
Science, Statistics, & Visualisation (DSSV2019) Doshisha University, Kyoto, Japan. Aug. 13, 2019 TALK
17. Gaudreault J., Saxena A., Shimazaki H. Online estimation of multiple dynamic graphs in pattern
sequences. The 2019 International Joint Conference on Neural Networks (IJCNN2019), Budapest,
Hungary. Jul 14 2019 TALK BEST STUDENT AWARD
18. Shimazaki H. Network architecture underlying sparse neural activity characterized by structured higherorder interactions. Okinawa Institute of Technology (OIST), Okinawa, Japan. Mar 26 2019 TALK
19. Moosavi S.A., Shimazaki H. Role of gain-control and neural correlations in efficient stimulus coding.
Consciousness Research Network (CoRN2019), Okazaki, Japan. Feb 23 2019 POSTER
20. Tatischeff M., Gaudreault J., Donner C., Shimazaki H.. Thermodynamic analysis of neural populations by
the state-space Ising model. Consciousness Research Network (CoRN2019), Okazaki, Japan. Feb
23 2019 POSTER■ 2018
21. Shimazaki H., Tatischeff M., Gaudreault J., Donner C. 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
22. Gaudreault J. and Shimazaki H. State-space analysis of an Ising model reveals contributions of pairwiseinteractions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons. The 27th International
Conference on Artificial Neural Networks (ICANN2018), Rhodes, Greek. Oct 5, 2018 POSTER
23. Shimazaki H. Neural network architecture that produces sparse population activity. Workshop on multitrack
event-trains in neural, social, seismological, and financial data. Karuizawa, Japan. July 7, 2018. TALK (InJapanese)
24. Gaudreault J, Saxena A, Shimazaki H. State-space analys of an Ising model reveals contributions ofpairwise interactions to sparseness, fluctuation, and stimulus coding of monkey V1 neurons. Mechanism ofBrain and Mind, Rusutsu, Hokkaido, Japan. Jan 9, 2018 POSTER■ 2017
25. Shimazaki H. Network architectures underlying variable, sparse population activity of neurons. Fluctuationsof event occurrences in a variety of networks. Kyoto, Japan. Nov 15, 2017 TALK
26. Shimazaki H. Mathematical and statistical approaches to neural population activity. Brain Science
Workshop of Tamagawa University. Isawa-onsen, Yamanashi, Japan. Sep 28, 2017. INVITED TALK (InJapanese)
27. 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 theJapan Neuroscience Society (Neuro2017). Makuhari Messe, Japan. Jul 23 2017. INVITED TALK
■ 2016
28. Donner C. and Shimazaki H., Estimating dynamic functional networks of larger neural populations. Society
for Neuroscience 2016. Nov 16, 2016

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Hideaki Shimazaki, Ph.D

29. Shomali S.R., Ahmadabadi M.N., Rasuli N. S., Shimazaki H., Exact analysis of spike-timing andhigher-order interactions of neurons at the threshold regime suggests network architecture underlying
sparse population activity. Society for Neuroscience 2016. Nov 14, 2016
30. 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
31. MaBouDi H., Shimazaki H., Lars Chittka L. Modelling elemental learning of honeybees by spiking neuralnetworks. EURBEE 2016. Cluj-Napoca, Romania. Sep 7, 2016 (Talk by MaBouDi)
32. Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations.
Modeling Neural Activity: Statistics, Dynamical Systems, and Networks, Waikoloa, Hawaii, USA. Jun 22-24
2016, TALK
33. Shimazaki H. Toward thermodynamic principles of consciousness. University of Sussex, UK. Jul 7, 2016.
INVITED TALK
34. Shimazaki H. Higher-order interactions of neural populations. University College London, UK. Jul 5, 2016.
TALK
35. Shimazaki H. Population coding of neurons: Dynamics, higher-order interactions, and mechanisms. Queen
Mary University of London, UK. Jul 4, 2016. INVITED TALK
36. Shomali S.R., Ahmadabad M.N., Shimazaki H., Rasuli SN. Exact spike-timing distribution reveals higherorder interactions. CNS*2016. Jeju, South Korea. Jul 2, 2016 (Poster by Shomali) POSTER (Reviewed)
37. Shimazaki H. Analysis of network activity of neurons by the dynamic Ising model. International Christian
University, Tokyo, Japan. Feb 2, 2016 INVITED TALK
38. Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural population.
MONA2 - Modelling Neural Activity. Hawaii, USA. Jun 22, 2016 TALK
39. Shimazaki H., Peters A.J., Komiyama T., Toyoizumi T. Redundant coding by layer 2/3 neurons of motor
cortex during initial motor learning. Mechanism of Brain and Mind, Rusutsu, Hokkaido, Japan. Jan 6-8,
2016 POSTER■ 2015
40. Donner C. and Shimazaki H., Approximation methods for inferring time-varying interactions of a large
neural population. Workshop in Statistical Methods for Understanding Neural Systems at Neural
Information Processing Systems (NIPS) 2015. Montreal Canada, Dec 11, 2015 POSTER (Reviewed)
41. Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations.
Juelich Research Center. Juelich, Germany. May 5, 2015 INVITED TALK
42. Shimazaki H. Simultaneous silence explains structured higher-order interactions of neural populations.
Bernstein Center of Computational Neuroscience Berlin. Berlin, Germany. Apr 28, 2015 TALK
■ 2014
43. Sharp T., Shimazaki H., Isomura Y., Fukai T. 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
44. Sharp T., Shimazaki H., Isomura Y., Fukai T. Behaviour- and layer-dependent synchrony in motor cortex
during volitional arm movement. Neuro2014, Yokohama, Japan. Sep 11, 2014. POSTER
45. Shomali S.R., Ahmadabadi M.N., Shimazaki H., Rasuli N. S. Theoretical study on spike-timing probability
in a pair of pre-post synaptic neurons. Neuro2014, Yokohama, Japan. Sep 11,2014. POSTER

