+ Publications 研究業績

:// Papers 論文

  • Callan, D. E., Tresols, J. J. T., Laguerta, J., & Ishii, S. (2024). Shredding artifacts: extracting brain activity in EEG from extreme artifacts during skateboarding using ASR and ICA. Frontiers in Neuroergonomics, section Neurotechnology and Systems Neuroergonomics, (to appear).
  • Katayama, R., Shiraki, R., Ishii, S., & Yoshida, W. (2024). Belief inference for hierarchical hidden states in spatial navigation. Communucations Biology, 7(1), 614.
  • Hwang, J., & Ishii, S. (2024). Simplified physical model-based balance-preserving motion retargeting for physical simulation. Computer Graphics Forum, 43(1), e14996.
  • Mitsuhashi, S., & Ishii, S. (2023). Triangle inequality for inverse optimal control. IEEE Access, 11, 119187-119199.
  • Ohashi, K., Nakanishi, K., Yasui, Y., & Ishii, S. (2023). Deep adversarial reinforcement learning to make policy robust against worst-case value prediction. IEEE Access, 11, 100798-100809.
  • Skibbe, H., Rachmadi, M. F., Nakae, K., Gutierrez, C. E., Hata, J., Tsukada, H., Poon, C., Doya, K., Majka, P., Rosa, M. G., Okano, H., Yamamori, T., Ishii, S., Reisert, M., & Watakabe, A. (2023). The Brain/MINDS marmoset connectivityresource: an open access platform for cellular-level tracing and tractography in the primate brain. PLoS Biology, 21(6), e3002158
  • Watakabe, A., Skibbe, H., Nakae, K., Abe, H., Ichinohe, N., Rachmadi, F. M., Wang, J., Takaji, M., Mizukami, H., Woodward, A., Gong, R., Hata, J., Okano, H., Ishii, S., & Yamamori, T. (2023). Local and long-distance organization of prefrontal cortex circuits in the marmoset brain. Neuron, 111, 1-16.
  • Callan, D. E., Fukada, T., Dehais, F., & Ishii, S. (2023). The role of brain localized gamma and alpha oscillations in inattentional deafness: implications for understanding human attention. Frontiers in Human Neuroscience, section Cognitive Neuroscience, 17, https://doi.org/10.3389/fn-hum.2023.1168108
  • Hata,J., Nakae,K., Tsukada,H., Woodward,A., Haga,Y., Iida,M., Uematsu,A., Seki,F., Ichinohe,N., Gong,R., Kaneko,T., Yohimaru,D., Watakabe,A., Abe,H., Tani,T., Hamada,T., Gutierrez, C,E., Skibbe,H., Maeda,M.,Papazian,F., Hagiya,K., Ishii,S., Doya,K., Shimogori,T., Yamamori,T., Okano,H,J., & Okano,H. (2023).Multi-modal brain magneric resonance imaging database covering marmosets with a wide age range. Scientific Data,,10(1),211, https://doi.org/10.1038/s41597-023-02121-2
  • Fujimoto, K., Hayashi, K., Katayama, R., Lee, S., Liang, Z., Yoshida, W., Ishii, S. (2022). Deep learning-based image deconstruction method with maintained saliency. Neural Networks, 155:224-241. https://doi.org/10.1016/j.neunet.2022.08.015 2023 年日本神経回路学会論文賞受賞.
  • Lee, S., Kume, H., Urakubo, H., Kasai, H., Ishii, S. (2022). Tri-view two-photon microscopic image registration and deblurring with convolutional neural networks. Neural Networks, 152:57-69. https://doi.org/10.1016/j.neunet.2022.04.011
  • Katayama, R., Yoshida, Y., & Ishii, S. (2022). Confidence modulates the decodability of scene prediction during partially-observable maze exploration in humans.Communications Biology, 5: 367. doi: 10.1038/s42003-022-03314-y.
  • Yamaguchi, K., Maeda, Y., Sawada, T., Iino, Y., Tajiri, M., Nakazato, R., Ishii, S., Kasai, H., & Yagishita, S. (2022). A behavioural correlate of the synaptic eligibility trace in the nucleus accumbens. Scientific Reports, 12:1921. https://doi.org/10.1038/s41598-022-05637-6
  • Hwang, J., Park, G., Kwon, T. and Ishii, S. (2022). Transition Motion Synthesis for Object Interaction based on Learning Transition Strategies. Computer Graphics Forum, https://doi.org/10.1111/cgf.14499
  • Ohashi, K., Nakanishi, K., Sasaki, W., Yasui, Y., & Ishii, S. (2021). Deep adversarial reinforcement learning with noise compensation by auto-encoder. IEEE Access, https://doi.org/10.1109/access.2021.3121751
  • Kita, Y., Nishibe, H.,Wang, Y.,Hashikawa, T., Kikuchi, S., Mami, U., Yoshida, A., Yoshida, C.,Kawase, T., Ishii, S., Skibbe, H., Shimogori, T., (2021). Cellular-resolution gene expression profiling in the neonatal marmoset brain reveals dynamic species- and region-specific differences. Proceedings of the National Academy of the USA, 118 (18), e2020125118.https://doi.org/10.1073/pnas.2020125118
  • Urakubo, H., Yagishita, S., Kasai, H., Kubota, Y., Ishii, S., (2021). The critical balance between dopamine D2 receptor and RGS for the sensitive detection of a transient decay in dopamine signal. PLoS Computational Biology, 17(9): e1009364. https://doi.org/10.1371/journal.pcbi.1009364
  • Kubo, A., Meshgi, K., & Ishii, S. (2021). A meta-Q-learning approach to discriminative correlation filter based visual tracking. Journal of Intelligence and Robotic Systems, 101(1), 1-11.
  • Liang, Z., Li, F., Hu, W., Huang, G., Oba, S., Zhang, Z., & Ishii, S. (2020). A generalized encoding system of alpha oscillations through visual saliency analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(12), 2731-2743
  • Gutierrez, C. E., Skibbe, H., Nakae, K., Tsukada, H., Lienard, J., Watakabe, A., Hata, J., Reisert, M., Woodward A., Yamaguchi, Y., Yamamori, T., Okano, H., Ishii, S. & Doya, K. (2020). Optimization and validation of diffusion MRIbased fiber tracking with neural tracer data. Scientific Reports, 10, 21285, https://doi.org/10.1038/s41598-020-78284-4
  • Hwang J., Ishii, S., Kwon, T., & Oba, S. (2020). Modularized predictive coding-based online motion synthesis combining environmental constraints and motion data. IEEE Access, 8, 10.1109/ACCESS.2020.3036449
  • Parajuli, L. K., Urakubo, H., Takahashi-Nakazato, A., Ogelman, R., Iwasaki, H., Koike, M., Kwon, H. B., Ishii, S., Oh, W. C., Fukazawa, Y., & Okabe, S. (2020). Geometry and the organization principle of spine synapses along a dendrite. eNeuro, 0248-20, https://doi.org/10.1523/ENEURO.0248-20.2020
  • Urakubo, H., Yagishita, S., Kasai, H., & Ishii, S. (2020). Signaling models for dopamine-dependent temporal contiguity in striatal synaptic plasticity. PLoS Computational Biology, 16(7), e1008078, https://doi.org/10.1371/journal.pcbi.1008078
  • Fujita, Y., Yagishita, S., Kasai, H., Ishii, S. (2020). Computational characteristics of the striatal dopamine system described by reinforcement learning with fast generalization. Frontiers in Computational Neuroscience. https://doi.org/10.1101/2019.12.12.873950
  • Woodward, A., Gong, R., Abe, H., Nakae, K., Hata, J., Skibbe, H., Yamaguchi, Y., Ishii, S., Okano, H., Yamamori, T., & Ichinohe, N. (2020). The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain. Brain Structure and Function, 225, 1225-1243, https://doi.org/10.1007/s00429-020-02073-y
  • Ishii, S., Lee, S., Urakubo, H., Kume, H., & Kasai, H. (2020). Generative and discriminative model-based approaches to microscopic image restoration and segmentation. Microscopy, 69(2), 79-91, https://doi.org/10.1093/jmicro/dfaa007.
  • Iino, Y., Sawada, T., Yamaguchi, K., Tajiri, M., Ishii, S., Kasai, H., & Yagishita, S. (2020). Dopamine D2 receptors in discrimination learning and spine enlargement. Nature, 579, 555-560, doi: 10.1038/s41586-020-2115-1
  • Lee, S., Negishi, M., Urakubo, H., Kasai, H., & Ishii, S. (2020). Mu-net: multi-scale U-net for two-photon microscopy image denoising and restoration. Neural Networks, 125, 92-103, doi: 10.1016/j.neunet.2020.01.026
  • Nishimoto, T., Higashi, H., Morioka, H., & Ishii, S. (2020). An EEG-based personal identification method using unsupervised feature extraction and its robustness against intra-subject variability. Journal of Neural Engineering, 17(2), 026007.
  • Urakubo, H., Bullmann, T., Kubota, Y., Oba, S., & Ishii, S. (2019). UNI-EM: an environment for deep neural network-based automated segmentation of neuronal electron microscopic images. Scientific Reports. 9, 19413.
  • Ohnishi, S., Uchibe, E., Yamaguchi, Y., Nakanishi, K., Yasui, Y. & Ishii, S. (2019). Con-strained deep Q-learning gradually approaching ordinary Q-learning. Frontiers in Neurorobotics, 13, https://doi.org/10.3389/fnbot.2019.00103.
  • Yoon, Y., Park, J., Taniguchi, A., Kohsaka, H., Nakae, K., Nonaka, S., Ishii, S., & Nose, A. (2019). System level analyses of motor-related neural activities in larval Drosophila. Journal of Neurogenetics, 33(3), 179-189, doi: 10.1080/01677063.2019.1605365.
  • Fujii, M., Murakami, Y., Karasawa, Y., Sumitomo, Y., Fujita, S., Koyama, M., Uda, S., Kubota, H., Inoue, H., Konishi, K., Oba, S., Ishii, S., & Kuroda, S. Logical design of oral glucose ingestion pattern minimizing blood glucose in humans. (2019). npj Systems Biology and Applications, 5, 31, https://doi.org/10.1038/s41540- 019-0108-1
  • Miyato, T., Maeda, S., Koyama, M., & Ishii, S. (2019). Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(8), 1979-1993.
  • Liang, Z., Oba, S., & Ishii, S. (2019). An unsupervised EEG decoding system for human emotion recognition. Neural Networks, 116, 257-268.
  • Ito, R., Nakae, K., Hata, J., Okano, H., & Ishii, S. (2019). Semi-supervised learning of brain tissue segmentation. Neural Networks, 116, 25-34.
  • Naoki, H., Akiyama, R., Sari, D. W. K, Ishii, S., Bessho, Y., & Matsui, T. (2019). Noise-resistant developmental reproducibility in vertebrate somite formation. PLoS Computational Biology, 15(12), e1006579, doi: 10.1371/journal.pcbi.1006579
  • Skibbe, H., Reisert, M., Nakae, K., Watakabe, A., Hata, J., Okano, H., Yamamori, T., & Ishii, S. (2019). PAT:Probabilistic axon tracking for densely labeled neurons in large 3D micrographs. IEEE Transactions on Medical Imaging, 38(1): 69-78. doi: 10.1109/TMI.2018.2855736.
  • Kobayashi, C., Okamoto, K., Urakubo, H., Funayama, K., Ishikawa, T., Szymanska, A. F., Ishii, S. & Ikegaya, Y. (2018). GABAergic inhibition reduces the impact of synaptic excitation on somatic excitation. Neuroscience Research, doi: 10.1016/j.neures.2018.09.014.
  • Fuchigami, T., Shikauchi, Y., Nakae, K., Shikauchi, M., Ogawa, T., Ishii, S. (2018). Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging. Scientific Reports, 8(1): 12342. doi: 10.1038/s41598-018-30676-3.
