Directory of Researchers
| OMORI Toshiaki |  |
| Graduate School of Engineering / Department of Electrical and Electronic Engineering | |
| Associate Professor | |
| Electro-Communication Engineering |
Researcher Profile and Settings
Affiliation
＜Faculty / Graduate School / Others＞
Graduate School of Engineering / Department of Electrical and Electronic Engineering＜Related Faculty / Graduate School / Others＞
Faculty of Engineering / Department of Electrical and Electronic Engineering, Center for Mathematical and Data Sciences, Center of Optical Scattering Image Science
Teaching
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, First Year Seminar
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory ⅡA
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Design of Electric Systems and Equipments A
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electricity Act
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory Ⅳ
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Graduate Research
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory ⅡA
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Research Works
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory Ⅱ
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory Ⅱ
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Research Works
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Introductory Seminar of Electrical and Electronics Engineering
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory ⅡB
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory ⅡB
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Design of Electric Systems and Equipments B
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electromagnetic Wave Theory A
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Integrated Circuit Engineering A
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Applied Communication Engineering A
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Control Engineering I
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Introduction to Research in Electrical and Electronic Engineering
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Applied Communication Engineering
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electrical and Electronics Engineering Laboratory Ⅲ
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electromagnetic Wave Theory
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Integrated Circuit Engineering
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Electromagnetic Wave Theory B
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Integrated Circuit Engineering B
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Applied Communication Engineering B
- Faculty of Engineering / Department of Electrical and Electronic Engineering, 2022, Control Engineering Ⅱ
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Special Lecture ⅠＡ
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Master's Thesis
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Independent study
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Advanced Vacuum Engineerin
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Special Lecture ⅠＢ
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Learning and Inference
- Graduate School of Engineering / Department of Electrical and Electronic Engineering, 2022, Advanced Science and Technology A
- Faculty of Engineering, 2022, Advanced Liberal Arts Seminar in Faculty of Engineering (Current Research Topics of Electrical and Electronic Engineering)
- Faculty of Engineering, 2022, Advanced Liberal Arts Seminar in Faculty of Engineering (Current Research Topics of Electrical and Electronic Engineering)
- Faculty of Engineering, 2022, Advanced Liberal Arts Seminar in Faculty of Engineering (Electrical and Electronic Engineering)
Research Activities
Profile and Settings
Profile
Toshiaki Omori received his Ph.D. degree in Information Science from Tohoku University in 2004. He was a predoctoral research fellow of Japan Society of the Promotion of Science (JSPS) from 2003 to 2004, a postdoctoral researcher at Japan Science and Technology Agency from 2004 to 2006, and a postdoctoral research fellow of JSPS from 2006 to 2008. He was a visiting researcher at University of Arizona, U.S.A. in 2007. He became a research assistant professor and an assistant professor at the University of Tokyo in 2008. He is currently an associate professor at the Graduate School of Engineering, Kobe University. His research interests include machine learning theory and its applications, probabilistic information processing, and computational neuroscience.
Research Interests
- 知的学習論
- スパースモデリング
- 確率的時系列解析
- イメージングデータ
- データ駆動科学
- 動的システム推定
- 電気回路モデル
- 脳情報学
- 知能情報学
- 応用数理
- 計算統計学
- 情報科学
- 確率的情報処理
- 数理脳科学
- ベイズ統計
- 計算脳科学
- 樹状突起
- 局所回路
- 海馬
- 数理モデル
- 包括脳ネットワーク
- ヘテロ複雑システム
- ニューラルネットワーク
Research Areas
- Informatics / Intelligent informatics
- Informatics / Soft computing
- Informatics / Sensitivity (kansei) informatics
- Informatics / Biological, health, and medical informatics
- Manufacturing technology (mechanical, electrical/electronic, chemical engineering) / Control and systems engineering
- Natural sciences / Mathematical physics and basic theory
- Life sciences / Neuroscience - general
Awards
Oct. 2022 国立大学法人神戸大学 令和4年度学長表彰
Nov. 2018 第40回日本比較生理生化学会 発表賞会長賞
Oct. 2018 第52回日本味と匂学会 優秀発表賞
Oct. 2017 Best Paper Award, 18th International Symposium on Advanced Intelligent Systems
Sep. 2014 情報科学技術フォーラム FIT奨励賞
May 2014 Achievement Award [Electrical Engineering], The Japan Electric Association Kansai Branch
Apr. 2012 RIKEN Appreciation Letter from President
Jun. 2011 情報処理学会 論文賞
Nov. 2010 独立行政法人理化学研究所 理事長感謝状
Sep. 2010 計測自動制御学会 生体・生理工学部会研究奨励賞
Published Papers
- 2023, Scientific Reports, in press., EnglishSuper-resolution of X-ray CT Images of Rock Samples by Sparse Representation: Applications to the Complex Texture of Serpentinite
[Refereed]
Scientific journal
- 2022, EntropyEstimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis-Hastings Method
[Refereed]
- Mar. 2021, Proceedings of the 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, 103 - 105Online Bayesian Approach for Estimation and Control of Neural System
[Refereed]
- 2021, Journal of the Physical Society of JapanEstimation of Neuronal Dynamics of Izhikevich Neuron Models from Spike-Train Data with Particle Markov Chain Monte Carlo Method
[Refereed]
- 2021, J. Image Graph., in press.Video Frame Rate Up-Conversion via Spatio-Temporal Generative Adversarial Networks
[Refereed]
- 2021, Entropy, in press.Data-driven Analysis of Nonlinear Heterogeneous Reactions through Sparse Modeling and Bayesian Statistical Approaches
[Refereed]
Scientific journal
- Last, 2021, European Physical Journal B, in press.Exploration of Nonlinear Parallel Heterogeneous Reaction Pathways through Bayesian Variable Selection
[Refereed]
Scientific journal
- Oct. 2020, Journal of the Physical Society of JapanReplica Exchange Particle Gibbs Method with Ancestor Sampling
[Refereed]
Scientific journal
- Sep. 2020, International Journal of Machine Learning and ComputingSwitching Probabilistic Slow Feature Analysis for Time-series Data
[Refereed]
Scientific journal
- Mar. 2020, Proceedings of 2020 IEEE 2nd Global Conference on Life Sciences and Technologies, 187 - 188Sparse Modeling Approach for Estimating Odor Pleasantness from Multi-dimensional Sensor Data
[Refereed]
International conference proceedings
- Dec. 2019, Communications in Computer and Information Science, 1143, 600 - 608Online Estimation and Control of Neuronal Nonlinear Dynamics Based on Data-Driven Statistical Approach
[Refereed]
International conference proceedings
- Dec. 2019, Communications in Computer and Information Science, 1143, 609 - 618Sparse Estimation of Neuronal Network Structure with Observed Data
[Refereed]
International conference proceedings
© 2019, Springer Nature Switzerland AG. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We propose a sparse modeling approach for extracting nonlinear dynamics of surface heterogeneous reactions from noisy observable data. We employ sparse modeling algorithm and sequential Monte Carlo algorithm to partial observation problem, in order to simultaneously extract substantial reaction terms and surface models from a number of candidates. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, necessary surface models and reaction terms underlying observable data were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.
