{"id":424,"date":"2010-07-28T13:47:25","date_gmt":"2010-07-28T04:47:25","guid":{"rendered":"http:\/\/www.cns.atr.jp\/cns-naist\/?page_id=424"},"modified":"2021-05-26T19:33:26","modified_gmt":"2021-05-26T10:33:26","slug":"publications-2","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/cns-naist\/publications-2\/","title":{"rendered":"\u696d\u7e3e"},"content":{"rendered":"

\u5f53\u8b1b\u5ea7\u6240\u5c5e\u5b66\u751f\u304c\u767a\u8868\u3057\u305f\u96d1\u8a8c\u8ad6\u6587\u3067\u3059\u3002<\/p>\n

2021<\/div>\n

Suzuki, K., Yamashita, O., MEG current source reconstruction using a meta-analysis fMRI prior. NeuroImage<\/em>, 2021, 236:118034.<\/p>\n

2019<\/div>\n

Takagi, Y., Hirayama, J., Tanaka, S., C., State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits. NeuroImage<\/em>, 2019, 201:116036.<\/p>\n

2018<\/div>\n

Takagi, Y., Sakai, Y., Abe, Y., Nishida, S., Harrison, B., J., Mart\u00ednez-Zalaca\u00edn, I., Soriano-Mas, C., Narumoto, J., Tanaka, S., C., A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity. NeuroImage<\/em>, 2018, 172, 506\u2013516.<\/p>\n

Ishihara, K., Morimoto, J., An optimal control strategy for hybrid actuator systems: application to an artificial muscle with electric motor assist. Neural Networks<\/em>, 2018, 99, 92-100.<\/p>\n

2017<\/div>\n

Cortese, A., Amano, K., Koizumi, A., Kawato, M., and Lau, H., Decoded fMRI neurofeedback can induce bidirectional confidence changes within single participants. NeuroImage<\/em>, 2017, 149, 323-337.<\/p>\n

Takagi, Y., Sakai, Y., Lisi, G., Yahata, N., Abe, Y., Nishida, S., Nakamae, T., Morimoto, J., Kawato, M., Narumoto, J., Tanaka, S., C., A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity. Scientific Reports<\/em>, 2017, 7, 7538.<\/p>\n

2016<\/div>\n

Cortese, A., Amano, K., Koizumi, A., Kawato, M., and Lau, H., Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nature Communications<\/em>, 2016, 7:13669.<\/p>\n

2015<\/div>\n

Yamashita, M., Kawato, M., Imamizu, H., Predicting learning plateau of working memory from whole-brain intrinsic network connectivity patterns. Scientific Reports<\/em>, 2015, 5, 7622.<\/p>\n

Fukushima, M., Yamashita, O., Kn\u00f6sche, T.R., Sato, M., MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks. NeuroImage<\/em>, 2015, 105, 408-427.<\/p>\n

2014<\/div>\n

Lisi, G., Noda, T., Morimoto, J., Decoding the ERD\/ERS: influence of afferent input induced by a leg assistive robot. Frontiers in Systems Neuroscience<\/em>, 2014, 8.<\/p>\n

Majima, K., Matsuo, T., Kawasaki, K., Kawai, K., Saito, N., Hasegawa, I., Kamitani, Y., Decoding visual object categories from temporal correlations of ECoG signals. NeuroImage<\/em>, 2014, 90, 74-83.<\/p>\n

2013<\/div>\n

Horikawa, T., Tamaki, M., Miyawaki, Y., Kamitani, Y., Neural decoding of visual imagery during sleep. Science<\/em>, 2013, 340(6132), 639-642.<\/p>\n

Onizuka, M., Hoang, H., Kawato, M., Tokuda, I., Schweighofer, N., Katori, Y., Aihara, K., Lang, E.J., Toyama, K., Solution to the inverse problem of estimating gap-junctional and inhibitory conductance in inferior olive neurons from the spike trains by network model simulation. Neural Networks<\/em>, 2013, 47, 51-63.<\/p>\n

2012<\/div>\n

Fukushima, M., Yamashita, O., Kanemura, A., Ishii, S., Kawato, M., Sato, M., A state-space modeling approach for localization of focal current sources from MEG. IEEE Transactions on Biomedical Engineering<\/em>, 2012, 59(6), 1561-1571.<\/p>\n

