研究成果
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発表年
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国際会議 講演・発表

  1. 1. Hayakawa T, Fukai T, The mean-field theory of dynamically balanced neuronal networks, 2018 BibTeX
  2. 2. *Rok Pahic, Andrej Gams, Ales Ude, Jun Morimoto, Deep Encoder-Decoder Networks for Mapping Raw Images to Movement Primitives, 2018 5, reviewed# BibTeX
  3. 3. Shinsuke Suzuki, Food value computation in the human orbitofrontal cortex, 2018 , invited BibTeX
  4. 4. Hiroaki Shioya, Yusuke Iwasawa, Yutaka Matsuo, Extending Robust Adversarial Reinforcement Learning Considering Adaptation and Diversity, 2018 4 BibTeX
  5. 5. Joji Toyama, Yusuke Iwasawa, Kotaro Nakayama, Yutaka Matsuo, Expert-based reward function training: the novel method to train sequence generators,, 2018 4 BibTeX
  6. 6. Shohei Ohsawa, Kei Akuzawa, Tatsuya Matsushima, Gustavo Bezerra, Yusuke Iwasawa, Hiroshi Kajino, Seiya Takenaka, Yutaka Matsuo, Neuron as an Agent, 2018 4 BibTeX
  7. 7. Doya K, Neural circuits for reinforcement learning and mental simulation, 2018 , New York University, USA, 2018.3.18 BibTeX
  8. 8. Doya K, Neural circuits for reinforcement learning and mental simulation, 2018 , Cold Spring Harbor Laboratory, USA, 2018.3.19 BibTeX
  9. 9. So Fujimoto, Yutaka Komura*, Neurobiology and statistics of reflective minds in primates, 2018 BibTeX
  10. 10. *Yoshizawa T, Ito M, Doya K, Cell-type specific calcium imaging of striatal neurons in the striosome compartments during an odor-conditioning task, 2018 , Four Points Sheraton/Holiday Inn Express, Ventura, CA, US, 2018.3.15 BibTeX
  11. 11. Doya K, How does the brain wire up itself on the fly?, 2018 , Institute for Advanced Study, Princeton, USA, 2018.3.14, invited BibTeX
  12. 12. Doya K, Neural Circuit for Mental Simulation, 2018 , Princeton Neuroscience Institute, USA BibTeX
  13. 13. Doya K, Imaging the neural circuit for mental simulation, 2018 , Denver City, USA, 2018.3.5 BibTeX
  14. 14. Takagishi, H., Sakagami, M., & Yamagishi, T, Social Value Orientation is Associated with the Role of Right Dorsolateral Prefrontal Cortex in Prosocial Behavior, 2018 3 BibTeX
  15. 15. Kurikawa T, Handa T, Fukai T, Different neural landscape regulates individual differences in sensory-guided decision making, 2018 BibTeX
  16. 16. Fung C.C, Fukai T, Discrete-attractor-like motion in continuous-attractor neural field models, 2018 BibTeX
  17. 17. Fukai T, Information transfer during slow oscillations in cortical networks, 2018 , invited BibTeX
  18. 18. Kurikawa T, Fukai T, Dynamic modulation in communications between Hippocampus and Medial entorhinal cortex depending on cognitive state, 2018 BibTeX
  19. 19. Shinsuke Suzuki, Value computation in the human brain: its basis and contagious nature, 2018 , invited BibTeX
  20. 20. Fukai T, Reservoir computing for chunking and decision-making, 2018 , invited BibTeX
  21. 21. Fukai T, Sequence learning through reverse replay and preplay in hippocampal circuit models., 2018 , invited BibTeX
  22. 22. Fukai T, Dendritic canonical correlation analysis for memory and sensory information processing., 2018 , invited BibTeX
  23. 23. Doya K, Neural circuits for reinforcement learning and mental simulation, 2018 , Gold Hall and Ruby Hall, WellyHilly park, South Korea, 2018.1.30, invited BibTeX
  24. 24. Masamichi Sakagami, Decoding the value related signal represented in multiple areas of the prefrontal cortex using ECoG electrodes, 2018 1, invited BibTeX
  25. 25. Masamichi Sakagami, Multiple Neural Circuits In Value-based Decision Making, 2018 1, invited BibTeX
  26. 26. Doya K, What should we further learn from the brain?, 2018 , WellyHilly park, South Korea, 2018.1.29 BibTeX
  27. 27. T. Yanagisawa, Brain-machine interface to modulate cortical functions, 2018 BibTeX
  28. 28. Ryosuke Tanno and Keiji Yanai, AR DeepCalorieCam: An iOS App for Food Calorie Estimation with Augmented Reality, 2018 2, reviewed BibTeX
  29. 29. Eiji Uchibe*, Efficient Sample Reuse in Policy Search by Multiple Importance Sampling, 2018 , reviewed# BibTeX
  30. 30. Stefan Elfwing*, Eiji Uchibe, and Kenji Doya, Online Meta-Learning by Parallel Algorithm Competition, 2018 , reviewed# BibTeX
  31. 31. Doya K, Neural circuits for reinforcement learning and mental simulation, 2017 , Queensland Brain Institute, The University of Queensland, Brisbane, Australia, 2017.12.14, invited BibTeX
  32. 32. *Hikida T, Macpherson T, Nucleus accumbens D1 receptor expressing neurons control sutoshaping behavior., 2017 BibTeX
  33. 33. Macpherson T, *Hikida T, Dopamine D2L receptors control flexible behavior., 2017 BibTeX