Dec. 2019, Lecture Notes in Computer Science, 11954, 380 - 391[Refereed]
International conference proceedings
- 2019, ACM International Conference Proceeding SeriesGaussian Process Dynamical Autoencoder Model
[Refereed]
- 2019, ACM International Conference Proceeding SeriesSpatio-Temporal Convolutional Neural Network for Frame Rate Up-Conversion
[Refereed]
- Jan. 2019, Neural Networks, 109, 137 - 146
[Refereed]
Scientific journal
Ants are known to use a colony-specific blend of cuticular hydrocarbons (CHCs) as a pheromone to discriminate between nestmates and non-nestmates and the CHCs were sensed in the basiconic type of antennal sensilla (S. basiconica). To investigate the functional design of this type of antennal sensilla, we observed the ultra-structures at 2D and 3D in the Japanese carpenter ant, Camponotus japonicus, using a serial block-face scanning electron microscope (SBF-SEM), and conventional and high-voltage transmission electron microscopes. Based on the serial images of 352 cross sections of SBF-SEM, we reconstructed a 3D model of the sensillum revealing that each S. basiconica houses > 100 unbranched dendritic processes, which extend from the same number of olfactory receptor neurons (ORNs). The dendritic processes had characteristic beaded-structures and formed a twisted bundle within the sensillum. At the "beads," the cell membranes of the processes were closely adjacent in the interdigitated profiles, suggesting functional interactions via gap junctions (GJs). Immunohistochemistry with anti-innexin (invertebrate GJ protein) antisera revealed positive labeling in the antennae of C. japonicus. Innexin 3, one of the five antennal innexin subtypes, was detected as a dotted signal within the S. basiconica as a sensory organ for nestmate recognition. These morphological results suggest that ORNs form an electrical network via GJs between dendritic processes. We were unable to functionally certify the electric connections in an olfactory sensory unit comprising such multiple ORNs; however, with the aid of simulation of a mathematical model, we examined the putative function of this novel chemosensory information network, which possibly contributes to the distinct discrimination of colony-specific blends of CHCs or other odor detection.
Sep. 2018, Frontiers in Cellular Neuroscience, 12, 310 - 310, English, International magazine[Refereed]
Scientific journal
- 2018, ACM International Conference Proceeding SeriesSparse Super-Resolution Method Using Accelerated Cross-Validation
[Refereed]
- 2018, Procedia Computer Science, 144, 298 - 305Harmonic Mean Similarity Based Quantum Annealing for k-means
[Refereed]
- 2018, Proceedings of the SICE Annual Conference 2018Estimation of Neural Network Dynamics Based on Sparse Modeling
[Refereed]
- 2018, Proceedings of Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent SystemsMultivariate Time Series Classification using DMD based Similarity Measure
[Refereed]
- 2018, eNeuroMicroglia Enhance Synapse Activity to Promote Local Network Synchronization
[Refereed]
Scientific journal
Slow feature analysis (SFA) is a time-series analysis method for extracting slowly-varying latent features from multidimensional data. A recent study proposed a probabilistic framework of SFA using the Bayesian statistical framework. However, the conventional probabilistic framework of SFA can not accurately extract the slow feature in noisy environments since its marginal likelihood function was approximately derived under the assumption that there exists no observation noise. In this paper, we propose a probabilistic framework of SFA with rigorously derived marginal likelihood function. Here, we rigorously derive the marginal likelihood function of the probabilistic framework of SFA by using belief propagation. We show using numerical data that the proposed probabilistic framework of SFA can accurately extract the slow feature and underlying parameters for the latent dynamics simultaneously even under noisy environments.
PHYSICAL SOC JAPAN, Aug. 2017, Journal of the Physical Society of Japan, 86 (8), English[Refereed]
Scientific journal
- 2017, Proceedings of 18th International Symposium on Advanced Intelligent SystemsFrame Rate Upconversion Using Spatio-Temporal Auto Regressive Model
[Refereed]
- 2017Nonparametric Estimation for Membrane Resistances Distributed Non-Uniformly in Neurons
[Refereed]
- 2017, ACM International Conference Proceeding SeriesBayesian Estimation of Neural Systems using Particle-Gibbs
[Refereed]
It is essential to extract nonlinear dynamics from time-series data as an inverse problem in natural sciences. We propose a Bayesian statistical framework for extracting nonlinear dynamics of surface heterogeneous reactions from sparse and noisy observable data. Surface heterogeneous reactions are chemical reactions with conjugation of multiple phases, and they have the intrinsic nonlinearity of their dynamics caused by the effect of surface-area between different phases. We adapt a belief propagationmethod and an expectation-maximization (EM) algorithm to partial observation problem, in order to simultaneously estimate the time course of hidden variables and the kinetic parameters underlying dynamics. The proposed belief propagation method is performed by using sequential Monte Carlo algorithm in order to estimate nonlinear dynamical system. Using our proposed method, we show that the rate constants of dissolution and precipitation reactions, which are typical examples of surface heterogeneous reactions, as well as the temporal changes of solid reactants and products, were successfully estimated only from the observable temporal changes in the concentration of the dissolved intermediate product.
AMER PHYSICAL SOC, Sep. 2016, PHYSICAL REVIEW E, 94 (3), English[Refereed]
Scientific journal
A research project, called "the Initiative for High-dimensional Data-Driven Science through Deepening of Sparse Modeling" is introduced. A concept, called the three levels of data-driven science, is proposed to untie the complicated relation between many fields and many methods. This concept claims that any problem of data analysis should be discussed at different three levels: computational theory, modeling, and representation/algorithm. Based on the concept, how to choose a suitable method among several candidates is discussed through our study on spectral deconvolution. In addition, how to find a universal problem across the disciplines is presented by explaining our proposed ES-SVM method. Moreover, it is illustrated that the hierarchical structure of data analysis should be visualized and shared. From these discussions, we believe that data-driven science is mother of science, namely, a scientific framework that drives many fields of science.
IOP PUBLISHING LTD, 2016, Journal of Physics: Conference Series, 699, English[Refereed]
International conference proceedings
We propose a data-driven statistical method for extracting nonlinear spatiotemporal membrane dynamics of active dendrites. We employ a framework of probabilistic information processing to extract the nonlinear spatiotemporal dynamics obeying the reaction-diffusion equation from partially observable data. By employing sequential Monte-Carlo method and other statistical methods, membrane dynamics and their underlying electrical properties are simultaneously estimated in the proposed method. Using the proposed method, we show that nonlinear spatiotemporal dynamics in active dendrites can be extracted from partially observable data.
IOP PUBLISHING LTD, 2016, Journal of Physics: Conference Series, 699, English[Refereed]
International conference proceedings
Metastable minerals commonly form during reactions between water and rock. The nucleation mechanism of polymorphic phases from solution are explored here using a two-dimensional Potts model. The model system is composed of a solvent and three polymorphic solid phases. The local state and position of the solid phase are updated by Metropolis dynamics. Below the critical temperature, a large cluster of the least stable solid phase initially forms in the solution before transitioning into more-stable phases following the Ostwald step rule. The free-energy landscape as a function of the modal abundance of each solid phase clearly reveals that before cluster formation, the least stable phase has an energetic advantage because of its low interfacial energy with the solution, and after cluster formation, phase transformation occurs along the valley of the free-energy landscape, which contains several minima for the regions of three phases. Our results indicate that the solid-solid and solid-liquid interfacial energy contribute to the formation of the complex free-energy landscape and nucleation pathways following the Ostwald step rule.