2009<\/div>\n

Fujiwara, Y., Yamashita, O., Kawawaki, D., Doya, K., Kawato, M., Toyama, K., Sato, M., A hierarchical Bayesian method to resolve an inverse problem of MEG contaminated with eye movement artifacts. NeuroImage<\/em>, 2009, 45(2), 393-409.<\/p>\n

Nambu, I., Osu, R., Sato, M., Ando, S., Kawato, M., Naito, E., Single-trial reconstruction of finger-pinch forces from human motor-cortical activation measured by near-infrared spectroscopy (NIRS). NeuroImage<\/em>, 2009, 47(2), 628-637.<\/p>\n

2008<\/div>\n

Bissmarck, F., Nakahara, H., Doya, K., Hikosaka, O., Combining modalities with different latencies for optimal motor control. Journal of Cognitive Neuroscience<\/em>, 2008, 20(11), 1966-1979.<\/p>\n

Ariki, Y., Morimoto, J., Hyon, S-.H., Behavior recognition with ground reaction force estimation and its application to imitation learning [in Japanese]. The IEICE Transactions on Information and Systems (Japanese Edetion)<\/em>, 2008, J91-D(9), 2394-2403.<\/p>\n

2007<\/div>\n

Shibata, K., Yamagishi, N., Goda, N., Yoshioka, T., Yamashita, O., Sato, M., Kawato, M., The effects of feature attention on prestimulus cortical activity in the human visual system. Cerebral Cortex<\/em>, 2007, 18(10), 1664-1675.<\/p>\n

2006<\/div>\n

Tanaka, S.C., Samejima, K., Okada, G., Ueda, K., Okamoto, Y., Yamawaki, S., Doya, K., Brain mechanism of reward prediction under predictable and unpredictable environmental dynamics. Neural Networks<\/em>, 2006, 19(8), 1233-1241.<\/p>\n

Kawawaki, D., Shibata, T., Goda, N., Doya, K., Kawato, M., Anterior and superior lateral occipito-temporal cortex responsible for target motion prediction during overt and covert visual pursuit. Neuroscience Research<\/em>, 2006, 54(2), 112-123.<\/p>\n

Sugimoto, N., Samejima, K., Doya, K., Kawato, M., Hierarchical reinforcement learning: temporal abstraction based on MOSAIC model [in Japanese]. The IEICE Transactions on Information and Systems (Japanese Edetion)<\/em>, 2006, J89-D(7), 1577-1587.<\/p>\n

Matsubara, T., Morimoto, J., Nakanishi, J., Sato, M., Doya, K., Learning CPG-based biped locomotion with a policy gradient method. Robotics and Autonomous Systems<\/em>, 2006, 54, 911-920.<\/p>\n

2005<\/div>\n

Doi, T., Kuroda, S., Michikawa, T., Kawato, M., Inositol 1,4,5-trisphosphate-dependent Ca2+ threshold dynamics detect spike timing in cerebellar Purkinje cells. The Journal of Neuroscience<\/em>, 2005, 25(4), 950-961.<\/p>\n

Matsubara, T., Morimoto, J., Nakanishi, J., Sato, M., Doya, K., Learning a dynamic policy by using policy gradient: application to biped walking [in Japanese]. The Transactions of the Institute of Electronics, Information and Communication Engineers<\/em>, 2005, J88-D-II(1), 53-65.<\/p>\n

2004<\/div>\n

Tanaka, S.C., Doya, K., Okada, G., Ueda, K., Okamoto, Y., Yamawaki, S., Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops. Nature Neuroscience<\/em>, 2004, 7(8), 887-893.<\/p>\n

Hirai, S., Osu, R., Yoshioka, T., Kawato, M., Simultaneous adaptation and switching for two viscous force fields [in Japanese]. The Transactions of the Institute of Electronics, Information and Communication Engineers<\/em>, 2004, J87-D-II(12), 2231-2241.<\/p>\n

Sugimoto, N., Samejima, K., Doya, K., Kawato, M., Reinforcement learning and goal estimation by multiple forward and reward models [in Japanese]. The Transactions of the Institute of Electronics, Information and Communication Engineers<\/em>, 2004, J87-D-II(2), 683-694.<\/p>\n","protected":false},"excerpt":{"rendered":"

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