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Hideaki Shimazaki, Ph.D
46. MaBouDi H., Shimazaki H., Abouzari M., Soltanian-Zadeh H., Amari S. Bimodal distributions of local phasevariables in natural images. The 2014 Vision Sciences Society (VSS) Annual Meeting, St. Pete Beach,
Florida, USA. May 18, 2014 POSTER (Reviewed)
47. Sharp T., Shimazaki H., Isomura Y., Fukai T. 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
48. MaBouDi H., Shimazaki H., Abouzari M., Amari S, Soltanian-Zadeh H. Statistical inference for directed
phase coupling in neural oscillators. Computational and Systems Neuroscience (Cosyne) 2014. Salt Lake
City, USA. Feb 27 POSTER (Reviewed)
49. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. Structured higher-order interactions explain thesimultaneous silence of neural populations. Mechanism of Brain and Mind, Rusutsu, Hokkaido, Japan. Jan
8-10, 2014 POSTER■ 201350. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T., The simultaneous silence of neurons explains
structured higher-order interactions in ensemble spiking activity. Society for Neuroscience (SfN). San
Diego, USA. Nov 9-13 POSTER
51. Shimazaki H., State-space Analysis of Time-varying Higher-order Interactions: its Applications to
Neuroscience. ELC International Meeting on ''Inference, Computation, and Spin Glasses'' (ICSG2013).
Sapporo, Japan. Jul 28 INVITED TALK
52. Shimazaki H., Higher-order interactions in population activity of hippocampal CA3 neurons. Workshop on
statistical analysis of neurophysiological and clinical data, Kyoto, Japan. Jul 8-9 TALK
53. 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 Jun 2628 TALK
54. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T., The simultaneous silence of neurons explains higherorder interactions in ensemble spiking activity. Neuro2013, Kyoto, Japan. Jun 20 POSTER
55. 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 INVITED TALK
56. 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 14 INVITED TALK
57. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. The simultaneous silence of neurons explains
structured higher-order interactions in spontaneous spiking activity. Dialog between Neuroscience andStatistics, Institute of Mathematics and Statistics, Tokyo, Japan. Feb 18-19 TALK
■ 2012
58. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. Joint inactivation statistics of population spiking
activities. RIKEN BSI Retreat 2012, Karuizawa, Japan. Nov 12-13 POSTER
59. Shimazaki H. The simultaneous silence of neurons explains structured higher-order interactions inensemble spiking activity. RIKEN BSI Lunch Seminar 2012, Wako, Japan. Nov 8 TALK
60. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. Joint inactivation statistics of population spiking
activities. Workshop on statistical aspects of neural coding. Nov 1-2. Kyoto University & Ritsumeikan
University. TALK
61. Shimazaki H. Tracking Dynamic Neural Interactions in Awake Behaving Animals. Workshop on neural
information flow, Kyoto University, Kyoto, Japan. Jun 20 INVITED TALK