AMER PHYSICAL SOC, Oct. 2015, PHYSICAL REVIEW E, 92 (4), English[Refereed]
Scientific journal
- 2015, Proceedings of 16th International Symposium on Advanced Intelligent SystemsStatistical Estimation of Neural System
[Refereed]
- 2015, Proceedings of 16th International Symposium on Advanced Intelligent SystemsSimultaneous Estimation of Hodgkin-Huxley Neuronal Dynamics and Network Connectivity Based on Bayesian Statistics
[Refereed]
- 2015, システム制御情報学会誌回帰問題への機械学習的アプローチ～スパース性に基づく回帰モデリング～
[Refereed][Invited]
Scientific journal
The spike-triggered average (STA) and phase response curve characterize the response properties of single neurons. A recent theoretical study proposed a method to estimate the phase response curve by means of linear regression with Fourier basis functions. In this study, we propose a method to estimate the STA by means of sparse linear regression with Fourier and polynomial basis functions. In the proposed method, we use sparse estimation with L1 regularization to extract substantial basis functions for the STA. We show using simulated data that the proposed method achieves more accurate estimation of the STA than the simple trial average used in conventional method.
Information Processing Society of Japan, 2014, IPSJ Online Transactions, 7 (2014), 52 - 58, English[Refereed]
Scientific journal
- 2014, Proceedings of 21th International Conference on Neural Information ProcessingEstimating Nonlinear Spatiotemporal Membrane Dynamics in Active Dendrites
[Refereed]
- 2014, Proceedings of Joint 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent SystemsEstimation of Hyperparameters in Probabilistic Slow Feature Analysis
[Refereed]
- 2013, 電気学会誌ビッグデータの利活用と機械学習研究
- 2013, Advances in Cognitive NeurodynamicsStatistical Estimation of Non-Uniform Dendritic Membrane Properties
[Refereed]
- 2013, Proceedings of 20th International Conference on Neural Information ProcessingExtracting Latent Dynamics from Multi-dimensional Data by Probabilistic Slow Feature Analysis
[Refereed]
For the purpose of elucidating the neural coding process based on the neural excitability mechanism, researchers have recently investigated the relationship between neural dynamics and the spike triggered stimulus ensemble (STE). Ermentrout et al. analytically derived the relational equation between the phase response curve (PRC) and the spike triggered average (STA). The STA is the first cumulant of the STE. However, in order to understand the neural function as the encoder more explicitly, it is necessary to elucidate the relationship between the PRC and higher-order cumulants of the STE. In this paper, we give a general formulation to relate the PRC and the nth moment of the STE. By using this formulation, we derive a relational equation between the PRC and the spike triggered covariance (STC), which is the covariance of the STE. We show the effectiveness of the relational equation through numerical simulations and use the equation to identify the feature space of the rat hippocampal CA1 pyramidal neurons from their PRCs. Our result suggests that the hippocampal CA1 pyramidal neurons oscillating in the theta frequency range are commonly sensitive to inputs composed of theta and gamma frequency components.
PUBLIC LIBRARY SCIENCE, Nov. 2012, PLOS ONE, 7 (11), English[Refereed]
Scientific journal
- 2012, 電気学会誌スーパーコンピュータ「京」の本格稼働
[Refereed]
- 2012, 情報処理学会論文誌数理モデル化と応用Slow Feature Analysisにおける観測ノイズの影響
[Refereed]
Scientific journal
With developments in optical imaging over the past decade, statistical methods for estimating dendritic membrane resistance from observed noisy signals have been proposed. In most of previous studies, membrane resistance over a dendritic tree was assumed to be constant, or membrane resistance at a point rather than that over a dendrite was investigated. Membrane resistance, however, is actually not constant over a dendrite. In a previous study, a method was proposed in which membrane resistance value is expressed as a non-constant function of position on dendrite, and parameters of the function are estimated. Although this method is effective, it is applicable only when the appropriate function is known. We propose a statistical method, which does not express membrane resistance as a function of position on dendrite, for estimating membrane resistance over a dendrite from observed membrane potentials. We use the Markov random field (MRF) as a prior distribution of the membrane resistance. In the MRF, membrane resistance is not expressed as a function of position on dendrite, but is assumed to be smoothly varying along a dendrite. We apply our method to synthetic data to evaluate its efficacy, and show that even when we do not know the appropriate function, our method can accurately estimate the membrane resistance.
Information Processing Society of Japan, 2012, IPSJ Online Transactions, 5 (2012), 186 - 191, English[Refereed]
Scientific journal
- 2012, 電子情報通信学会情報・システムソサイエティ誌, 17 (2), 4海馬の理論研究：ニューロンから行動まで
[Invited]
We sought to measure infinitesimal phase response curves (iPRCs) from rat hippocampal CA1 pyramidal neurons. It is difficult to measure iPRCs from noisy neurons because of the dilemma that either the linearity or the signal-to-noise ratio of responses to external perturbations must be sacrificed. To overcome this difficulty, we used an iPRC measurement model formulated as the Langevin phase equation (LPE) to extract iPRCs in the Bayesian scheme. We then simultaneously verified the effectiveness of the measurement model and the reliability of the estimated iPRCs by demonstrating that LPEs with the estimated iPRCs could predict the stochastic behaviors of the same neurons, whose iPRCs had been measured, when they were perturbed by periodic stimulus currents. Our results suggest that the LPE is an effective model for real oscillating neurons and that many theoretical frameworks based on it may be applicable to real nerve systems. © 2011 American Physical Society.
03 Oct. 2011, Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 84 (4), English[Refereed]
Scientific journal
We sought to measure infinitesimal phase response curves (iPRCs) from rat hippocampal CA1 pyramidal neurons. It is difficult to measure iPRCs from noisy neurons because of the dilemma that either the linearity or the signal-to-noise ratio of responses to external perturbations must be sacrificed. To overcome this difficulty, we used an iPRC measurement model formulated as the Langevin phase equation (LPE) to extract iPRCs in the Bayesian scheme. We then simultaneously verified the effectiveness of the measurement model and the reliability of the estimated iPRCs by demonstrating that LPEs with the estimated iPRCs could predict the stochastic behaviors of the same neurons, whose iPRCs had been measured, when they were perturbed by periodic stimulus currents. Our results suggest that the LPE is an effective model for real oscillating neurons and that many theoretical frameworks based on it may be applicable to real nerve systems.
AMER PHYSICAL SOC, Oct. 2011, PHYSICAL REVIEW E, 84 (4), English[Refereed]
Scientific journal
- 2011, Clinical NeuroscienceMathematical Models of Hippocampal Systems
[Invited]
Scientific journal
- 2011, NEURAL INFORMATION PROCESSING, PT III, 7064, 240 - +Nonlinear Effect on Phase Response Curve of Neuron Model
[Refereed]
A calcium imaging method has superior ability in recording of spatial temporal variations in ion concentration. However, it has two major problems. First, the imaging signals are very noisy. Second, the observation data are only the fluorescence intensities of Ca2+ indicator dyes that provide indirect information about the Ca2+ concentration. We develop a nonlinear state-space model for Ca imaging series involving Ca2+ kinetics and a noisy fluorescence intensity pickup process. We devise recursive update algorithms for estimating the Ca2+ concentration and Ca2+ flux, and give the expectation-maximization algorithm for inferring model parameters.
PHYSICAL SOC JAPAN, Dec. 2010, JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 79 (12), English[Refereed]
Scientific journal
Spike-triggered analysis is a statistical method used to elucidate encoding properties in neural systems by estimating the statistical structure of input stimulus preceding spikes. A recent numerical study suggested that the profile of the spike-triggered average (STA) changes depending on whether the mean input stimuli are subthreshold or suprathreshold. Here we analytically verify the difference between subthreshold STA and suprathreshold STA by using the spike response model (SRM). We show by moment expansion that the suprathreshold STA is proportional to the first derivative of the response kernel, and that the subthreshold STA is expressed by a linear combination of the response kernel and its first derivative. We verify whether the analytical results obtained from the SRM can be applied to a multicompartment model with Hodgkin-Huxley type dynamics. © 2010 The American Physical Society.