  • Liang, Z., Hamada, Y., Oba, S. & Ishii, S. (2018). Characterization of electroencephalography signals for estimating saliency features in videos. Neural Networks,105, 52-64.
  • Yamaguchi, S., Naoki, H., Ikeda, M., Tsukada, Y., Nakano, S., Mori, I., & Ishii, S. (2018). Thermotactic behavioral strategy of C. elegans identified using inverse reinforcement learning. PLoS Computational Biology,14(5): e1006122. https://doi.org/10.1371/journal.pcbi.1006122.
  • Kondo, Y., Aoki, K., & Ishii, S. (2018). Inverse tissue mechanics of cell monolayer expansion. PLoS Computational Biology,14(3):e1006029. https://doi.org/10.1371/journal.pcbi.1006029.
  • Yamada, T., Nishiyama, M., Oba, S., Jimbo, H. C., Ikeda, K., Ishii, S., Hong, K., & Sakumura, Y. (2018). Computational methods for estimating molecular system from membrane potential recordings in neuronal growth cone. Scientific Reports, 8, 4559, doi: 10.1038/s41598-018-22506-3.
  • Takenouchi, T., & Ishii, S. (2017). Binary classifiers ensemble based on Bregman divergence for multi-class classification. Neurocomputing, 273(17),424-434.
  • Murakami, Y., Koyama, M., Oba, S., Kuroda, S., & Ishii, S. (2017). Model-based control of the temporal patterns of intracellular signaling in silico. Biophysics and Physicobiology, 14, 29-40.
  • Meshgi, K., Maeda, S., Oba, S., & Ishii, S. (2017). Constructing a meta-tracker using dropout to imitate the behavior of an arbitrary black-box tracker. Neural Networks, 87, 132-148.
  • Shikauchi, Y., & Ishii, S. (2016). Robust encoding of scene anticipation during human spatial navigation. Scientific Reports, 6, 37599. doi:10.1038/srep37599.
  • Naoki, H., Nishiyama, M., Togashi, K., Igarashi, Y., Hong, K., & Ishii, S. (2016). Multi-phasic bi-directional chemotactic responses of the growth cone. Scientific Reports, 6, 36256. doi:10.1038/srep36256.
  • Yamao, M., Aoki, K., Yukinawa, N., Ishii, S., Matsuda, M., & Naoki, H. (2016). Two new FRET imaging measures: linearly proportional to and highly contrasting the fraction of active molecules. PLoS ONE, 11(10), e0164254. doi:10.1371/journal.pone.0164254.
  • Abdur-Rahim, J.A., Morales, Y., Gupta, P., Umeta, I., Watanabe, A., Even, J., Suyama, T., & Ishii, S. (2016). Multi-sensor based state prediction for personal mobility vehicles. PLoS ONE, 11(10), e0162593. doi: 10.1371/journal.pone.0162593.
  • Li, Y., Nakae, K., Ishii, S., & Naoki, H. (2016). Uncertainty-dependent extinction of fear memory in an amygdala-mPFC neural circuit model. PLoS Computational Biology, 12(9), e1005099. doi: 10.1371/journal.pcbi.1005099.
  • Meshgi, K., Maeda, S., Oba, S., Skibbe, H., Li, Y., & Ishii, S. (2016). An occlusion-aware particle filter tracker to handle complex and persistent occlusions. Computer Vision and Image Understanding, 150, 81-94.
  • Oba, S., Nakae, K., Ikegaya, Y., Aki, S., Yoshimoto, J., & Ishii, S. (2016). Empirical Bayesian significance measure of neuronal spike response. BMC Neuroscience, 17. doi: 10.1186/s12868-016-0255-x.
  • Hirayama, J., Hyvarinen, A., & Ishii, S. (2016). Sparse and low-rank matrix regularization for learning time-varying Markov networks. Machine Learning, doi: 10.1007/s10994-016-5568-6.
  • Tsukada, Y., Yamao, M., Naoki, H., Shimowada, T., Ohnishi, N., Kuhara, A., Ishii, S., & Mori, I. (2016). Reconstruction of spatial thermal gradient encoded in thermosensory neuron AFD in Caenorhabditis elegans. Journal of Neuroscience, 36(9), 2571-2581.
  • Yamao, M., Naoki, H., Kunida, K., Aoki, K., Matsuda, M., & Ishii, S. (2015). Distinct predictive performance of Rac1 and Cdc42 in cell migration. Scientific Reports, 5, 17527. doi: 10.1038/srep17527.
  • Shikauchi, Y., & Ishii, S. (2015). Decoding the view expectation during learned maze navigation from human fronto-parietal network. Scientific Reports, 5, 17648. doi: 10.1038/srep17648.
  • Asada, A., Ujita, S., Nakayama, R., Oba, S., Ishii, S., Matsuki, N., & Ikegaya, Y. (2015). Subtle modulation of ongoing calcium dynamics in astrocytic microdomains by sensory inputs. Physiological Reports, 3(10), e12454. doi: 10.14814/phy2.12454.
  • Meshgi, K., & Ishii, S. (2015). The state-of-the-art in handling occlusions for visual object tracking. IEICE Transactions on Information & Systems, E98-D(7), 1260-1274.
  • Morioka, H., Kanemura, A., Hirayama, J., Shikauchi, M., Ogawa, T., Ikeda, S., Kawanabe, M., & Ishii, S. (2015). Learning a common dictionary for subject-transfer decoding with resting calibration. NeuroImage, 111, 167-178.
  • Skibbe, H., Reisert, M., Maeda, S., Koyama, M., Oba, S., Ito, K., & Ishii, S. (2015). Efficient Monte Carlo image analysis for the location of vascular entity. IEEE Transactions on Medical Imaging, 34(2), 628-643.
  • Hayashi, Y., Ishii, S., & Urakubo, H. (2014). A computational model of afterimage rotation in the peripheral drift illusion based on retinal ON/OFF responses. PLoS ONE, 9(12), e115464. doi: 10.1371/journal.pone.0115464.
  • Ahamed, T., Kawanabe, M., Ishii, S., & Callan, D. (2014). Structural differences in gray matter between glider pilots and non-pilots. A voxel-based morphometry study. Frontiers in Neurology, 5, 248. doi: 10.3389/fneur.2014.00248.
  • Nakae, K., Ikegaya, Y., Ishikawa, T., Oba, S., Urakubo, H., Koyama, M. & Ishii, S. (2014). A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging. PLoS Computational Biology, 10(11), e1003949. doi: 10.1371/journal.pcbi.1003949.
  • Yagishita, S., Hayashi-Takagi, A., Ellis-Davies, G.C.R., Urakubo, H., Ishii, S., & Kasai, H. (2014). A critical time window for dopamine actions on the structural plasticity of dendritic spines. Science, 345(6204), 1616-1620.
    • 2015年日本神経回路学会論文賞受賞.
  • Kunkel, S., Schmidt, M., Eppler, J.M., Plesser, H.E., Masumoto, G., Igarashi, J., Ishii, S., Fukai, T., Morrison, A., Diesmann, M., & Helias, M. (2014). Spiking network simulation code for petascale computers. Frontiers in Neuroinformatics, 8, 78. doi: 10.3389/fninf.2014.00078.
  • 中野太智, 前田新一, 石井信. (2014). 状態非依存の方策を用いた新しい強化学習手法の提案. システム制御情報学会論文誌, 27(8), 327-332, 2015年システム制御情報学会賞論文賞受賞.
  • Naoki, H., & Ishii, S. (2014). Mathematical modeling of neuronal polarization during development. Progress in Molecular Biology and Translational Science, 41, 123-127.
  • Urakubo, H., Sato, M., Ishii, S., & Kuroda, S. (2014). In vitro reconstitution of a CaMKII memory switch by an NMDA receptor-derived peptide. Biophysical Journal, 106(6), 1414-1420.
  • Morioka, H., Kanemura, A., Morimoto, S., Yoshioka, T., Oba, S., Kawanabe, M., & Ishii, S. (2014). Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information. NeuroImage, 90, 128-139.
  • Kouno, M., Nakae, K., Oba, S., & Ishii, S. (2012). Microscopic image restoration based on tensor factorization of rotated patches. Artificial Life and Robotics, 17(3-4), 417-425.
  • Helias, M., Kunkel, S., Masumoto, G., Igarashi, J., Eppler, J. M., Ishii, S., Fukai, T., Morrison, A., & Diesmann, M. (2012). Supercomputers ready for use as discovery machines for neuroscience. Frontiers in Neuroinformatics, doi:10.3389/fninf.2012.00026.
  • Ihara, M., Maeda, S., Ikeda, K., & Ishii, S. (2012). Low-dimensional feature representation for instrument identification. SICE Journal of Control, Measurement, and System Integration, 5(4), 249-258.
  • Kaneko-Kawano, T., Takasu, F., Naoki, H., Sakumura, Y., Ishii, S., Ueba, T., Eiyama, A., Okada, A., Kaneko, Y., & Suzuki, K. (2012). Dynamic regulation of myosin light chain phosphorylation by Rho-kinase. PLoS ONE, 7(6), e39269. doi:10.1371/journal.pone.0039269.
  • Fukushima, M., Yamashita, O., Kanemura, A., Ishii, S. Kawato, M., & Sato, M. (2012). A state-space modeling approach for localization of focal current sources from MEG. IEEE Transactions on Biomedical Engineering, 59(6), 1561-1571.
  • Ueno, T., Maeda, S., & Ishii, S. (2012). Asymptotic analysis of value prediction by well-specified and misspecified models. Neural Networks, 31, 88-92.
  • Yamao, M., Naoki, H., & Ishii, S. (2011). Multi-cellular logistics of collective cell migration. PLoS One, 6(12), e27950. doi:10.1371/journal.pone.0027950.
  • Takahashi, N., Oba, S., Yukinawa, N., Ujita, S., Mizunuma, M., Matsuki, N., Ishii, S., & Ikegaya, Y. (2011). High-speed multineuron calcium imaging using Nipkow-type confocal microscopy. Current Protocols in Neuroscience, 2:Unit2.14, 1-10.
  • Takenouchi, T., & Ishii, S. (2011). Ternary Bradley-Terry model-based decoding for multi-class classification and its extensions. Machine Learning, 85(3), 249-272.
  • Mori, T., & Ishii, S. (2011). Incremental state aggregation for value function estimation in reinforcement learning. IEEE Transactions on Systems, Man and Cybernetics, Part B, 41(5), 1407-1416.
  • Kim, W., Matsui, T., Yamao, M., Ishibashi, M., Tamada, K., Takumi, T., Kohno, K., Oba, S., Ishii, S., Sakumura, Y., Bessho, Y. (2011). The period of the somite segmentation clock is sensitive to Notch activity. Molecular Biology of the Cell, 22(18), 3541-3549.
  • Nonaka, S., Naoki, H., & Ishii, S. (2011). A multiphysical model of cell migration integrating reaction-diffusion, membrane and cytoskelton. Neural Networks, 24(9), 979-989.
  • Ueno, T., Maeda, S., Kawanabe, M., & Ishii, S. (2011). Generalized TD Learning. Journal of Machine Learning Research, 12(6), 1977-2020.
  • Nishiyama, M., Togashi, K., von Schimmelmann, M. J., Lim, C.-J., Maeda, S., Yamashita, N., Goshima, Y., Ishii, S., & Hong, K. (2011). Semaphorin 3A induces Ca(V)2.3 channel-dependent conversion of axons to dendrites. Nature Cell Biology, 13(6), 676-686.
  • Yoshimoto, J., Sato, M., & Ishii, S. (2011). Bayesian normalized gaussian network and hierarchical model selection method. Intelligent Automation and Soft Computing, 17(1), 71-94.