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Hideaki Shimazaki, Ph.D
62. Shimazaki H., Sadeghi K., Ikegaya Y., Toyoizumi T. The simultaneous silence of neurons explains higherorder interactions in ensemble spiking activity. Computational and Systems Neuroscience (Cosyne) 2012.
Salt Lake City, USA. Feb 23-26 POSTER (Reviewed)■ 2011
63. 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
64. Shimazaki H., Ikegaya Y., and Toyoizumi T. A New Sparse Log-linear Model for Simultaneously Active andInactive Neurons. RIKEN BSI Retreat, Karuizawa, Japan. Oct 31 POSTER
65. 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
66. Shimazaki H. and Brown E. N. Constructing a joint time-series model of continuous and Bernoulli/Poissonprocesses using a copula. Computational and Systems Neuroscience (Cosyne) 2011, Salt Lake City, USA.
Feb 24-27 POSTER (Reviewed)■ 201067. Shimazaki H. Analysis of Dynamic Neural Spike Data: From Firing Rates to Spike Correlations.
Neurostatistics Working Group Seminar, Dept. of Biostatistics, Harvard University, Boston, USA. Dec 1
INVITED TALK
68. Shimazaki H. Detection of dynamic cell assemblies by the Bayes Factor. Workshop on spatio-temporal
neuronal computation, Kyoto University, Kyoto, Japan. Sep 6-7 INVITED TALK
69. Shinomoto S*, Shimazaki H, and Shimokawa T. Characterizing neuronal firing with the rate and theirregularity. Neuro2010, Kobe, Japan. Sep 2 S3-10-1-3 TALK
70. Shimazaki H., Gruen S., and Amari S. Analysis of subsets of higher-order correlated neurons based on
marginal correlation coordinates. Computational and Systems Neuroscience (Cosyne) 2010, Salt Lake City,
USA. Feb 25-28 POSTER (Reviewed)■ 200971. 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
72. 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
73. Shimazaki H., Amari S., Brown E. N., and Gruen S. Bayes Factor Analysis for Detection of Timedependent Higher-order Spike Correlations. CNS2009. Berlin, Germany. Jul 18-23 P99 POSTER
74. Shimazaki H. and Shinomoto S. Histogram binwidth and kernel bandwidth selection for the Spike-rate
estimation. CNS2009. Berlin, Germany. Jul 18-23 P116 POSTER
75. 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,
Germany. May 25-28 POSTER
76. 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 INVITED LECTURE
77. Shimazaki H., Amari S., Brown E. N., and Gruen S. Detection of non-stationary higher-order spikecorrelation. Cosyne 2009, Salt Lake City, USA. Feb 26 -Mar 3 II-62 POSTER (Reviewed)■ 200812

Hideaki Shimazaki, Ph.D
78. 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 INVITED TALK
79. Shimazaki H. and Gruen S. Selecting a state-space model of higher-order correlations in parallel spiketrains from competing hierarchical log-linear models. RIKEN BSI Retreat 2008, Karuizawa, Japan. Nov 4-5
POSTER
80. Shimazaki H. and Shinomoto S. Spike-rate Estimation with Locally Adaptive Kernel Method. Japanese
Neural Network Society 2008, Tsukuba, Japan (In Japanese). Sep 24-26 PS3-4 SPOTLIGHT POSTER
81. Shimazaki H. and Gruen S. Estimating time-dependent higher-order interactions in parallel spike trains.
Neuro2008, Tokyo, Japan. Jul 9-11 POSTER
82. 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■ 2007
83. 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
84. Shimazaki H. and Shinomoto S. Kernel Width Optimization in the Spike-rate Estimation. Neural Coding
2007, Montevideo, Uruguay. Nov 7 POSTER BEST POSTER AWARD
85. Shimazaki H. and Shinomoto S. Optimization of a Histogram of Spike Data. Neuro2007, Yokohama, Japan.
Sep 11 P2-k22 POSTER
86. 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 INVITED TALK
87. Shimazaki H. A recipe for constructing a Peri-stimulus Time Histogram. The Boadian Seminar at
Mind/Brain Institute. Johns Hopkins University, Baltimore, USA. Host: Ernst Niebur. Mar 1 INVITED TALK
88. Shimazaki H. and Shinomoto S. A recipe for optimizing a time-histogram with variable bin sizes.
Computational and Systems Neuroscience 2007, Salt Lake City, USA. Feb 22-27 POSTER (Reviewed)■ 2006
89. Shimazaki H. and Shinomoto, S. A recipe for optimizing a time-histogram. Neural Information Processing
Systems, Whistler, B. C. Dec 4-9 POSTER (Reviewed) SPOTLIGHT POSTER
90. Shimazaki H. A method to optimize a time histogram by correcting onset latencies of spike sequences
Japanese Neural Network Society 2006, Nagoya, Japan. Sep 1-2 (In Japanese) O4-1 TALK＋POSTER
Japanese Neural Network Society 2007 Young Researcher Award
91. Shimazaki H. Self-organized criticality by natural selection. Frontiers in Dynamics: Physical and Biological
Systems Tokyo, Japan. May 22-24, 2006, POSTER
92. Shimazaki H. and Shinomoto S. Recipes for constructing an optimal time histogram. Statistical Analysis of
Neuronal Data (SAND3) Pittsburgh, PA, USA. May 12-13, 2006, POSTER■ 2005
93. Shimazaki H. and Shinomoto S. A recipe for making a time histogram with an optimal bin width from spikesequences. Japanese Neural Network Society 2005, Kagoshima, Japan (In Japanese) SPOTLIGHT
POSTER

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