01 Feb. 2010, Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, 81 (2), English[Refereed]
Scientific journal
Under physiological and artificial conditions, the dendrites of neurons can be exposed to electric fields Recent experimental studies suggested that the membrane resistivity of the distal apical dendrites of cortical and hippocampal pyramidal neurons may be significantly lower than that of the proximal dendrites and the soma To understand the behavior of dendrites in time-varying extracellular electric fields, we analytically solved cable equations for finite cylindrical cables with and without a leak conductance attached to one end by employing the Green's function method The solution for a cable with a leak at one end for direct-current step electric fields shows a reversal in polarization at the leaky end, as has been previously shown by employing the separation of variables method and Fourier series expansion The solution for a cable with a leak at one end for alternating-current electric fields reveals that the leaky end shows frequency preference in the response amplitude Our results predict that a passive dendrite with low resistivity at the distal end would show frequency preference in response to sinusoidal extracellular local field potentials The Green's function obtained in our study can be used to calculate response for any extracellular electric field
CELL PRESS, Feb. 2010, BIOPHYSICAL JOURNAL, 98 (4), 524 - 533, English[Refereed]
Scientific journal
Spike-triggered analysis is a statistical method used to elucidate encoding properties in neural systems by estimating the statistical structure of input stimulus preceding spikes. A recent numerical study suggested that the profile of the spike-triggered average (STA) changes depending on whether the mean input stimuli are subthreshold or suprathreshold. Here we analytically verify the difference between subthreshold STA and suprathreshold STA by using the spike response model (SRM). We show by moment expansion that the suprathreshold STA is proportional to the first derivative of the response kernel, and that the subthreshold STA is expressed by a linear combination of the response kernel and its first derivative. We verify whether the analytical results obtained from the SRM can be applied to a multicompartment model with Hodgkin-Huxley type dynamics.
AMER PHYSICAL SOC, Feb. 2010, PHYSICAL REVIEW E, 81 (2), English[Refereed]
Scientific journal
- 2010, IPSJ Trans. Math Model. Appl.Phase Response Curve of Spike Response Model
[Refereed]
Scientific journal
- 2010, 情報処理学会論文誌 数理モデル化と応用粒子フィルタによる細胞内カルシウム動態の推定
[Refereed]
Scientific journal
- 2010, 情報処理学会論文誌 数理モデル化と応用樹状突起における電気的応答特性の推定
[Refereed]
Scientific journal
- ELSEVIER IRELAND LTD, 2010, NEUROSCIENCE RESEARCH, 68, E107 - E107, English
[Refereed]
- ELSEVIER IRELAND LTD, 2010, NEUROSCIENCE RESEARCH, 68, E46 - E46, English
[Refereed]
- 2010, NEUROSCIENCE RESEARCH, 68, E111
[Refereed]
- ELSEVIER IRELAND LTD, 2010, NEUROSCIENCE RESEARCH, 68, E435 - E435, English
[Refereed]
- 2010, Australian Journal of Intelligent Information Processing Systems, 11 (1), 29 - 34, English
[Refereed]
Scientific journal
An associative memory model and a neural network model with a Mexican-hat type interaction are two major attractor neural network models. The associative memory model has discretely distributed fixed-point attractors, and achieves a discrete information representation. On the other hand, a neural network model with a Mexican-hat type interaction uses a ring attractor to achieves a continuous information representation, which can be seen in the working memory in the prefrontal cortex and columnar activity in the visual cortex. In the present study, we propose a neural network model that achieves discrete and continuous information representation. We use a statistical-mechanical analysis to find that a localized retrieval phase exists in the proposed model, where the memory pattern is retrieved in the localized Subpopulation of the network. In the localized retrieval phase, the discrete and continuous information representation is achieved by using the orthogonality of the memory patterns and the neutral stability of fixed points along the positions of the localized retrieval. The obtained phase diagram suggests that the anti ferromagnetic interaction and the external field are important for generating the localized retrieval phase.
PHYSICAL SOC JAPAN, Nov. 2009, JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 78 (11), English[Refereed]
Scientific journal
Specific membrane resistance (R(m)), distributed non-uniformly over the dendrite, has a substantial effect on neuronal information processing, since it is a major determinant in subthreshold-synaptic integration. From experimental data of dendritic excitatory postsynaptic potential (EPSP) spread, we previously reported that non-uniform R(m) distribution in hippocampal CA1 pyramidal neurons could be expressed as a step function. However, it remains unclear how steeply R(m) decreases. Here, we estimated the R(m) distribution using sigmoid function to evaluate the steepness of decrease in R(m). Simulations were performed to find the distribution which reproduced experimental voltage responses to extracellular electric field applied to CA1 slices, in contrast to the EPSP spread. Distribution estimated from the responses to electric field was a steep-sigmoid function, similar to that from the EPSP spread. R(m) in distal dendrite was estimated to be less than or similar to 10(3.5) Omega cm(2) whereas that in proximal dendrite/soma was greater than or similar to 10(4.5) Omega cm(2). Our results not only supported previous studies, but, surprisingly, implied that R(m) decreases at a location more distal, and that distal dendrite was leakier, than previous estimates by other groups. Simulations satisfactorily reproduced the responses to two distinct perturbations, suggesting that steep decrease in R. is reliable. Our study suggests that the non-uniform Rm distribution plays an important role in information processing for spatially segregated synaptic inputs. (C) 2009 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.
ELSEVIER IRELAND LTD, May 2009, NEUROSCIENCE RESEARCH, 64 (1), 83 - 95, English[Refereed]
Scientific journal
Many research groups have sought to measure phase response curves (PRCs) from real neurons. However, methods of estimating PRCs from noisy spike-response data have yet to be established. In this paper, we propose a Bayesian approach for estimating PRCs. First, we analytically obtain a likelihood function of the PRC from a detailed model of the observation process formulated as Langevin equations. Then we construct a maximum a posteriori (MAP) estimation algorithm based on the analytically obtained likelihood function. The MAP estimation algorithm derived here is equivalent to the spherical spin model. Moreover, we analytically calculate a marginal likelihood corresponding to the free energy of the spherical spin model, which enables us to estimate the hyper-parameters, i.e., the intensity of the Langevin force and the smoothness of the prior.
SPRINGER, Apr. 2009, JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 26 (2), 185 - 202, English[Refereed]
Scientific journal
- 2009, Proceedings of Asia Simulation Conference 2009Estimation of Intracellular Calcium Ion Concentration by Nonlinear State Space Modeling
[Refereed]
- ELSEVIER IRELAND LTD, 2009, NEUROSCIENCE RESEARCH, 65, S74 - S74, English
[Refereed]
- 2009, J. Phys. Conf. Ser., EnglishFusion of Real Neuron and Mathematical Model by using Dynamic Clamp Technique
[Refereed]
Scientific journal
- 2009, Proc. Asia. Sim. Conf. 2009, EnglishFusion of Real Neuron and Mathematical Model by using Dynamic Clamp Technique
[Refereed]
International conference proceedings
- ELSEVIER IRELAND LTD, 2009, NEUROSCIENCE RESEARCH, 65, S84 - S84, English
[Refereed]
International conference proceedings
- 2009, Proc. Asia. Sim. Conf. 2009, EnglishEstimation of Non-Uniform Membrane Property over the Dendrite: Data Assimilation Approach using Bioimaging Data and Multi-Compartment Model
[Refereed]
International conference proceedings
- ELSEVIER IRELAND LTD, 2009, NEUROSCIENCE RESEARCH, 65, S136 - S136, English
[Refereed]
International conference proceedings
The Langevin phase model is an important canonical model for capturing coherent oscillations of neural populations However, little attention has been given to verifying its applicability In this paper, we demonstrate that the Langevin phase equation is an efficient model for neural oscillators by using the machine learning method in two steps. (a) Learning of the Langevin phase model We estimated the parameters of the Langevin phase equation, i.e. , a phase response curve and the intensity of white noise from physiological data measured in the hippocampal CA1 pyramidal neurons (b) Test of the estimated model. We verified whether a Fokker-Planck equation derived from the Langevin phase equation with the estimated parameters could capture the stochastic oscillatory behavior of the same neurons disturbed by periodic perturbations The estimated model could predict the neural behavior, so we can say that the Langevin phase equation is an efficient model for oscillating neurons
IOP PUBLISHING LTD, 2009, INTERNATIONAL WORKSHOP ON STATISTICAL-MECHANICAL INFORMATICS 2009 (IW-SMI 2009), 197 (012016), English[Refereed]
International conference proceedings
A lossy data compression scheme for uniformly biased Boolean messages is investigated via statistical mechanics techniques. We utilize a treelike committee machine (committee tree) and a treelike parity machine (parity tree) whose transfer functions are nonmonotonic. The scheme performance at the infinite code length limit is analyzed using the replica method. Both committee and parity treelike networks are shown to saturate the Shannon bound. The Almeida-Thouless stability of the replica symmetric solution is analyzed, and the tuning of the nonmonotonic transfer function is also discussed.