  • Naoki, H., Nakamuta, S., Kaibuchi, K., & Ishii, S. (2011). Flexible search for single-axon morphology during neuronal spontaneous polarization. PLoS ONE 6(4): e19034, doi:10.1371/journal.pone.0019034.
  • Hotta, S., Oba, S., & Ishii, S. (2010). Visual attention model involving feature-based inhibition of return. Artificial Life and Robotics, 15(2), 129-132.
  • Toriyama, M., Sakumura, Y., Shimada, T., Ishii, S., & Inagaki, N. (2010). A diffusion-based neurite length-sensing mechanism involved in neuronal symmetry breaking. Molecular Systems Biology, 6(1), 394, doi:10.1038/msb.2010.51.
  • Maeda, S., Fukuda, W., Kanemura, A., & Ishii, S. (2010). Maximum a posteriori X-ray computed tomography using graph cuts. Journal of Physics, Conference Series, 233(1),012023, doi:10.1088/1742-6596/233/1/012023.
  • Kanemura, A., Maeda, S., & Ishii, S. (2010). Sparse Bayesian learning of filters for efficient image expansion. IEEE Transactions on Image Processing, 19(6)1480.
  • Yoshida, W., Funakoshi, H., & Ishii, S. (2010). Hierarchical rule switching in prefrontal cortex. NeuroImage, 50(1), 314-322.
  • Kanemura, A., Maeda, S., Fukuda, W., & Ishii, S. (2010). Bayesian image superresolution and hidden variable modeling. Journal of Systems Science and Complexity, 23(1), 116-136.
  • Oba, S., & Ishii, S. (2009). Differential gene detection incorporating common expression patterns. Journal of Physics, Conference Series, 197(1), 012007.
  • Shibata, K., Yamagishi, N., Ishii, S., & Kawato, M. (2009). Boosting perceptual learning by fake feedback. Vision Research, 49(21), 2574-2585.
  • 兼村厚範, 前田新一, 石井信. (2009). 複層マルコフ確率場を事前分布とする超解像法におけるハイパパラメータ推定. 電子情報通信学会論文誌, J92-D(10), 1802-1811.
  • Kanemura, A., Maeda, S., & Ishii, S. (2009). Superresolution with compound Markov random fields via the variational EM algorithm. Neural Networks, 22(7), 1025-1034, 2010年日本神経回路学会論文賞受賞.
  • Maeda, S., & Ishii, S. (2009). Learning a multi-dimensional companding function for lossy source coding. Neural Networks, 22(7), 998-1010.
  • Suzuki, I., Takenouchi, T., Ohira, M., Oba, S., & Ishii, S. (2009). Robust model selection for classification of microarrays. Cancer Informatics, 7, 141-157.
  • Takenouchi, T., & Ishii, S. (2009). A multiclass classification method based on decoding of binary classifiers. Neural Computation, 21(7), 2049-2081.
  • Oba, S., & Ishii, S. (2009). Optimal sufficient statistics for parametric and non-parametric multiple simultaneous hypothesis testing. International Journal of Biostatistics, 5(1), 1557-4679.
  • Yukinawa, N., Yoshioka, T., Kobayashi, K., Ogasawara, N., & Ishii, S. (2009). A constrained Gaussian mixture model for correlation-based cluster analysis of gene expression data. IPSJ Transactions on Bioinformatics, 2, 47-62.
  • Yukinawa, N., Oba, S., Kato, K., & Ishii, S. (2009). Optimal aggregation of binary classifiers for multiclass cancer diagnosis using gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6(2), 333-343.
  • Yano, N., Shibata, T., & Ishii, S. (2009). Adaptive particle allocation for multifocal visual attention based on particle filtering. Journal of Artificial Life and Robotics, 13(2), 522-525.
  • Ihara, M., Maeda, S., & Ishii, S. (2009). Solo instrumental music analysis using the source-filter model as a sound production model considering temporal dynamics. Neural Computing & Applications, 18(1), 3-14.
  • Shirahata, M., Oba, S., Iwao-Koizumi, K., Saito, S., Ueno, N., Oda, M., Hashimoto, N., Ishii, S., Takahashi, J. A., & Kato, K. (2009). Using gene expression profiling to identify a prognostic molecular spectrum in gliomas. Cancer Science, 100(1), 165-172.
  • Naoki, H., Sakumura, Y., & Ishii, S. (2008). Stochastic control of spontaneous signal generation for gradient sensing in chemotaxis. Journal of Theoretical Biology, 255(2), 259-266.
  • Tsukada, Y., Aoki, K., Nakamura, T., Sakumura, Y., Matsuda, M., & Ishii, S. (2008). Quantification of local morphodynamics and local GTPase activity by edge evolution tracking. PLoS Computational Biology, 4(11), doi:10.1371/journal.pcbi.1000223.
  • Hirayama, J., Nakatomi, M., Takenouchi, T., & Ishii, S. (2008). Collaborative prediction by multiple Bayesian networks and its application to printer usage modeling. Behaviometrika, 35(2), 99-114.
  • Tamei, T., Ishii, S., & Shibata, T. (2008). Virtual force/tactile sensors for interactive machines using the user's biological signals. Advanced Robotics, 22(8), 893-911.
  • Shikauchi, M., Ishii, S., & Shibata, T. (2008). Prediction of aperiodic target sequences by saccades. Behavioural Brain Research, 189(2), 325-331.
  • Hirayama, J., Nakatomi, M., Takenouchi, T., & Ishii, S. (2008). α-Bayesian collaboration of multiple predictors and its applications to hybrid recommendation and user modeling. Neural Information Processing - Letters and Reviews, 12(1-3), 11-20.
  • Yukinawa, N., Takenouchi, T., Oba, S., & Ishii, S. (2008). Combining multiple decisions: applications to bioinformatics. Journal of Physics, Conference Series, 95(1), 12-18.
  • Osaga, S., Hirayama, J., Takenouchi, T., & Ishii, S. (2008). A probabilistic modeling of MOSAIC learning. Journal of Artificial Life and Robotics, 12(1-2), 167-171.
  • Makino, K., Nakamura, Y., Shibata, T., & Ishii, S. (2008). Adaptive control of a looper-like robot based on the CPG-actor-critic method. Journal of Artificial Life and Robotics, 12(1-2), 129-132.
  • Tomioka, N., Oba, S., Ohira, M., Misra, A., Fridlyland, J., Ishii, S., Nakamura, Y., Isogai, E., Hirata, T., Yoshida, Y., Todo, S., Kaneko, Y., Albertson, D.G., Pinkel, D., Feuerstein, B.G., & Nakagawara, A. (2007). Novel risk stratification of patients with neuroblastoma by genomic signature, which is independent of molecular signature. Oncogene, 27(4), 441-449.
  • Fujita, H., & Ishii, S. (2007). Model-based reinforcement learning for partially observable games with sampling-based state estimation. Neural Computation, 19(11), 3051-3087.
  • Hirayama, J., Maeda, S., & Ishii, S. (2007). Markov and semi-Markov switching of source appearances for nonstationary independent component analysis. IEEE Transactions on Neural Networks, 18(5), 1326-1342.
  • Nakamura, Y., Mori, T., Sato, M., & Ishii, S. (2007). Reinforcement learning for a biped robot based on a CPG-actor-critic method. Neural Networks, 20(6), 723-735.
  • Tanabe, A., Fukumizu, K., Oba, S., Takenouchi, T., & Ishii, S. (2007). Parameter estimation for von Mises-Fisher distributions. Computational Statistics, 22(1), 145-157.
  • Hitomi, K., Shibata, T., Nakamura, Y., & Ishii, S. (2006). Reinforcement learning for quasi-passive dynamic walking of an unstable biped robot. Journal of Robotics and Autonomous Systems, 54(12), 982-988.
  • Oba, S., Tomioka, N., Ohira, M., & Ishii, S. (2006). Combfit: a normalization method for array CGH data. IPSJ Transactions on Bioinformatics, 47(SIG 17), 73-82.
  • 坂東誉司, 柴田智広, 清水幹郎, 石井信. (2006). 適応的サンプリングによる階層モデル化された対象の効率的状態推定. システム制御情報学会論文誌, 19(10), 369-377.
  • Igarashi, Y., Sakumura, Y., & Ishii, S. (2006). The role of short-term depression in sustained neural activity in the prefrontal cortex: A simulation study. Neural Networks, 19(8), 1137-1152.
  • Bando, T., Shibata, T., Doya, K., & Ishii, S. (2006). Switching particle filters for efficient visual tracking. Journal of Robotics and Autonomous Systems, 54(10), 873-884.
  • Oba, S., & Ishii, S. (2006). Semi-supervised discovery of differential genes. BMC Bioinformatics, 7:414.
  • Hirayama, J., Yoshimoto, J., & Ishii, S. (2006). Balancing plasticity and stability of on-line learning based on hierarchical Bayesian adaptation of forgetting factors. Neurocomputing, 69(16-18), 1954-1961.
  • Motoori, M., Takemasa, I., Doki, Y., Saito, S., Miyata, H., Takiguchi, S, Fujiwara, Y., Yasuda, T., Yano, M., Kurokawa, Y., Komori, T., Yamasaki, M., Ueno, N., Oba, S., Ishii, S., Monden, M. & Kato, K. (2006). Prediction of peritoneal metastasis in advanced gastric cancer by gene expression profiling of the primary site. European Journal of Cancer, 42(12), 1897-1903.
  • Yukinawa, N., Oba, S., Kato, K., Taniguchi, K., Iwao-Koizumi, K., Tamaki, Y., Noguchi, S., & Ishii, S. (2006). A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors. BMC Genomics, 7:190.
  • Yoshida, W., & Ishii, S. (2006). Resolution of uncertainty in prefrontal cortex. Neuron, 50(5), 781-789, 2007年日本神経回路学会論文賞受賞.
  • Sakumura, Y., & Ishii, S. (2006). Stochastic resonance with differential code in feedforward network with intra-layer random connections. Neural Networks, 19(4), 469-476.
  • 森健, 中村泰, 石井信. (2006). 重点サンプリングに基づくNatural Actor-Critic 法による効果的なサンプルの再利用. 電子情報通信学会論文誌, J89-D(5), 954-966.
  • Honda, N., Sakumura, Y., & Ishii, S. (2005). Local signaling with molecular diffusion as a decoder of Ca2+ signals in synaptic plasticity. Molecular Systems Biology,1(1), doi:10.1038/msb4100035.
  • Nakamura, Y., Mori, T., Tokita, Y., Shibata, T., & Ishii, S. (2005). Off-policy natural policy gradient method for a biped walking using a CPG controller. Journal of Robotics and Mechatronics, 17(6), 636-644.
  • 藤田肇, 石井信. (2005). 部分観測カードゲームのためのモデル同定型強化学習. 電子情報通信学会論文誌, J88-D-II(11), 2277-2287.
  • Sakumura, Y., Tsukada, Y., Yamamoto, N., & Ishii, S. (2005). A molecular model for axon guidance based on cross talk between Rho GTPases. Biophysical Journal, 89(2), 812-822.
  • Yoshimoto, J., Nishimura, M., Tokita, Y., & Ishii, S. (2005). Acrobot control by learning the switching of multiple controllers. Journal of Artificial Life and Robotics, 9(2), 67-71.
  • 行縄直人, 吉本潤一郎, 大羽成征, 石井信. (2005). 線形ダイナミカルシステムモデルの変分ベイズ推定による遺伝子発現時系列のシステム同定. 情報処理学会論文誌：数理モデル化と応用, 46(SIG 10), 57-65.
  • 森健, 中村泰, 石井信. (2005). 方策こう配法に基づく強化学習法と二足歩行運動制御への応用. 電子情報通信学会論文誌, J88-D-II(6), 1080-1089.
  • 西村政哉, 吉本潤一郎, 時田陽一, 中村泰, 石井信. (2005). 複数制御器の切換学習法による実アクロボットの制御. 電子情報通信学会論文誌, J88-A(5), 646-657.
  • Ishii, S., Fujita, H., Mitsutake, M., Yamazaki, T., Matsuda, J., & Matsuno, Y. (2005). A reinforcement learning scheme for a partially-observable multi-agent game. Machine Learning, 59(1-2), 31-54.
  • Ishihara, Y., Ishii, S., Seki, H., & Ito, M. (2005). Temporal reasoning about two concurrent sequences of events. SIAM Journal on Computing, 34(2), 498-513, 第 21回電気通信普及財団賞 (テレコムシステム技術賞 )受賞.
  • Ohira, M., Oba, S., Nakamura, Y., Isogai, E., Kaneko, S., Nakagawa, A., Hirata, T., Kubo, H., Goto, T., Yamada, S., Yoshida, Y., Fuchioka, M., Ishii, S., & Nakagawara, A. (2005). Expression profiling using a tumor-specific cDNA microarray predicts the prognosis of intermediate risk neuroblastomas. Cancer Cell, 7(4), 337-350.
  • Kita-Matsuo, H., Yukinawa, N., Matoba, R., Saito, S., Oba, S., Ishii, S., & Kato, K. (2005). Adaptor-tagged competitive polymerase chain reaction: amplification bias and quantified gene expression levels. Analytical Biochemistry, 339(1), 15-28.
  • Motoori, M., Takemasa, I., Yano, M., Saito, S., Miyata, H., Takiguchi, S., Fujiwara, Y., Yasuda, T., Doki, Y., Kurokawa, Y., Ueno, N., Oba, S., Ishii, S., Monden, M., & Kato, K. (2005) Prediction of recurrence in advanced gastric cancer patients after curative resection by gene expression profiling. International Journal of Cancer, 114(6), 963-968.
  • Yoshida, W., & Ishii, S. (2005). Model-based reinforcement learning: A computational model and an fMRI study. Neurocomputing, 63C, 253-269.
  • Maeda, S., Song, W.-J., & Ishii, S. (2005). Nonlinear and noisy extension of independent component analysis: theory and its application to a pitch sensation model. Neural Computation, 17(1), 115-144.
  • Hirayama, J., Yoshimoto, J., & Ishii, S. (2004). Bayesian representation learning in the cortex regulated by acetylcholine. Neural Networks, 17(10), 1391-1400.
  • Kurokawa, Y., Matoba, R., Nagano, H., Sakon, M., Takemasa, I., Nakamori, S., Dono, K., Umeshita, K., Ueno, N., Ishii, S., Kato, K., & Monden, M. (2004). Molecular prediction of response to 5-fluorouracil and interferon-α combination chemotherapy in advanced hepatocellular carcinoma. Clinical Cancer Research, 10, 6029-6038.
  • Kurokawa, Y., Matoba, R., Takemasa, I., Nagano, H., Dono, K., Nakamori, S., Umeshita, K., Sakon, M., Ueno, N., Oba, S., Ishii, S., Kato, K., & Monden, M. (2004). Molecular-based prediction of early recurrence in hepatocellular carcinoma. Journal of Hepatology, 41(2), 284-291.
  • Amemori, K., & Ishii, S. (2004). Self-organization of delay lines by spike-time-dependent learning. Neurocomputing, 61, 291-316.
  • 中村泰, 佐藤雅昭, 石井信. (2004). 神経振動子ネットワークを用いたリズム運動に対する強化学習法. 電子情報通信学会論文誌, J87-D-II(3), 893-902.
  • 前田新一, 石井信. (2004). 学習によるproduct codeの設計. 電子情報通信学会論文誌, J87-A(3), 382-390.
  • Kurokawa, Y., Matoba, R., Takemasa, I., Nakamori, S., Tsujie, M., Nagano, H., Dono, K., Umeshita, K., Sakon, M., Ueno, N., Kita, H., Oba, S., Ishii, S., Kato, K. & Monden, M. (2003). Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study. Journal of Hepatology, 39(6), 1004-1012.
  • Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K., & Ishii, S. (2003). A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 19(16), 2088-2096.
  • 吉本潤一郎, 石井信, 佐藤雅昭. (2003). 変分法的ベイズ推定法に基づく正規化ガウス関数ネットワークと階層的モデル選択法. 計測自動制御学会論文集, 39(5), 503-512.
  • 吉本潤一郎, 石井信, 佐藤雅昭. (2003). 連続力学システムの自動制御のためのオンラインEM強化学習法. システム制御情報学会論文誌, 16(5), 209-217.
  • Oba, S., Sato, M., & Ishii, S. (2003). On-line learning methods for Gaussian processes. IEICE Transactions on Information and Systems, E86-D(3), 650-654.
  • Muro, S., Takemasa, I., Oba, S., Matoba, R., Ueno, N., Maruyama, C., Yamashita, R., Sekimoto, M., Yamamoto, H., Nakamori, S., Monden, M., Ishii, S., & Kato, K. (2003). Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data. Genome Biology, 4:R21.
  • 綴木馴, 高橋規一, 石井信. (2002). ハイブリッド型学習による隠れ素子付き連想記憶モデル. システム制御情報学会論文誌, 15(11), 600-606.
  • 新妻弘崇, 石井信. (2002). Poisson方程式に対する複数双極子の直接的推定法における動径方向分解能の向上のための重み関数の提案.電子情報通信学会論文誌, J85-A(11), 1347-1355.
  • Ishii, S., Yoshida, W., & Yoshimoto, J. (2002). Control of exploitation-exploration meta-parameter in reinforcement learning. Neural Networks, 15(4-6), 665-687.
  • 大羽成征, 佐藤雅昭, 石井信. (2002). 変分法的ベイズ推定による混合主成分分析.電子情報通信学会論文誌, J85-D-II(6), 1055-1065.
  • Ishii, S., & Sato, M. (2002). Doubly constrained network for combinatorial optimization. Neurocomputing, 43, 239-257.
  • Tamakoshi, H., & Ishii, S. (2001). Multiagent reinforcement learning applied to a chase problem in a continuous world. Journal of Artificial Life and Robotics, 5, 202-206.
  • Amemori, K., & Ishii, S. (2001). Gaussian process approach to spiking neurons for inhomogeneous Poisson inputs. Neural Computation, 13, 2763-2797.
  • Ishii, S., & Sato, M. (2001). Reconstruction of chaotic dynamics by on-line EM algorithm. Neural Networks, 14, 1239-1256.
  • 松野陽一郎, 山崎達也, 松田潤, 石井信. (2001). 相手モデル学習を取り入れたマルチエージェント系の強化学習法. 電子情報通信学会論文誌, J84-D-I(8), 1150-1159.
  • Ishii, S., & Niitsuma, H. (2000). λ-opt neural approaches to quadratic assignment problems. Neural Computation, 12(9), 2209-2225.
  • 雨森賢一, 石井信. (2000). 精緻な時空間スパイク列の自己組織化学習と想起. システム制御情報学会論文誌, 13(7), 308-317. 2002年システム制御情報学会賞論文賞受賞.
  • Amemori, K., & Ishii. S. (2000). Ensemble average and variance of a stochastic spiking neuron model. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E83-A(3), 575-578.
  • 新妻弘崇, 石井信. (2000). ニューラルネットワークによる２次割当て問題の一解法とそのダイナミクス. 電子情報通信学会論文誌, J83-A(3), 263-274.
  • 吉本潤一郎, 石井信, 佐藤雅昭. (2000). オンラインEMアルゴリズムによる強化学習法のacrobot制御への応用. 電子情報通信学会論文誌, J83-D-II(3), 1024-1033.
  • Sato, M., & Ishii, S. (2000). On-line EM algorithm for the normalized Gaussian network. Neural Computation, 12(2), 407-432.
  • 吉田和子, 石井信, 佐藤雅昭. (2000). オンラインEMアルゴリズムによるカオス力学系の学習と耐ノイズ性. 電子情報通信学会論文誌, J83-A(1), 28-37.
  • Yoshioka, T., Ishii, S., & Ito, M. (1999). Strategy acquisition for the game ‘‘Othello’’ based on reinforcement learning. IEICE Transactions on Information and Systems, E82-D(12), 1618-1626.
  • 新妻弘崇, 石井信, 伊藤実. (1999). アナログλ-optアルゴリズムを使った２次割当て問題の解法. 電子情報通信学会論文誌, J82-D-II(12), 2375-2384.
  • 新妻弘崇, 石井信, 伊藤実. (1999). 座標変換を用いたカオス最適化手法. 電子情報通信学会論文誌, J82-A(9), 1428-1436.
  • Osaka, M., Gohara, K., Ishii, S., Kishida, H., Hayakawa, H., & Ito, N. (1999). Symbolic strings and spatial 1/f spectra. Physica D, 125, 142-154.
  • Ishii, S., & Sato, M. (1998). Constrained neural approaches to quadratic assignment problems. Neural Networks, 11, 1073-1082.
  • Ishii, S., & Sato, M. (1998). Associative memory based on parametrically coupled chaotic elements. Physica D, 121, 344-366.
  • Ishii, S., & Sato, M. (1997). Chaotic Potts spin model for combinatorial optimization problems. Neural Networks, 10(5), 941-963.
  • Ishii, S. (1997). Eliminating spurious memories in a network of chaotic elements. Journal of Intelligent & Fuzzy Systems, 5, 69-83.
  • Sato, M., & Ishii, S. (1996). Bifurcations in mean-field-theory annealing. Physical Review E, 53, 5153-5168.
  • Ishii, S., Fukumizu, K., & Watanabe, S. (1996). A network of chaotic elements for information processing. Neural Networks, 9, 25-40.
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:// Books 著書

  • 石井信. (2010). 価値と学習. よくわかる認知科学 (編 乾敏郎, 吉川左紀子, 川口潤), 分担執筆 (IV-7節), 124-127,ミネルヴァ書房.
  • 石井信. (2007). 確率モデルによるヒューマンモデリングとその応用. 統計数理は隠された未来をあらわにする,分担執筆 (2章), 東京電機大学出版局.
  • Doya, K., & Ishii, S. (2007). A probability primer. In Bayesian Brain (eds., K. Doya, S. Ishii, A. Pouget, and R. P. N. Rao), MIT Press.
  • 石井信. (2006) .バイオインフォマティクス事典, 分担執筆 (「階層型ニューラルネットワーク」、「アレイデータの誤差処理」 ), 27-29, 383-385, 共立出版.
  • 佐藤雅昭, 石井信. (2005). 脳の計算機構「－ボトムアップ・トップダウンのダイナミクス－」, 分担執筆 (3章, 18-38), 朝倉書店.
  • 石井信. (2000). 脳科学大事典, 分担執筆 (III-7.1節), 朝倉書店.
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:// General Remarks 解説論文・総説論文

  • 石井信. (2015). スーパーコンピュータによる脳神経系シミュレーション. 人工知能学会誌, 30(5), 616-622
  • 平山淳一郎, 石井信. (2014). 潜在空間モデリングによる時系列からの再構成. 電子情報通信学会誌, 97(5), 399-404.
  • 前田新一, 兼村厚範, 石井信. (2011). 確率システムの立場からの画像情報処理技術. システム制御情報学会誌, 55(12), 532-538.
  • Ishii, S., Diesmann, M., & Doya, K. (2011). Editorial: Multi-scale, multi-modal neural modeling and simulation. Neural Networks, 24, 917.
  • 兼村厚範, 前田新一, 福田航, 石井信. (2010). 不確実性を手なずけるベイズ統計推測による画像超解像. 電子情報通信学会誌.
  • 兼村厚範, 前田新一, 福田航, 石井信. (2008). ベイズ超解像と階層モデリング. 日本神経回路学会誌, 15(3), 181-192.