AMER PHYSICAL SOC, Aug. 2008, PHYSICAL REVIEW E, 78 (2), English[Refereed]
Scientific journal
- Statistical mechanics of lossy compression for non-monotonic multilayer perceptrons
A lossy data compression scheme for uniformly biased Boolean messages is investigated via statistical mechanics techniques. The present paper utilize tree-like committee machine (committee tree) and tree-like parity machine (parity tree) whose transfer functions are non-monotonic, completing the study of the lossy compression scheme using perceptron-based decoder. The scheme performance at the infinite code length limit is analyzed using the replica method. Both committee and parity treelike networks are shown to saturate the Shannon bound.
IEEE, 2008, 2008 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY PROCEEDINGS, VOLS 1-6, 509 - +, English[Refereed]
International conference proceedings
- 2008, NEUROSCIENCE RESEARCH, 61, S72Statistical estimation of spatiotemporal distribution of membrane potential over the dendrite using partially observable voltage imaging data
[Refereed]
- ELSEVIER IRELAND LTD, 2008, NEUROSCIENCE RESEARCH, 61, S140 - S140, EnglishBayesian restoration of phase response curves and prediction of stochastic behavior of hippocampal CA1 pyramidal neurons
[Refereed]
- 2007, NEUROSCIENCE RESEARCH, 58, S40
[Refereed]
- ELSEVIER IRELAND LTD, 2007, NEUROSCIENCE RESEARCH, 58, S185 - S185, English
[Refereed]
- ELSEVIER IRELAND LTD, 2006, NEUROSCIENCE RESEARCH, 55, S140 - S140, EnglishSteep decrease of specific membrane resistance in distal dendrite
[Refereed]
- 2006, J. Jpn. Soc. Sim. Tech.Optical Recording of Neuronal Response to Perturbation and Estimation of Membrane Property by Numerical Simulation
[Refereed]
Scientific journal
- 2006, Brain Res., EnglishEstimated Distribution of Specific Membrane Resistance in Hippocampal CA1 Pyramidal Neuron
[Refereed]
Scientific journal
We propose a two-layered neural network model for oscillatory phenomena in the thalamic system and investigate an effect of neuromodulation due to the acetylcholine on the oscillatory phenomena by numerical simulations. The proposed model consists of a layer of the thalamic reticular neurons and that of the cholinergic neurons. We introduce a dynamics of concentration of the acetylcholine which depends on a state of the cholinergic neurons, and assume that the conductance of the thalamic reticular neurons is dynamically regulated by the acetylcholine. From the results obtained by numerical simulations, we find that a dynamical transition between a bursting state and a resting state occurs successively in the layer of the thalamic reticular neurons due to the acetylcholine. Therefore it turns out that the neuromodulation due to the acetylcholine is important for the dynamical state transition in the thalamic system.
PHYSICAL SOC JAPAN, Dec. 2004, JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 73 (12), 3489 - 3494, English[Refereed]
Scientific journal
We propose a neural network model of working memory with one-compartmental neurons and investigate its dynamical properties. We assume that the model consists of excitatory neurons and inhibitory neurons; all the neurons are connected to each other. The excitatory neurons are distinguished as several groups of selective neurons and one group of non-selective neurons. The selective neurons are assumed to form subpopulations in which each selective neuron belongs to only one of subpopulations. The non-selective neurons are assumed not to form any subpopulation. Synaptic strengths between neurons within a subpopulation are assumed to be potentiated. By the numerical simulations, persistent firing of neurons in a subpopulation emerges; the persistent firing corresponds to the retention of memory as one of the functions of working memory. We find that the strength of external input and the strength of N-methyl-D-aspartate synapse are important factors for dynamical behaviors of the network; for example, if we enhance the strength of the external input to a subpopulation while the persistent firing is occurring in other subpopulation, the persistent firing occurs in the subpopulation or is sustained against the external input. These results reveal that the neural network as for the function of the working memory is controlled by the neuromodulation and the external stimuli within the proposed model. We also find that the persistent time of firing of the selective neurons shows a kind of phase transition as a function of the degree of potentiation of synapses, (C) 2003 Elsevier B.V. All rights reserved.
ELSEVIER SCIENCE BV, Mar. 2004, PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 334 (3-4), 600 - 614, English[Refereed]
Scientific journal
We propose a dynamical neural network model with excitatory neurons and inhibitory neurons for memory function in hippocampus and investigate the effect of inhibitory neurons on memory recall. The results by numerical simulations show that the introduction of inhibitory neurons improves the stability of the memory recall in the proposed model by supppressing the bursting of neurons.
PHYSICAL SOC JAPAN, Mar. 2004, JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 73 (3), 749 - 755, English[Refereed]
Scientific journal
- 2003, Proceedings of 2003 Workshop on Information-Based Induction SciencesDynamical Neuromodulation in Neural Network with Inhibitory Neurons and Cholinergic Neurons
[Refereed]
We investigate some noise effect on a neural network model proposed by Araki and Aihara for the memory recall of dynamical patterns in the hippocampus and the entorhinal cortex; the noise effect is important since the release of transmitters at synaptic clefts, the operation of gate of ion channels and so on are known as stochastic phenomena. We consider two kinds of noise effect due to a deterministic noise and a stochastic noise. By numerical simulations, we find that reasonable values of noise give better performance on the memory recall of dynamical patterns. Furthermore we investigate the effect of the strength of external inputs on the memory recall.
PHYSICAL SOC JAPAN, Jun. 2002, JOURNAL OF THE PHYSICAL SOCIETY OF JAPAN, 71 (6), 1598 - 1604, English[Refereed]
Scientific journal
We propose a hippocampal CA3 model with excitatory neurons and inhibitory neurons by using Hodgkin-Huxley equations and investigate oscillatory phenomena in the model. We assume that there are two layers of neurons with interlayer and intra-layer synaptic connections; one is a layer of excitatory neurons and the other a layer of inhibitory neurons. The results by numerical simulations show that theta and gamma oscillations occur on the layer of excitatory neurons and also on the layer of inhibitory neurons and reveal that synaptic strengths influence dominant oscillations on each layer. This implies that the interplay between the excitatory and the inhibitory neurons is essential for the generation of theta and gamma oscillations.
NANYANG TECHNOLOGICAL UNIV, 2002, ICONIP'02: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON NEURAL INFORMATION PROCESSING, 1597 - 1601, English[Refereed]
International conference proceedings
MISC
- Aug. 2019, 神戸大学工学部オープンキャンパス2019 模擬講義人工知能が拓く情報処理技術～脳型AIの新展開～
- Jun. 2013, 第31回神戸大学工学部公開講座「20X0年のくらしを支える工学」大規模データ時代に立ち向かう情報処理技術の開拓～機械学習法とその応用～
Presentations
- 11th RIEC Internaional Symposium on Brain Functions and Brain Computer, 18 Feb. 2023Machine Learning Algorithm for Estimating Nonlinear Neurodynamics by Sequential Monte Carlo Method and Sparse Modeling
- 11th RIEC Internaional Symposium on Brain Functions and Brain Computer, 18 Feb. 2023Learning Super-resolution of X-ray CT Images of Rocks Based on Sparse Representation
- 11th RIEC Internaional Symposium on Brain Functions and Brain Computer, 18 Feb. 2023Data-driven Approach for Image Reconstruction Using Neural Ordinary Differential Equation Models
- 11th RIEC Internaional Symposium on Brain Functions and Brain Computer, 18 Feb. 2023Sparse Estimation of Nonlinear Dynamics Using Generalized Dynamical Constraints
- 定量生物学の会第十回年会, 15 Dec. 2022データ駆動型アプローチによる神経ダイナミクスの推定制御
- 第21回情報科学技術フォーラム, 15 Sep. 2022温度の効果を用いた自己組織化状態空間モデルによる潜在変数とパラメータの推定
- 日本応用数理学会2022年度年会, 08 Sep. 2022温度の効果を導入した自己組織化状態空間モデルによる神経ダイナミクスの推定
- 神戸大学次世代光散乱イメージング科学研究センターキックオフシンポジウム, 27 Jun. 2022統計的機械学習に基づく神経システムの推定と制御
- 神戸大学次世代光散乱イメージング科学研究センターキックオフシンポジウム, 27 Jun. 2022ベイズ推論に基づく神経ネットワークのシステム同定
- 日本地球惑星科学連合2022年大会, 30 May 2022Superresolution of X-ray CT Images of Serpentinites by Sparse Modeling
- 第66回システム制御情報学会研究発表講演会, 20 May 2022レプリカ交換粒子マルコフ連鎖モンテカルロ法を用いた神経システムのダイナミクス推定
- メレオロジー研究会, 20 May 2022多次元・多階層データへのデータ駆動型アプローチの展開
[Invited]
- 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 10 Apr. 2022Unsupervised Deep Video Interpolation Based on Spatio-Temporal Autoregressive Neural Network
- 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 10 Apr. 2022Estimating Dynamical Nonlinear System with Nonstationarity by Gaussian Process Self- Organizing Generalized State-Space Model
- 6th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 10 Apr. 2022A Framework for Estimating Integrated Information of Brain Based on Deep Neural Network
- The 99th Annual Meeting of the Physiological Society of Japan, 18 Mar. 2022Data-driven Modeling of Neuronal Nonlinear Dynamics
[Invited]
- The 99th Annual Meeting of the Physiological Society of Japan, 16 Mar. 2022Data-driven Approach for Multi-dimensional and Multi-scale Data Analysis
[Invited]
- Joint Symposium on Life Science, Computational Science, and Structural Engineering Between UC San Diego and Kobe University, 03 Feb. 2022Data-driven Approach for Dynamical System Modeling
[Invited]
- 2021年度情報処理学会関西支部定期講演会『機械学習・深層学習に関する最新動向』, 16 Dec. 2021データ駆動型アプローチによる動的システムの数理モデリング
[Invited]
- 大阪大学 数理・データ科学教育研究センター AI・データ利活用研究会 第22回, 29 Oct. 2021データ駆動型アプローチによる動的システムモデリング
[Invited]
- 研究会「数理科学と情報学の連携による次世代モデリング理論」, 20 Sep. 2021ホワイトボックスモデリングによる非線形動的システムの推定II
[Invited]
- 研究会「数理科学と情報学の連携による次世代モデリング理論」, 19 Sep. 2021ホワイトボックスモデリングによる非線形動的システムの推定I
[Invited]
- 日本応用数理学会 2021年度 年会, 08 Sep. 2021データ駆動型アプローチによる神経ネットワークのダイナミクス推定
[Invited]
- 日本地球惑星科学連合2021年大会, 03 Jun. 2021岩石コア試料のX線CT画像の超解像とオマーンオフィオライトの蛇紋岩への適用
- 第26回計算工学講演会, 26 May 2021データ駆動型アプローチによる動的システムモデリング
- EGU General Assembly 2021, 27 Apr. 2021Extraction of Nonlinear Dynamics of Heterogeneous Reactions Based on Sparse Modeling
- 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 11 Apr. 2021Video Frame Rate up Conversion via Spatio-Temporal Generative Adversarial Networks
- 5th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, 10 Apr. 2021Sparse-Sequential Monte Carlo Method for Extracting Nonlinear Dynamics of Heterogeneous Reactions
- 2021 IEEE 3rd Global Conference on Life Sciences and Technologies, Mar. 2021Online Bayesian Approach for Estimation and Control of Neural System
- 日本物理学会第76回年次大会, Mar. 2021スパースモデリングによる不均質反応非線形ダイナミクスの推定
- The SfN Global Connectome, Society for Neuroscience, Jan. 2021Data-driven Analysis of Information Transfer Using Cortical Neural Circuit Model with Extracellular Electric Field
- The SfN Global Connectome, Society for Neuroscience, Jan. 2021Online Data-driven Estimation and Control of Nonlinear Model for Neuronal Dynamics
- The 9th RIEC International Symposium on Brain Functions and Brain Computer, Dec. 2020Estimation of Neural Dynamics with Particle Markov Chain Monte Carlo
- The 9th RIEC International Symposium on Brain Functions and Brain Computer, Dec. 2020Influence of Extracellular Electric Fields on Network Model of Neocortical Neural Circuit
- 日本応用数理学会2020年度年会, Sep. 2020データ駆動型アプローチによる動的システムのモデリング
- 神戸大学先端融合研究環第4回極みプロジェクトシンポジウム, Sep. 2020データ駆動型アプローチに基づく神経活動の解析
- KISC Workshop 2020, Sep. 2020データ駆動型アプローチによる非線形ダイナミクスの推定
[Invited]
- KISC Workshop 2020, Sep. 2020スパースモデリングと逐次モンテカルロ法による不均質反応非線形ダイナミクスの推定
- 日本物理学会2020年秋季大会, Sep. 2020スパースモデリングによる不均質反応ダイナミクスの推定
- KISC Workshop 2020, Sep. 2020神経システムにおける非線形ダイナミクスの統計的オンライン推定と状態制御
- 第19回情報科学技術フォーラム, Sep. 2020データ駆動型アプローチに基づく神経ダイナミクスのオンライン推定と制御
- 連携探索セミナー「幾何学的力学・計算代数学を基礎とするデータ駆動型モデリング」第２回, 03 Aug. 2020データ駆動型アプローチに基づく物理モデリングⅡ
[Invited]
- 連携探索セミナー「幾何学的力学・計算代数学を基礎とするデータ駆動型モデリング」第１回, 14 Jul. 2020データ駆動型アプローチに基づく物理モデリングⅠ
[Invited]
- JpGU-AGU Joint Meeting 2020, May 2020Bayesian Data-driven Approach for Extracting Nonlinear Dynamics of Heterogeneous Reactions