  • Shibata, T., Hitomi, K., Nakamura, Y., & Ishii, S. (2007). Reinforcement learning for quasi-passive dynamic walking of an unstablebiped robot. In Humanoid Robots: Human-like Machines (eds. M.Mackel), pp. 211-226, I-Tech Education and Publishing.
  • 石井信. (2007). 確率モデルに基づく2値分類から多値分類へのデコード.「情報物理学の数学的構造」, 数理解析研究所講究録 1532, 11-18.
  • 石井信, 作村諭一. (2007). 神経の可塑性と発達の生体反応モデリング. ゲノム情報と生命現象の統合的理解 2007, 実験医学増刊 (2007年 2月), 羊土社, 198-204.
  • 銅谷賢治, 石井信. (2006). 学習ダイナミクスの制御と脳の物質機構. システム／制御／情報, 50(8), 303-308.
  • Ishii, S., & Yoshida, W. (2006). Reinforcement learning: machine learning and natural learning. New Generation Computing, 24, 325-350.
  • Ohira, M., Oba, S., Nakamura, Y., Hirata, T., Ishii, S., & Nakagawara, N. (2005). A review of DNA microarray analysis of human neuroblastomas. Cancer Letters, 228, 5-11.
  • 石井信, 銅谷賢治. (2005). 強化学習：理論と応用 .電子情報通信学会誌, 88(1), 804-810.
  • 吉本潤一郎, 銅谷賢治, 石井信. (2005). 強化学習の基礎理論と応用. 計測と制御, 44(5), 313-318.
  • 石井信. (2004). 部分観測環境での強化学習法とマルチエージェントゲームへの応用. システム／制御／情報, 48(9), 383-388.
  • 吉田和子, 石井信. (2004). 強化学習の脳内機構と情動による制御. 心理学評論, 47(1), 150-164.
  • 加藤菊也, 石井信. (2003). 遺伝子発現プロファイルのデータ解析. 蛋白質核酸酵素, 48(16), 2300-2309.
  • 石井信, 佐藤雅昭. (2000). 統計的手法に基づく強化学習と制御ルールの獲得. 計測と制御, 39(12), 763-768.
  • 石井信, 佐藤雅昭. (1999). 正規化ガウス関数ネットワーク、Mixture of expertsとEMアルゴリズム. 日本神経回路学会誌, 6(1), 30-40.
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:// Commentary 解説記事

  • 本田直樹, 山尾将隆, 石井信. (2014). 細胞運動のシステム同定. 生体の科学, 65(5), 公益法人金原一郎記念医学医療振興財団／医学書院, 468-469.
  • 石井信. (2010). 脳の意思決定：「自然の知」への「機械の知」からのモデル化. 科学, 80(12), 1188.
  • 石井信, 行縄直人, 大羽成征. (2006). 確率モデルに基づく遺伝子発現データの多値分類. 確率的情報処理と統計力学, 臨時別冊・数理科学(2006年9月), サイエンス社, 169-177.
  • 石井信. (2006). コミュニケーションゲームにおける相手モデルの役割. TELECOM FRONTIER, 50, 4-11.
  • 石井信. (2004). 制御理論・強化学習への展開. 数理科学(2004年3月), サイエンス社, 38-45.
  • 石井信. (2003). 不完全データの問題とバイオインフォマティクス. Computer Today, 114, 35-42.
  • 石井信. (2002). 強化学習におけるランダムさの自己調整. 日本神経回路学会誌, 9(4), 254-262.
  • 石井信. (2002). 強化学習と２足歩行学習. 脳情報数理科学の発展, 別冊・数理科学(2002年10月), サイエンス社, 118-124.
  • 石井信, 前田新一. (2002). 脳における予測と推定の仕組み. Computer Today, 110, 16-23.
  • 石井信. (1999). カオスを用いた非線形最適化. Computer Today, 92, 22-29.
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  • Callan, D., Ishii, S. (2024). 5th International Neuroergonomics Conference, (to appear).
  • Kita, H., Koyamada, S., Yamaguchi, S., & Ishii, S. (2024). A simple, solid, and reproducible baseline for bridge bidding AI. IEEE Conference on Games, (to appear).
  • Koyamada, S., Nishimori, S., & Ishii, S. (2024). A batch sequential having algorithm without performance degradation. Reinformcement Learning Conference, (to appear).
  • Song, Z., Higasgi, H., & Ishii, S. (2024). EEG markers for anticipated difficulty of future visual task. 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (to appear).
  • Katayama, R., & Ishii, S. (2024). Value-based decision-making relying on uncertain prior-level information. Computational and Systems Neuroscience, 2-063.
  • Koyamada, S., Okano, S., Nishimori, S., Murata, Y., Habara, K., Kita, H., & Ishii, S. (2023). Pgx: Hardware-accelerareted parallel game simulators for reinforcement learning. NeurIPS 2023 Track Datasets and Benchmarks.
  • Song, Z., Higasgi, H., & Ishii, S. (2023). EEG study onanticipation of difficulty for upcoming auditory task. 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.
  • Hayashi, K., Katayama, R., Fujimoto, K., Yoshida, W., & Ishii, S. (2023). The neural bases of prior and likelihood uncertainty. 2023 IBRO Neuroscience Reports, 2140.
  • Katayama, R., & Ishii, S. (2023). Value-based decision-making relying on uncertain prior information. 2023 IBRO Neuroscience Reports, 2759.
  • Goto, N., Kubo, A., Ohashi, K., Watanabe, T., Nakanishi, K., Yausi, Y., Nakamura, Y., & Ishii, S. (2023). Deep adversarial reinforcement learning and its application to adaptive control of a locomotive rpbot. 22nd World Congress of the International Federation of Automatic Control.
  • Zhou, J., Higashi, H., & Ishii, S. (2022). Data generation for missing frequencies in SSVEP-based brain computer interfaces. The 19th Pacific Rim International Conference on Artificial Intelligence, Decoding models of human emotion using brain signals.
  • Koyamada, S., Habara, S., Goto, N., Okano, S., Nishimori, S., & Ishii, S. (2022). Mjx: a framework for mahjong AI research. IEEE Conference on Games, doi: 10.1109/CoG51982.2022.9893712
  • Chen, W., Nakamura, A. T., Ding, J., Okada, N., Koike, S., Yagishita, S., Ishii, S. Kasai, H., Yotsumoto, Y., & Cai, M. B. (2022). Developmental trajectory of generalization and discrimination in human reinforcement learning. Fifth multidisciplinary conference on reinforcement learning and decsion making.
  • Katayama, R., Ishii, S., & Yoshida, W. (2019). Decoding scene prediction and its confidence during maze exploration. Joint Meeting of Brain Research Organization (IBRO).
  • Hwang, J., Oba, S., & Ishii, S. (2019). Online motion synthesis framework using a simplified physical model based on predictive coding.SIGGRAPH Eurographics Symposium on Computer Animation, https://dl.acm.org/doi/abs/10.1145/3309486.3339894
  • Liang, Z., Higashi, H., Oba, S., & Ishii, S. (2019). Brain dynamics encoding from visual input during free viewing of natural videos. International Joint Conference on Neuranl Networks. https://doi.org/10.1109/IJCNN.2019.8852478
  • Fujita, Y., & Ishii, S. (2018). Reproducing the cognitive function with the robustness against the brain structure and with the efficient learning algorithm. Computational Neuroscience 2018.
  • Liang, Z., Oba, S., & Ishii, S. (2018). Inherent Feature Connection (I-Con) Map for Liking Emotion Detection: an EEG Study. 32nd Human Computer Interaction Conference (BHCI2018). doi: 10.14236/ewic/HCI2018.209.
  • Meshgi, K., Oba, S., & Ishii, S. (2018). Efficient diverse ensemble for discriminative co-tracking. Computer Vision and Pattern Recoginition 2018 (CVPR), 4814-4823.
  • Uchida, S., Oba, S., & Ishii, S. (2017). Estimation of the change of agents behavior strategy using state-action history. Artificial Neural Networks and Machine Learning - ICANN 2017, LNCS10614, 100-107.
  • Nishimoto, T., Morioka, H., & Ishii, S. (2017). Individual identification by resting- state EEG using common dictionary learning. Artificial Neural Networks and Machine Learning - ICANN 2017, LNCS10613, 199-207.
  • Meshgi, K., Mirzaei, M. S., Oba, S., & Ishii, S. (2017). Efficient asynmetric co-tracking using uncertainty sampling. International Conference on Signal and Image Analysis Applications.
  • Meshgi, K., Oba, S., & Ishii, S. (2017). Efficient version-space reduction for visual tracking. 14th Conference on Computer and Robot Vision.
  • Meshgi, K., Oba, S., & Ishii, S. (2017). Active discriminative tracking using collective memory. IAPR International Conference on Machine Vision Applications.
  • Meshgi, K., Oba, S., & Ishii, S. (2016). Robust Discriminative Tracking via Query-by-Bagging. AVSS2016, 8-14. doi:10.1109/AVSS.2016.7738027
  • Baek, J., Oba, S., Yoshimoto, J., Doya, K., & Ishii, S. (2016). Descriptive, generative, and hybrid approaches for neural connectivity inference from neural activity data. CNS2016.
  • Fuchigami, T., Shikauchi, Y., Nakae, K., Shikauchi, M., & Ishii, S. (2016). Brain decoding can be improved by diffusion imaging based registration. 4th AINI 2016 and 14th INCF Nodes Workshop, PS-19.
  • Meshgi, K., Maeda, S., Oba, S., & Ishii, S. (2016). Data-driven Probabilistic Occlusion Mask to Promote Visual Tracking. Conference on Computer and Robot Vision 2016. CRV2016, 178-185. doi:10.1109/CRV.2016.19
  • Miyato, T., Maeda, S., Koyama, M., Nakae, K., & Ishii, S. (2016). Distributional smoothing with virtual adversarial training. International Conference on Learning Representations 2016, arXiv:1507.00677.
  • Baek, J., Oba, S., Yoshimoto, J., Doya, K., & Ishii, S. (2015). Computational complexity reduction for functional connectivity estimation in large scale neural network. The 22nd International Conference on Neural Information Processing, Lecture Notes in Computer Science, 9491, 583–591.
  • Sakurai, S., Oba, S., & Ishii, S. (2015). Inverse reinforcement learning based on behaviors of a learning agent. The 22nd International Conference on Neural Information Processing, Lecture Notes in Computer Science, 9489, 724-732.
  • Ogawa, T., Hirayama, J., Gupta, P., Moriya, H., Yamaguchi, S., Ishikawa, A., Inoue, Y., Kawanabe, M., & Ishii, S. (2015). Brain-machine interfaces for assistive smart homes: A feasible study with wearable near-infrared spectroscopy. The 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1107-1110, ISSN: 978-1-4244-9270-1/15/
  • Meshgi, K., & Ishii, S. (2015). Expanding histogram of colors with gridding to improve tracking accuracy. The 14th IAPR Conference on Machine Vision Applications, 475-479.
  • Koyamada, S., Koyama, M., Nakae, K., & Ishii, S. (2015). Principal sensitivity analysis. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, 9077, 621-632.
  • Ishii, S. (2015). Data-driven brain computer interface in real environments. The 3rd International Winter Conference on Brain-Computer Interface, 1-2.
  • Abdur-Rahim, J., Ogawa, T., Hirayama, J., & Ishii, S. (2014). Database-driven artifact detection method for EEG systems with few channels (DAD). IEEE Biomedical Circuits and Systems Conference, 5-8.
  • Koyamada, S., Shikauchi, Y., Nakae, K., & Ishii, S. (2014). Construction of subject-independent brain decoders for human fMRI with deep learning. Proceedings of the International Conference on Data Mining, Internet Computing, and Big Data, 60-68.