- The 97th Annual Meeting of the Physiological Society of Japan, Mar. 2020Estimating Neural Dynamics Based on Data-driven Approach
- 2020 IEEE 2nd Global Conference on Life Sciences and Technologies, Feb. 2020Sparse Modeling Approach for Estimating Odor Pleasantness from Multi-dimensional Sensor Data
- The 8th RIEC International Symposium on Brain Functions and Brain Computer, Feb. 2020Data-Driven Approach for Extracting Neuronal Non-linear Dynamics
[Invited]
- The 8th RIEC International Symposium on Brain Functions and Brain Computer, Feb. 2020Machine Learning Algorithm for Estimating Odor Pleasantness by Sparse Modeling Method
- The 8th RIEC International Symposium on Brain Functions and Brain Computer, Feb. 2020Data-driven Approach for Structure Estimation of Neural Networks
- The 8th RIEC International Symposium on Brain Functions and Brain Computer, Feb. 2020Online Statistical Algorithm for Estimation and Control of Neuronal Dynamics
- International Conference on Ophiolites and the Oceanic Lithosphere, Jan. 2020Super-resolution of X-ray CT Images of Rock Core Samples by Sparse Representation : Methodology and Applications to Serpetinized Peridotite from CM1A
- 26th International Conference on Neural Information Processing, Dec. 2019Sparse Modeling of Nonlinear Dynamics in Heterogeneous Reactions
- 26th International Conference on Neural Information Processing, Dec. 2019Sparse Estimation of Neuronal Network Structure with Observed Data
- 26th International Conference on Neural Information Processing, Dec. 2019Online Estimation and Control of Neuronal Nonlinear Dynamics Based on Data-driven Statistical Approach
- 神戸大学先端融合研究環 第3回極みプロジェクトシンポジウム，CREST「ホログラム光刺激による神経回路再編の人為的創出」第3回シンポジウム, Dec. 2019スパースモデリングに基づく神経回路活動からの刺激推定
[Invited]
- 文部科学省 科学技術試験研究委託事業「数学アドバンストイノベーションプラットフォーム」，日本比較生理生化学会若手の会 数学-生物学領域横断ワークショップ, Dec. 2019データ駆動によるシステム・パラメタのベイズ推定について
[Invited]
- Joint Symposium on Life Science, Computational Science, and Structural Engineering Between Kobe University and UC San Diego, Center for Neural Circuits and Behavior, University of California San Diego, Nov. 2019Extracting Neural Dynamics Based on Computational and Data-driven Approaches
[Invited]
- 第80回応用物理学会秋季学術講演会, Sep. 2019生物に学ぶ効率的情報統合過程の数理モデル
- 第6回イメージング数理研究会, Sep. 2019スパースモデリングに基づく匂いセンサ情報の認識アルゴリズム
- 第6回イメージング数理研究会, Sep. 2019Simultaneous Estimation of Neuronal Dynamics and Connectivity
- 第6回イメージング数理研究会, Sep. 2019スパースモデリングによる神経回路活動のデータ駆動型解析
- 第6回イメージング数理研究会, Sep. 2019マルコフ連鎖モンテカルロ法を用いた神経ネットワークの構造推定
- 第18回情報科学技術フォーラム, Sep. 2019スパースモデリングに基づく神経回路構造のシステム同定
- The Joint Meeting of the 42nd Annual Meeting of the Japan Neuroscience Society and the 62nd Annual Meeting of the Japanese Society for Neurochemistry, Jul. 2019Estimating Nonlinear Neuronal Dynamics Based on Sparse Modeling
- The Joint Meeting of the 42nd Annual Meeting of the Japan Neuroscience Society and the 62nd Annual Meeting of the Japanese Society for Neurochemistry, Jul. 2019Structure Estimation of Neural Networks Based on Sparse Modeling
- 名古屋大学 大学院環境学研究科 第2回岩鉱ワークショップ, Jul. 2019オマーン掘削コア試料のX線CT画像の超解像
[Invited]
- 第33回人工知能学会全国大会, Jun. 2019スパースモデリングに基づく神経回路の構造推定
- 日本地球惑星科学連合2019年大会, May 2019Specifying Rate Constants and Reaction Path on the Water-rock Chemical Reaction Based on Exchange Monte Carlo Method and Sparse Modeling
- 日本地球惑星科学連合2019年大会, May 2019スパースモデリングによる不均質反応非線形ダイナミクスの推定
- 日本地球惑星科学連合2019年大会, May 2019Superresolution of X-ray CT images of core samples for understanding the multi-scale structures of water-rock interaction
- RIMS, Kyoto University, May 2019Data-Driven Approach for Estimating Neuronal Network Dynamics
[Invited]
- RIMS, Kyoto University, May 2018Estimation of Spatiotemporal Dynamics based on Cable Equation - Mathematical Modeling of Odor Sensor Information Network
[Invited]
- 研究会「ホログラム光刺激による神経回路再編の人為的創出を目指して」, Oct. 2017データ駆動型アプローチに基づく神経ダイナミクスの抽出
[Invited]
- 北海道大学大学院理学研究院 数学部門，北海道大学電子科学研究所附属 社会創造数学研究センター クロスボーダーシンポジウム, Jan. 2017神経樹状突起における電気的応答特性の抽出―データ駆動型アプローチによるダイナミクス推定―
[Invited]
- 電子情報通信学会 2016年総合大会, Mar. 2016時空間自己回帰モデルのスパース推定に基づく時間的超解像
- International Meeting on "High-Dimensional Data Driven Science (HD3-2015)", Dec. 2015Extracting Nonlinear Spatiotemporal Dynamics in Active Dendrites Using Data-Driven Statistical Approach
[Invited]
- NINS/IURIC Colloquium 2015, Dec. 2015データ駆動型アルゴリズムとその応用～潜在ダイナミクスの抽出技術～
- 計測自動制御学会 システム・情報部門 自律分散システム部会 第57回自律分散システム部会研究会「大規模システムのダイナミクス予測と制御に向けて」, Dec. 2015データ駆動型アプローチに基づく時空間ダイナミクスの推定
[Invited]
- The 45th Annual Meeting of Society for Neuroscience, Oct. 2015Extracting Non-linear Spatiotemporal Dynamics in Active Dendrite: Data-Driven Statistical Approach
- The 38th Annual Meeting of the Japan Neuroscience Society, Jul. 2015Extracting Non-linear Spatiotemporal Dynamics in Active Dendrite: Data-Driven Statistical Approach
- 第59回システム制御情報学会研究発表講演会, May 2015樹状突起における非線形時空間ダイナミクスの抽出
- 第59回システム制御情報学会研究発表講演会, May 2015カルシウムイメージングによる神経システムの統計的推定
- 第59回システム制御情報学会研究発表講演会, May 2015ベイズ統計に基づく神経細胞の電気回路モデルとネットワーク結合の同時推定
- 日本地球惑星科学連合2015年大会, May 2015不均質反応を支配する非線形ダイナミクスのベイズ解析～岩石形成ダイナミクスの理解に向けて～
- UC San Diego x Kobe University Joint Research Kick-off Symposium, 05 Feb. 2015Extracting Spatiotemporal Dynamics of Neural Systems: Computational and Statistical Approach
[Invited]
- 「スパースモデリングの深化と高次元データ駆動科学の創成」，「核内クロマチン・ライブダイナミクスの数理研究拠点」合同シンポジウム，広島大学, 08 Oct. 2013イメージングデータからの脳神経ダイナミクスの抽出～状態空間モデルに基づく動態推定～
- 7th APCTP-KAIST School for Brain Dynamics, 25 Nov. 2012Extracting Spatiotemporal Dynamics of Dendritic Membrane Potential
[Invited]
- 5th Annual Meeting of Japanese Society for Quantitative Biology, 24 Nov. 2012Estimating Spatiotemporal Dynamics of Dendritic Membrane Potential – Information Extraction Using Bayesian Statistics
[Invited]
- 第56回システム制御情報学会研究発表講演会「学習・進化・適応の最新動向」, 22 May 2012スパース性に基づく動的システムの統計的推定
- 知能化医療システム研究会, 23 Jul. 2011リズム現象の数理～非線形神経システムへの位相縮約によるアプローチ～
- 第5回学融合ビジュアライゼーションシンポジウム, Jun. 2011樹状突起に不均一に分布する膜応答特性の推定～ベイズ統計に基づく情報抽出～
[Invited]
- Japan-Germany Joint Workshop on Computational Neuroscience, 03 Mar. 2011, EnglishSwitch of Encoding Characteristics in Single Neurons by Subthreshold and Suprathreshold Stimuli
- 統計数理研究所研究会 神経科学と統計科学の対話, Dec. 2010樹状突起膜電位の時空間ダイナミクスを統計的に推定する
- Neuro2010, Sep. 2010Opening Remarks: "Dynamic Clamp: Bridging between Theory and Experiment"
- Neuro2010, Sep. 2010Opening Remarks: "Dynamic Clamp: Bridging between Theory and Experiment"
- The Fourth International Neural Microcircuitry Conference, Signal Processing Mechanism of Cortical Neurons, Jun. 2010Statistical Estimation of Non-Uniform Dendritic Membrane Properties
- The Fourth International Neural Microcircuitry Conference, Signal Processing Mechanism of Cortical Neurons, Jun. 2010Statistical Estimation of Non-Uniform Dendritic Membrane Properties
- 第3回学融合ビジュアライゼーションシンポジウム, May 2010イメージングデータからの神経樹状突起ダイナミクスの抽出
[Invited]
- 日本物理学会第65回年次大会, Mar. 2010入力刺激が閾値上か閾値下かに依存する神経細胞の符号化特性の変化
- Asia Simulation Conference 2009, Oct. 2009Estimation of Non-Uniform Membrane Property over the Dendrite: Data Assimilation Approach Using Bioimaging Data and Multi-Compartment Model
- 2008年度シナプス研究会「シナプス成熟と可塑性のダイナミクス」, Dec. 2008海馬CA1錐体細胞の樹状突起における膜特性分布の推定とその機能的意義の検討
- 東京大学複雑系生命システム研究センター研究会「多次元複雑システムの観測科学」, Dec. 2008樹状突起における膜応答特性の不均一性分布の推定
[Invited]
- 日本物理学会2008年秋季大会, Sep. 2008Spike-Triggered Averageとスパイク応答モデルの関係
- 第31回日本神経科学大会, Jul. 2008部分的に観測される膜電位データを用いた樹状突起膜電位の時空間分布推定
- 九州工業大学大学院生命体工学研究科脳情報専攻COEセミナー, Mar. 2007神経活動データを用いた単一ニューロンの応答特性の推定
[Invited]
- 東北大学情報数物研究会, Jun. 2006海馬CA1錐体細胞の樹状突起における膜特性分布の推定
[Invited]
- 日本神経回路学会平成17年度時限研究会「プラットフォームシミュレータを用いた分子から神経回路までの統合的理解」, Sep. 2005NEURONシミュレータ入門
[Invited]
- 日本神経回路学会平成17年度時限研究会「プラットフォームシミュレータを用いた分子から神経回路までの統合的理解」, Sep. 2005細胞の摂動応答の光計測とシミュレータによる膜特性の推定(理論)
[Invited]
Association Memberships
電子情報通信学会
情報処理学会
電気学会
計測自動制御学会
システム制御情報学会
米国電気電子学会(IEEE)
米国計算機学会(ACM)
日本物理学会
日本応用数理学会
日本神経回路学会
人工知能学会
日本神経科学学会
北米神経科学学会(Society for Neuroscience)
日本地球惑星科学連合
Research Projects
- AMED, 脳とこころの研究推進プログラム, 2021
- 2021, Principal investigator文部科学省:科学研究費補助金 基盤研究(B)
- 科学技術振興機構, CREST 数理的情報活用基盤, 2019
Competitive research funding
- 科学技術振興機構, CREST バイオオプト, 2017
- 2016, Principal investigator文部科学省:科学研究費補助金 基盤研究(C)
Competitive research funding
- 文部科学省, 科学研究費補助金 国際共同研究加速基金(国際共同研究強化), 2016, Principal investigator
Competitive research funding
- 文部科学省, 科学研究費補助金 新学術領域研究 研究領域提案型 (スパースモデリングの深化と高次元データ駆動科学の創成，計画研究), 2013
- MEXT, Grant-in-Aid for Young Scientists (B), 2013, Principal investigator
Competitive research funding
- 日本学術振興会, 国際交流事業 (国際学会等派遣事業 第I期), 2010, Principal investigator
Competitive research funding
- MEXT, Grant-in-Aid for Scientific Research on Innovative Areas, 2010, Principal investigator
Competitive research funding
- 2009, Principal investigator文部科学省:科学研究費補助金 基盤研究(C)
Competitive research funding
- 日本学術振興会, 国際交流事業 (国際学会等派遣事業 第III期), 2008, Principal investigator
Competitive research funding
- MEXT, Grant-in-Aid for Special Purposes, 2008, Principal investigator
Competitive research funding
- MEXT, Grant-in-Aid for JSPS Fellows, 2006, Principal investigator
Competitive research funding
- MEXT, Grant-in-Aid for Young Scientists (B), 2005, Principal investigator
Competitive research funding
- MEXT, Grant-in-Aid for JSPS Fellows, 2003, Principal investigator
Competitive research funding
Others
Financial Chair, Organizing Committee, IEEE World Congress on Computational Intelligence 2024 (WCCI 2024)
2021 - Present情報処理学会論文誌 数理モデル化と応用編集委員会 編集委員
2018 - Present情報処理学会 数理モデル化と問題解決研究運営委員会 運営委員
2017 - PresentProgram Committee Member, 15th IEEE/ACIS International Conference on Computer and Information Science (2016)
2016 - 2016Organizing Committee Member, 23th International Conference on Neural Information Processings (2016)
2016 - 2016Program Committee Member, 21th International Conference on Neural Information Processings (2014)
2014 - 2014システム制御情報学会 編集委員
2014 - 2014平成25年(第68回)電気関係学会関西連合大会 実行委員
2013 - 2013電気学会 選挙管理委員会 委員
2013 - 2013日本物理学会 領域11 領域役員（統計・物基）
2013 - 2013電気学会 創立125周年記念事業委員会 委員
2013 - 2013電気学会 総務会議 委員
2013 - 2013第57回システム制御情報学会研究発表講演会(SCI'13) 実行委員
2012 - 2012電気学会 創立125周年記念事業実行委員会 委員
2012 - 2012電気学会 広報委員会 委員
2012 - 2012電気学会 編修専門第3部会 委員
2012 - 2012電気学会 関西支部 総務企画幹事
2012 - 2012, Secretary, Planning & General Affairs Kansai Branch The Institute of Electrical Engineers of JapanProgram Committee Member, 18th International Conference on Neural Information Processings (2011).
Program Committee Member, 17th International Conference on Neural Information Processings (2010).
Program Committee Member, 16th International Conference on Neural Information Processings (2009).
Technical Committee Member, 15th International Conference on Neural Information Processings (2008).
Program Committee Member, 20th International Conference on Neural Information Processings (2013)