  • Skibbe, H., Reisert, M., & Ishii, S. (2014). Efficient Metropolis-Hasting image analysis for the location of vascular entity. Proceedings of the 36th German Conference on Pattern Recognition (GCPR), Lecture Notes in Computer Science, 8753, 421-431.
  • Okadome, Y., Nakamura, Y., Shikauchi, Y., Ishii, S., & Ishiguro, H. (2013). Fast approximation method for Gaussian process regression using hash function for non-uniformly distributed data. 23rd International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 8131, 17-25.
  • Kanemura, A., Morales, Y., Kawanabe, M., Morioka, H., Kallakuri, N., Ikeda, T., Miyashita, T., Hagita, N., & Ishii, S. (2013). A waypoint-based framework in brain-controlled smart home environments: brain interfaces, domotics, and robotics integration. IEEE/RSJ International Conference on Intelligent Robots and Systems, 865-870.
  • Nakano, D., Maeda, S., & Ishii, S. (2012). Control of a free-falling cat by policy-based reinforcement learning. International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 7553, 116-123.
  • Kouno, M., Nakae, K., Oba, S., & Ishii, S. (2012). Microscopic image restoration based on tensor factorization of rotated patches. International Symposium on Artificial Life and Robotics, 902-905.
  • Aki, S., Oba, S., Nakae, K., & Ishii, S. (2012). A sparse regression method to estimate neuronal structure from spike sequence. International Symposium on Artificial Life and Robotics, 718-721.
  • Tanaka, T., Maeda, S., & Ishii, S. (2011). Motion compensated X-ray CT algorithm for moving objects. 10th International Conference on Machine Learning and Applications, doi:10.1109/icmla.2011.97.
  • Harima, H., Oba, S., & Ishii, S. (2011). Multiple Granger causality tests for network structure estimation from time-series data. International Symposium on Artificial Life and Robotics, GS14-4.
  • Naito, S., Yukinawa, N., & Ishii, S. (2011). A simulation study of visual perceptual learning with attentional signals. International Symposium on Artificial Life and Robotics, GS11-103.
  • Hirayama, J., Hyvarinen, A., & Ishii, S. (2010). Sparse and low-rank estimation of time-varying Markov networks with alternating direction method of multipliers. ICONIP 2010, Part I (eds., K. W. Wong, et al.), Lecture Notes in Computer Science, 6443, 371-379.
  • Adomi, M., Shikauchi, Y., & Ishii, S. (2010). Hidden Markov model for human decision process in a partially observable environment. International Conference on Artificial Neural Networks 2010, Lecture Notes in Computer Science, 6353, 94-103.
  • Yamao, M., Naoki, H., & Ishii, S. (2010). Noise-induced collective migration for neural crest cells. International Conference on Artificial Neural Networks 2010, Lecture Notes in Computer Science, 6352, 155-163.
  • Fukuda, W., Maeda, S., Kanemura, A., & Ishii, S. (2010). Bayesian X-ray computed tomography using material class knowledge. International Conference on Acoustics, Speech and Signal Processing, 2126-2129.
  • Maeda, S., Fukuda, W., Kanemura, A., & Ishii, S. (2010). Maximum a posterior X-ray computed tomography using graph cuts. International Workshop on Statistical-Mechanical Informatics, 234-243.
  • Fukuda,W., Maeda, S., Kanemura, A., & Ishii, S. (2010). X-ray computed tomography using material-class modeling by Markov random field energy minimization. International Symposium on Artificial Life and Robotics, 662-665.
  • Hotta, S., Oba, S., & Ishii, S. (2010). Visual attention model involving feature-based inhibition of return. International Symposium on Artificial Life and Robotics, 533-536.
  • Nishioka, S., Maeda, S., Ueno, T., Ishiguro, H., & Ishii, S. (2010). A machine learning approach to 9-DOF arm robot control. International Symposium on Artificial Life and Robotics, 282-285.
  • Fukuda, W., Kanemura, A., Maeda, S., & Ishii, S. (2009). Superresolution from occluded scenes. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 5864, 19-27.
  • Tomizawa, H., Maeda, S., & Ishii, S. (2009). Learning of Go board state evaluation function by artificial neural networks. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 5863, 598-605.
  • Mori, T., & Ishii, S. (2009). Robust approximation in decomposed reinforcement learning. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 5863, 590-597.
  • Kanemura, A., Maeda, S., & Ishii, S. (2009). Learning color image expansion filters. IEEE International Conference on Image Processing, 357-360.
  • Oba, S., & Ishii, S. (2009). Differential gene detection incorporating common expression patterns. International Workshop on Statistical-Mechanical Informatics, 22-36.
  • Ueno, T., Maeda, S., Kawanabe, M., & Ishii, S. (2009). Optimal online learning procedures for model-free policy evaluation. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Lecture Notes in Artificial Intelligence, 5782, 473-488.
  • Mori, T., and Ishii, S. (2009). An additive reinforcement learning. International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 5768, 608-617.
  • Hayashi, K., Hirayama, J., & Ishii, S. (2009). Dynamic exponential family matrix factorization. Pacific-Asia Conference on Knowledge Discovery and Data Mining, Lecture Notes in Computer Science, 5476, 452-462.
  • Dauwels, J., Tsukada, Y., Sakumura, Y., S.Ishii, Aoki, K., Nakamura, T., Matsuda, M., Francois, V., & Cichocki, A. (2008). On the synchrony of morphological and molecular signaling events in cell migration. 15th International Conference on Neural Information Processing, Lecture Notes in Computer Science, 5506, 469-477.
  • Asahina, A., Hirayama, J., & Ishii, S. (2008). Interpreting dopamine activities in stochastic reward tasks. 15th International Conference on Neural Information Processing, Lecture Notes in Computer Science, 5506, 361-368. (APPNA Best Student Paper Award).
  • Hirayama, J., & Ishii, S. (2008). A closed-form estimator of fully visible Boltzmann machines. 15th International Conference on Neural Information Processing, Lecture Notes in Computer Science, 5507, 951-959.
  • Takenouchi, T., & Ishii, S. (2008). Ternary Bradley-Terry model-based decoding for multi-class classification. IEEE Machine Learning for Signal Processing Workshop 85, 249-272.
  • Hiei, Y., Mori, T., & Ishii, S. (2008). Self-organized reinforcement learning based on policy gradient in nonstationary environment. Artificial Neural Networks - ICANN 2008, Lecture Notes in Computer Science, 5163, I367-I476.
  • Taniguchi, Y., Mori, T., & Ishii, S. (2008). A continuous internal-state controller for partially observable Markov decision processes. Artificial Neural Networks ICANN 2008, Lecture Notes in Computer Science, 5163, I397-I406.
  • Ueno, T., Kawanabe, M., Mori, T., Maeda, S., & Ishii, S. (2008). A semi-parametric statistical approach to model-free policy evaluation. The 25th International Conference on Machine Learning, 1072-1079.
  • Oba, S., Kawanabe, M., Mueller, K.-R., & Ishii, S. (2008). Heterogeneous component analysis. Advances in Neural Information Processing Systems 21 (eds., J.C.Platt, D.Koller, Y.Singer, & S.Roweis), 1097-1104.
  • Takeda, K., Mori, T., & Ishii, S. (2008). Active sampling based on Gaussian process for reinforcement learning. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 678-681.
  • Taniguchi, Y., Mori, T., & Ishii, S. (2008). Continuous internal-state controller for a partially observable linear dynamical system. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 674-677.
  • Nanjoh, N., Mori, T., & Ishii, S. (2008). An effective reinforcement learning with automatic construction of basis functions and sequential approximation. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 662-665.
  • Furuya, M., Oba, S., & Ishii, S. (2008). Collaborative filtering based on a weighted maximum margin matrix factorization. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 543-546.
  • Maeda, C., Oba, S., Yukinawa, N., & Ishii, S. (2008). Detection of multiple overlapping string-shaped objects using spectral clustering. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 240-243.
  • Yano, N., Shibata, T., & Ishii, S. (2008). Adaptive particle allocation for multifocal visual attention based on particle filtering. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 211-214.
  • Ishihara, A., Hirayama, J., Takenouchi, T., & Ishii, S. (2008). A unified approach to collaborative and feature-based recommendation based on probabilistic latent semantic models. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 187-190.
  • Sodebayashi, K., Oba, S., & Ishii, S. (2008). Two-way factor analysis for missing value estimation of matrix data. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 85-88.
  • Nomura, H., Oba, S., & Ishii, S. (2008). Re-weighted ODP for differential gene expression analysis. Proceedings of the Thirteenth International Symposium on Artificial Life and Robotics, 67-70.
  • Kanemura, A., Maeda, S., & Ishii, S. (2007). Image superresolution under spatially structured noise. IEEE International Symposium on Signal Processing and Information Technology, 279-284.
  • Ihara, M., Maeda, S., & Ishii, S. (2007). Instrument identification in monophonic music using spectral information. IEEE International Symposium on Signal Processing and Information Technology, 607-611.
  • Takenouchi, T., & Ishii, S. (2007). A multi-class classification with a probabilistic localized decoder. IEEE International Symposium on Signal Processing and Information Technology, 861-865.
  • Tsukada, Y., Sakumura, Y., & Ishii, S. (2007). Quantitative morphodynamic analysis of time-lapse imaging by edge evolution tracking. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 4984, 817-826.
  • Hirayama, J., Nakatomi, M., Takenouchi, T., & Ishii, S. (2007). Bayesian collaborative predictors for general user modeling tasks. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 4984, 742-751.
  • Maeda, S., & Ishii, S. (2007). Optimization of parametric companding function for an efficient coding. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 4984, 713-722.
  • Shibata, T., Bando, T., & Ishii, S. (2007). Visual tracking achieved by adaptive sampling from hierarchical and parallel predictions. International Conference on Neural Information Processing, Lecture Notes in Computer Science, 4984, 604-613.
  • Ishii, S., Yukinawa, N., Takenouchi, T., & Oba, S. (2007). Combining multiple decisions: applications to bioinformatics. The International Workshop on Statistical-Mechanical Informatics, 151-161(invited).
  • Kanemura, A., Maeda, S., & Ishii, S. (2007). Edge-preserving Bayesian image superresolution based on compound Markov random fields. Artificial Neural Networks -ICANN 2007, Lecture Notes in Computer Science, 4669, II-611-620.
  • Taniguchi, Y., Mori, T., & Ishii, S. (2007). Reinforcement learning for cooperative actions in a partially observable multi-agent system. Artificial Neural Networks ICANN 2007, Lecture Notes in Computer Science, 4669, I-229-238.
  • Maeda, S., & Ishii, S. (2007). Convergence analysis of the EM algorithm and joint minimization of free energy. IEEE Machine Learning for Signal Processing Workshop, 318-323.
  • Kanemura, A., Maeda, S., & Ishii, S. (2007). Hyperparameter estimation in Bayesian image superresolution with a compound Markov random field prior. IEEE Machine Learning for Signal Processing Workshop, 181-186.
  • Tamei, T., Ishii, S., & Shibata, T. (2007). Dynamic and cooperative interaction with a robot that possesses no force/tactile sensors. 13th International Conference on Advanced Robotics, 647-652.
  • Ihara, M., Maeda, S., & Ishii, S. (2007). Estimation of the source-filter model using temporal dynamics. International Joint Conference on Neural Networks, 1803.
  • Takenouchi, T., & Ishii, S. (2007). A probabilistic decoding approach to multi-class classification. International Joint Conference on Neural Networks, 1696.
  • Takenouchi, T., & Ishii, S. (2007). Multiclass classification as a decoding problem. IEEE Symposium on Foundations of Computational Intelligence, 470-475.
  • Osaga, S., Hirayama, J., Takenouchi, T., & Ishii, S. (2007). A probabilistic model of MOSAIC. IEEE Symposium on Foundations of Computational Intelligence, 41-46.
  • Makino, K., Nakamura, Y., Shibata, T., & Ishii, S. (2007). Adaptive control of a looper-like robot based on the CPG-actor-critic method. International Symposium on Artificial Life and Robotics, GS24-2.
  • Date, T., Bando, T., Shibata, T., & Ishii, S. (2007). On-line variational PCA for adaptive visual tracking. International Symposium on Artificial Life and Robotics, GS18-5.
  • Osaga, S., Hirayama, J., Takenouchi, T., & Ishii, S. (2007). A probabilistic modeling of MOSAIC learning. International Symposium on Artificial Life and Robotics, GS16-3.
  • Ihara, M., Maeda, S., & Ishii, S. (2007). Estimation of source-filter model via acoustical feature extraction by GA-like algorithm. International Symposium on Artificial Life and Robotics, GS12-4.
  • Yamashita, T., Takenouchi, T., & Ishii, S. (2007). A multi-labeled classification based on error-correcting output coding. International Symposium on Artificial Life and Robotics, GS3-2.
  • Ueno, T., Nakamura, Y., Takuma, T., Shibata, T., Hosoda, K., & Ishii, S. (2006). Fast and stable learning of quasi-passive dynamic walking by an unstable biped robot based on off-policy natural actor-critic. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 5226-5231.
  • Fujita, H., Nakamura, Y., & Ishii, S. (2006). Feature extraction for decision-theoretic planning in partially observable environments. Artificial Neural Networks -ICANN 2006, Lecture Notes in Computation Science, 4132, I-820-829.
  • Oba, S., & Ishii, S. (2006). Semi-supervised significance score of differential gene expressions. Artificial Neural Networks -ICANN 2006, Lecture Notes in Computation Science, 4132, II-808-817.
  • Kanemura, A., Maeda, S., & Ishii, S. (2006). Bayesian super-resolution with a smooth-gap model. ECCV Workshop on Statistical Methods in Multi-Image and Video Processing (Graz, May, 2006), 85-93.
  • Tamei, T., Shibata, T., & Ishii, S. (2006). Development of learning support system for piano-keying - relationship between the activity of finger muscles and key release velocities of an expert -. The eleventh international symposium on Artificial Life and Robotics (Oita, Jan., 2006), GS7-5.
  • Shikauchi, M., Ishii, S., & Shibata, T. (2006). Prediction of the aperiodic time series of a visual target by humans. The eleventh international symposium on Artificial Life and Robotics (Oita, Jan., 2006), GS1-4.
  • Tokita, Y., Nakamura, Y., Yoshimoto, J., & Ishii, S. (2006). Reinforcement learning of switching multiple controllers to control a real robot. The eleventh international symposium on Artificial Life and Robotics (Oita, Jan., 2006), GS22-3.
  • Hirayama, J., Maeda, S., & Ishii, S. (2006). A Bayesian approach to blind source separation with variable number of sources. The eleventh international symposium on Artificial Life and Robotics (Oita,Jan.,2006),GS19-6,AROB young author award.
  • Nakamura, Y., Mori, T., & Ishii, S. (2006). Natural policy gradient reinforcement learning method for a looper-like robot. The eleventh international symposium on Artificial Life and Robotics (Oita, Jan., 2006), GS3-3.
  • Fujita, H., & Ishii, S. (2006). Model-based reinforcement learning for large-scale multi-agent games with sampling-based state estimation. The eleventh international symposium on Artificial Life and Robotics (Oita, Jan., 2006), GS3-1.
  • Nomura, T., Shibata, T., Tamei, T., & Ishii, S. (2005). Extended force/tactile senses of machines by measurement of user's biological signals. Proceedings 36th International Symposium on Robotics (Tokyo, Nov., 2005).
  • Oba, S., Kato, K., & Ishii, S. (2005). Multi-scale clustering for gene expression profiling data. IEEE 5th Symposium on Bioinformatics and Bioengineering, 210217, IEEE.
  • Magono, M., Yoshimoto, J., Ishii, S., & Doya, K. (2005). Localization of cyber rodent based on mixture Kalman filters. Proceedings of 2005 International Symposium on Nonlinear Theory and its Applications, 401-404.
  • Honda, N., Sakumura, Y., & Ishii, S. (2005). Local signaling with diffusion process as a decoder of Ca2+ signals in synaptic plasticity. The Assembly and Function of Neuronal Circuits (Ascona, Sep., 2005), 79.
  • Fujita, H., & Ishii, S. (2005). Model-based reinforcement learning for a multi-player card game with partial observability. The 2005 IEEE-WIC-ACM International Conference on Intelligent Agent Technology, 467-470, IEEE.
  • Nakamura, Y., Mori, T., & Ishii, S. (2005). An off-policy natural policy gradient method for a partial observable Markov decision process. Artificial Neural Networks: Formal Models and Their Applications - ICANN 2005, Lecture Notes in Computer Science, 3697, 431-436.
  • Yukinawa, N., Oba, S., Kato, K., & Ishii, S. (2005). Multi-class pattern classification based on a probabilistic model of combining binary classifiers. Artificial Neural Networks: Formal Models and Their Applications -ICANN 2005, Lecture Notes in Computer Science, 3697, 337-342.
  • Hitomi, K., Shibata, T., Nakamura, Y., & Ishii, S. (2005). On-line learning of a feedback controller for quasi-passive-dynamic walking by a stochastic policy gradient method. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1923-1928, IEEE.
  • Hitomi, K., Shibata, T., Nakamura, Y., & Ishii, S. (2005). Reinforcement learning of stable trajectory for quasi-passive-dynamic walking. Modeling Natural Action Selection: Proceedings of an International Workshop, 229-234.
  • Hirayama, J., Maeda, S., & Ishii, S. (2005). Bayesian noisy ICA for source switching environments. IEEE Workshop for Statistical Signal Processing, 232.
  • Ishii, S. (2005). Prediction-based optimal controls in artificial and real intelligences. Proceedings of International Symposium on The Art of Statistical Metaware (Tokyo, Mar., 2005), 111-121,(invited).
  • Tensho, S., Maekawa, S., Yoshimoto, J., Shibata, T., & Ishii, S. (2005). Gradual emergence of communication in a multi-agent environment. Proceedings of the Tenth International Symposium on Artificial Life and Robotics, GS16-3.
  • Bando, T., Shibata, T., Doya, K., & Ishii, S. (2005). Hard/soft switching particle filters for efficient real-time visual tracking. Proceedings of the Tenth International Symposium on Artificial Life and Robotics, GS15-5.
  • Fukunaga, S., Nakamura, Y., Aso, K., & Ishii, S. (2004). Reinforcement learning for a snake-like robot controlled by a central pattern generator. Proceedings of the 2004 IEEE Conference on Robotics, Automation and Mechatronics, 909-914, IEEE.
  • Ogura, T., Hasegawa, F., Oba, S., F., Miyazawa, T., & Ishii, S. (2004). Character string extraction from color documents using supervised learning. Proceedings of 2004 International Symposium on Nonlinear Theory and its Applications, 701-704.
  • Yukinawa, N., Yoshimoto, J., Oba, S., & Ishii, S. (2004). Modeling gene expression dynamics based on a linear dynamical system model. Proceedings of 2004 International Symposium on Nonlinear Theory and its Applications, 577-580.
  • Igarashi, Y., Sakumura, Y., & Ishii, S. (2004). The role of short term depression for sustained neural activities in the prefrontal cortex: a simulation study. International Symposium on Nonlinear Theory and its Applications, 509-512.
  • Fukunaga, S., Nakamura, Y., Aso, K., & Ishii, S. (2004). Reinforcement learning for a snake-like robot. Proceedings of 2004 International Symposium on Nonlinear Theory and its Applications, 75-78.
  • Funakoshi, H., Yoshida, W., & Ishii, S. (2004). An imaging study on human action selection using hierarchical rules. The Third International Conference on Development and Learning (ICDL)(La Jolla, Oct., 2004).
  • Maeda, S., & Ishii, S. (2004). A noisy nonlinear independent component analysis. IEEE International Workshop on Machine Learning for Signal Processing(Sao Luis, Oct.,2004), 173-182, IEEE.
  • Nakamura, Y., Mori, T., & Ishii, S. (2004). Natural policy gradient reinforcement learning for a CPG control of a biped robot. In Parallel Problem Solving from Nature -PPSN VIII, Lecture Notes in Computer Science, 3242, 972-981.
  • Hirayama, J., Yoshimoto, J., & Ishii, S. (2004). Cortical representation learning regulated by acetylcholine. Brain Inspired Cognitive Systems (Stirling, Sep., 2004), ICESS.3.
  • Bando, T., Shibata, T., Doya, K., & Ishii, S. (2004). Switching particle filters for efficient real-time visual tracking. International Conference on Pattern Recognition (ICPR),2, 720-723.
  • Yoshimoto, J., & Ishii, S. (2004). A solving method for MDPs by minimizing variational free energy. International Joint Conference on Neural Networks (IJCNN), 3, 1817-1822, IEEE.
  • Mori, T., Nakamura, Y., Sato, M., & Ishii, S. (2004). Reinforcement learning for CPG-driven biped robot. The Nineteenth National Conference on Artificial Intelligence (AAAI), 623-630.
  • Fujita, H., & Ishii, S. (2004). A reinforcement learning scheme for a multi-agent card game with Monte Carlo state estimation. International Conference on Computational Intelligence for Modelling Control and Automation, 799-806.
  • Shibata, T., Suhara, Y., Oga, T., Ueki, Y., Mima, T., & Ishii, S. (2004). Application of multivariate autoregressive modeling for analyzing the interaction between EEG and EMG in humans. International Congress Series, 1270, 249-253.
  • Maeda, S., & Ishii, S. (2004). Optimization of product code. WSEAS Transactions on Systems, 2(3), 473-476.
  • Nishimura, M., Yoshimoto, J., & Ishii, S. (2004). Acrobot control by learning the switching of multiple controllers. Proceedings of the Ninth International Symposium on Artificial Life and Robotics, 2, 633-636.
  • Kanemoto, K., Yoshimoto, J., & Ishii, S. (2004). A probabilistic approach to identify environmental models of mobile robots. Proceedings of the Ninth International Symposium on Artificial Life and Robotics, 1, 329-332.
  • Fujita, H., Matsuno, Y., & Ishii, S. (2003). A reinforcement learning scheme for a multi-agent card game. 2003 IEEE International Conference on Systems, Man & Cybernetics (Washington, D.C., Oct., 2003), 4071-4078.
  • Oba, S., Sato, M., & Ishii, S. (2003). Prior hyperparameters in Bayesian PCA. In Artificial Neural Networks and Neural Information Processing, Lecture Notes in Computer Science, 2714, 271-279, Berlin: Springer-Verlag.
  • Yoshimoto, J., Ishii, S., & Sato, M. (2003). System identification based on online variational Bayes method and its application to reinforcement learning. In Artificial Neural Networks and Neural Information Processing, Lecture Notes in Computer Science, 2714, pp. 123-131, Berlin: Springer-Verlag.
  • Yoshida, W., & Ishii, S. (2003). A model-based reinforcement learning: a computational model and an fMRI study. In 11th European Symposium on Artificial Neural Networks (Bruges, Apr., 2003), 313-318, Belgium: d-side publications.
  • Nakamura, Y., Sato, M., & Ishii, S. (2003). Reinforcement learning for biped robot. 2nd International Symposium on Adaptive Motion of Animals and Machines (Kyoto, Mar., 2003), ThP-II-5.
  • Yoshioka, T., & Ishii, S. (2002). Clustering for time-series gene expression data using mixture of constrained PCAS. 9th International Conference on Neural Information Processing,(Singapore, Nov., 2002).
  • Tamakoshi, H., & Ishii, S. (2002). Emergence of communication protocol from agents' experiences. 4th Asia-Pacific Conference on Simulated Evolution And Learning (Singapore, Nov., 2002).
  • Sakumura, Y., & Ishii, S. (2002). The differential operation by neural assembly. 2002 International Symposium on Nonlinear Theory and its Applications (Xi’an, Oct., 2002), 323-326.
  • Sato, M., Nakamura, Y., & Ishii, S. (2002). Reinforcement learning for biped locomotion. In Artificial Neural Networks -ICANN 2002, Lecture Notes in Computer Science, 2415, 777-782, Berlin: Springer-Verlag.
  • Yoshimoto, J., Ishii, S., & Sato, M. (2002). Hierarchical model selection for NGnet based on variational Bayes inference. In Artificial Neural Networks -ICANN 2002, Lecture Notes in Computer Science, 2415, 661-666, Berlin: Springer-Verlag.
  • Yoshioka, T., Morioka, R., Kobayashi, K., Oba, S., Ogasawara, N., & Ishii, S. (2002). Clustering of gene expression data by mixture of PCA models. In Artificial Neural Networks -ICANN 2002, Lecture Notes in Computer Science, 2415, 522-527, Berlin: Springer-Verlag.
  • Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K., & Ishii, S. (2002). Missing value estimation using mixture of PCAs. In Artificial Neural Networks -ICANN 2002, Lecture Notes in Computer Science, 2415, 492-497, Berlin: Springer-Verlag.
  • Maeda, S., & Ishii, S. (2002). An auditory system for efficient coding of natural sounds. Proceedings of the 2002 International Joint Conference on Neural Networks (Honolulu, May2002), 23-28, IEEE.
  • Amemori, K., & Ishii, S. (2001). Resonance of a stochastic spiking neuron Mimicking the Hodgkin-Huxley model. In Artificial Neural Networks -ICANN 2001, Lecture Notes in Computer Science, 2130, 1087-1094,Berlin: Springer-Verlag.
  • Amemori, K., & Ishii, S. (2001). Gaussian process approach to stochastic spiking neurons with reset. In Artificial Neural Networks -ICANN 2001, Lecture Notes in Computer Science, 2130, 361-368, Berlin: Springer-Verlag.
  • Oba, S., Sato, M., & Ishii, S. (2001). On-line learning methods for Gaussian processes. In Artificial Neural Networks -ICANN 2001, Lecture Notes in Computer Science, 2130, 292-299, Berlin: Springer-Verlag.
  • Tamakoshi, H., Ishii, S., Yoshida, W., & Sato, M. (2001). Vowel synthesis by on-line EM algorithm with IIR filter. In International Joint Conference on Neural Networks (Washington DC, Jul., 2001),4, 2821-2825.
  • Yoshioka, T., Takata, Y., Ito, M., & Ishii, S. (2001). A neural visualization method for WWW document clusters. In International Joint Conference on Neural Networks (Washington DC, Jul., 2001),3, 2270-2275.
  • Yoshioka, T., & Ishii, S. (2001). Fast Gaussian process regression using representative data. In International Joint Conference on Neural Networks (Washington DC, Jul., 2001), 1, 132-137.
  • Matsuno, Y., Yamazaki, T., & Ishii, S. (2001). A multi-agent reinforcement learning method for a partially-observable competitive game. In Proceedings of the Fifth International Conference on Autonomous Agents (Montreal, May, 2001), 39-40, ACM.
  • Niitsuma, H., & Ishii, S. (2000). Learning of minimax strategy by support vector machines. In Proceedings of 7th International Conference on Neural Information Processing (Taejon, Nov., 2000),2, 1432-1437.
  • Oba, S., Ishii, S., & Sato, M. (2000). Variational Bayes method for mixture of principal component analyzers. In Proceedings of 7th International Conference on Neural Information Processing (Taejon, Nov., 2000),2, 1416-1421.
  • Amemori, K., & Ishii, S. (2000). Effect of the synaptic time constant on stochastic spiking neurons. In Proceedings of 7th International Conference on Neural Information Processing (Taejon, Nov., 2000),1, 6-11.
  • Yoshimoto, J., Ishii, S., & Sato, M. (2000). On-line EM reinforcement learning. In Proceedings of IEEE-INNS-ENNS International Joint Conference on Neural Networks (Como, Jul., 2000),III, 163-168.
  • Nagayuki, Y., Ishii, S., & Doya, K. (2000). Multi-agent reinforcement learning: an approach based on the other agent's internal model. In Proceedings of Fourth International Conference on MultiAgent Systems (Boston, Jul., 2000), 215221, Los Alamitos: IEEE Computer Society.
  • Yoshida, W., Ishii, S., & Sato, M. (2000). Reconstruction of chaotic dynamics using a noise-robust embedding method. In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (Istanbul, Jun., 2000),I, 181-184.
  • Tamakoshi, H., & Ishii, S. (2000). Multi-agent reinforcement learning applied to a chase problem in a continuous world. In Proceedings of Fifth International Symposium on Artificial Life and Robotics (Oita, Jan., 2000),2, 661-664.
  • Nagayuki, Y., Ishii, S., Ito, M., Shimohara, K., & Doya, K. (2000). A multi-agent reinforcement learning method with the estimation of the other agent’s actions. In Proceedings of Fifth International Symposium on Artificial Life and Robotics (Oita,Jan.,2000),1, 255-259.
  • Ishii, S., & Niitsuma, H. (1999). A non-equilibrium neural optimization method for quadratic assignment problem. In Proceedings of 1999 International Symposium on Nonlinear Theory and Its Applications (Hawaii, Nov., 1999), 1, 171-174.
  • Ishii, S., Yoshimoto, J., Yoshida, W., & Sato, M. (1999). On-line EM algorithm and its applications. In Emerging Knowledge Engineering and Connectionist-Based Information Systems (Dunedin, Nov., 1999), 17-20, Dunedin: University of Otago.
  • Yoshida, W., Ishii, S., & Sato, M. (1999). Approximating discrete mapping of chaotic dynamical system based on on-line EM algorithm. 6th International Conference on Neural Information Processing(Perth, Nov.,1999),III, 1010-1016, New York: IEEE.
  • Yoshimoto, J., Ishii, S., & Sato, M. (1999). Application of reinforcement learning to balancing of acrobot. In Proceedings of 1999 IEEE International Conference on Systems, Man and Cybernetics (Tokyo,Oct.,1999),V, 516-521,NewYork: IEEE.
  • Yoshioka, T., & Ishii, S. (1999). On-line EM algorithm for acquiring evaluation function of game Othello through reinforcement learning. In Proceedings of 1999 IEEE International Conference on Systems, Man and Cybernetics (Tokyo, Oct., 1999), V, 498-503, New York: IEEE.
  • Yoshida, W., Ishii, S., & Sato, M. (1999). Reconstruction of chaotic dynamics and robustness to noise with on-line EM algorithm. In Proceedings of 1999 IEEE International Conference on Systems, Man and Cybernetics (Tokyo, Oct., 1999), I, 414-419, New York: IEEE.
  • Amemori, K., & Ishii, S. (1999). Self-organization and association for temporal coding. In ICANN99: Artificial Neural Networks, pp. 162-167, Conference Publication No.470, London: Institution of Electrical Engineers.
  • Ishii, S., & Niitsuma, H. (1999). λ-opt neural networks for quadratic assignment problem. In ICANN99: Artificial Neural Networks, pp.115-120, Conference Publication No.470, London: Institution of Electrical Engineers.
  • Sato, M., & Ishii, S. (1999). Reinforcement learning based on on-line EM algorithm, In Advances in Neural Information Processing Systems 11, (eds. M. S. Kearns, S. A. Solla and D. A. Cohn), 1052-1058, Cambridge: MIT Press.
  • Amemori, K., & Ishii, S. (1998). Unsupervised learning for sub-millisecond temporal coded sequence. In JCIS ’98 Proceedings (Research Triangle Park, Oct., 1998), II, 80-83.
  • Yoshioka, T., Ishii, S., & Ito, M. (1998). Strategy acquisition for the game “Othello” based on reinforcement learning. In Proceedings of the Fifth International Conference on Neural Information Processing,(eds. S. Usui and T. Omori), 2, 841-844, Burke: IOS Press.
  • Amemori, K., & Ishii, S. (1998). Self-organizing network learning of sub-millisecond temporal coded information. In Proceedings of the Fifth International Conference on Neural Information Processing,(eds. S.Usui and T.Omori), 3, 1285-1288, Burke: IOS Press.
  • Sato, M., & Ishii, S. (1998). On-line EM algorithm for mixture of local experts. In Proceedings of the Fifth International Conference on Neural Information Processing,(eds. S. Usui and T. Omori), 3, 1397-1401, Burke: IOS Press.
  • Ishii, S. (1998). Deterministic annealing and chaotic annealing in a neural approach to quadratic assignment problem. Methodologies for the Conception, Design and Application of Soft Computing,(eds. T.Yamakawa and G.Matsumoto), 2, 892-895, Singapore: World Scientific.
  • Ishii, S., & Sato, M. (1998). On-line EM algorithm and reinforcement learning. In ICANN 98 (eds. L.Niklasson, M.Boden and T.Ziemke), 1127-1132,London: Springer-Verlag.
  • Ishii, S., & Sato, M. (1998). On-line EM algorithm and reconstruction of chaotic dynamics. In Neural Networks for Signal Processing VIII(eds. T.Constantinides, S.-Y. Kung, M. Niranjan and E. Wilson), 360-369, New York: IEEE.
  • Niitsuma, H., Ishii, S., & Ito, M. (1997). Chaotic optimization and linear transformation. In Proceedings of the 1997 International Symposium on Nonlinear Theory and Its Applications (Hawaii, Dec., 1997), 601-604.
  • Ishii, S. (1997). Neural approaches to quadratic assignment problems. Proceedings of the 2nd International Conference on Computational Intelligence and Neuroscience (Research Triangle Park, Mar., 1997), 276-279.
  • Ishii, S., & Sato, M. (1996). Parametrically coupled chaotic elements as associative memory. In Proceedings of the 1996 International Symposium on Nonlinear Theory and Its Applications (Kochi, Oct., 1996), 273-276.
  • Ishii, S., & Sato, M. (1995). Doubly constrained network model for combinatorial optimization problems. In Proceedings of the 1995 International Symposium on Nonlinear Theory and Its Applications (Las Vegas, Dec., 1995), 371-374.
  • Ishii, S. (1995). Chaotic Potts spin. In Proceedings of the 1995 IEEE International Conference on Neural Networks (Perth, Nov., 1995), 1578-1583,(invited).
  • Ishii, S., Fukumizu, K., & Watanabe, S. (1994). An approach for reducing spurious memories using globally coupled map. In Proceedings of the 3rd International Conference on Fuzzy Logic, Neural Nets and Soft Computing (Iizuka, Aug., 1994), 643-644,(invited).
  • Ishii, S., Fukumizu, K., & Watanabe, S. (1993). A globally coupled map model for information processing. In 1993 International Symposium on Nonlinear Theory and Its Applications (Hawaii, Dec., 1993), 1157-1160.
  • Ishii, S., Fukumizu, K., & Watanabe, S. (1993). Associative memory using spatiotemporal chaos. In Proceedings of 1993 International Joint Conference on Neural Networks (Nagoya, Oct., 1993), 2638-2641.
  • Ishii, S. (1992). A fuzzy semantic network and its use for dealing with incomplete semantic description. In Proceedings of the 2nd International Conference on Fuzzy Logic and Neural Networks (Iizuka, Jul., 1992), 697-700.
  • Ishii, S. (1991). A robust Japanese parser with asleep-awake mechanism. In Proceedings of the Natural Language Processing Pacific Rim Symposium (Singapore, Nov., 1991), 193-199.
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