  34. 34. Takufumi Yanagisawa, Ryohei Fukuma, Ben Seymour, Koichi Hosomi, Haruhiko Kishima, Toshiki Yoshimine, Yukiyasu Kamitani, Youichi Saitoh, MEG-Neurofeedback for phantom limb pain, 2017 BibTeX
  35. 35. *Tadahiro Taniguchi, Symbol Emergence in Robotics: from sensorimotor information to language, 2017 , invited BibTeX
  36. 36. Fukai T, Unsupervised reservoir computing for chunking sequence information., 2017 BibTeX
  37. 37. Tanaka S, Kawasaki K, Hasegawa I, Suzuki T, Kawato M and Sakagami M, Elucidating the role of the macaque lateral prefrontal cortex for the value-based decision making using the decoded neurofeeback, 2017 11 BibTeX
  38. 38. *J. Hirayama, T. Ogawa, H. Moriya, A. Hyvärinen & M. Kawanabe, Exploring EEG source resting-state networks by SPLICE: A simultaneous fMRI study, 2017 , reviewed# BibTeX
  39. 39. *T. Ogawa, H. Moriya, T. Yamada, M. Kawanabe & J. Hirayama, Prediction of resting state fMRI signatures from EEG signal: a study of EEG-fMRI simultaneous recording, 2017 , reviewed# BibTeX
  40. 40. *H. Moriya, T. Ogawa, M. Kawanabe, J. Hirayama, Predictability of amygdala BOLD signal from multiple-electrode EEGs, 2017 , reviewed# BibTeX
  41. 41. *Hikida T, Basal ganglia network mechanisms in cognitive learning, 2017 , invited BibTeX
  42. 42. T. Yanagisawa, R. Fukuma, B. Seymour, K. Hosomi, H. Kishima, T. Shimizu, H. Yokoi, M. Hirata, T. Yoshimine, Y. Kamitani, Y. Saitoh, MEG-based BMI controlled the sensorimotor cortical plasticity and phantom limb pain, 2017 , invited BibTeX
  43. 43. *Reinke C, Doya K, Adaptation of Optimization Algorithms to Problem Domains by Transfer Learning, 2017 , OIST, 2017.11.24 BibTeX
  44. 44. Benucci A., Sensory Representation Plasticity Driven by Single Neurons in the Mouse Cortex, 2017 Nov, Invited talk BibTeX
  45. 45. Doya K, What can we further learn from the brain?, 2017 , Guangzhou, China, 2017.11.16, invited BibTeX
  46. 46. *Jun-ichiro FURUKAWA, Asuka TAKAI,Jun MORIMOTO, Database-driven approach for biosignal-based robot control with collaborative filtering, 2017 11, reviewed# BibTeX
  47. 47. Haga T, Fukai T, Reverse replay strengthens forward pathways to reward through Hebbian learning and short-term depression., 2017 BibTeX
  48. 48. *Yoshizawa T, Ito M, Doya K, Neural representation of sensory-state value in the striatal striosome compartment , 2017 , Walter E. Washington Convention Center, Washington DC, USA, 2017.11.14 BibTeX
  49. 49. Fukuda H, Ma N, Suzuki S, Harasawa N, Ueno K, Gardner JL, Ichinohe N, Haruno M, Cheng K, Nakahara H. , Neural mechanisms for converting social value into one’s own decision value, 2017 BibTeX
  50. 50. Ma N, Harasawa N, Ueno K, Ichinohe N, Haruno M, Cheng K, Nakahara H., Neural mechanisms for deciding with predicting others in human brain, 2017 BibTeX
  51. 51. Tanaka S, Kawasaki K, Hasegawa I, Suzuki T, Kawato M and Sakagami M, Spatial and temporal distribution of value-related and the visual information in the macaque lateral prefrontal cortex, 2017 11 BibTeX
  52. 52. Tsubota T., *Benucci A., Plasticity for stimulus selectivity in the visual cortex of adult mice induced by patterned optogenetic stimulation, 2017 Nov BibTeX
  53. 53. Asabuki T, Fukai T, Chunk learning from complex sequences by mutually supervising recurrent neural networks, 2017 BibTeX
  54. 54. DeCostanzo A, Fukai T, A local supervised learning rule protects memories from catastrophic interference during subsequent unsupervised learning., 2017 BibTeX
  55. 55. Watanabe K, Haga T, Tatsuno M, Euston DR, Fukai T, Blind detection of behavior related population activity by using edit similarity measurement and statistical modeling., 2017 , 　 BibTeX
  56. 56. Uezono S, Tanabe S, Fujiwara M, Tsuge H, Nakamura K, Inoue K, Takada M, Organization of multisynaptic inputs from the basal ganglia and cerebellum to the anterior and posterior cingulate cortical areas in common marmosets: Retrograde transneuronal double labeling with fluorescent rabies viral vectors., 2017 11, Washington, DC, USA BibTeX
  57. 57. Takaya Ogasawara, Masahiko Takada, Masayuki Matsumoto, The nigrostriatal dopamine pathway transmits a stop signal during the performance of a saccadic countermanding task in monkeys, Society for Neuroscience BibTeX
  58. 58. Inoue K, Fujiwara M, Uezono S, Tanabe S, Ishida H, Hoshi E, Takada M, Arrangement of multisynaptic inputs from the basal ganglia to the dorsal and ventral premotor cortical areas in macaques: retrograde transneuronal double labeling with fluorescent rabies viral vectors., 2017 11, Washington, DC, USA BibTeX
  59. 59. Sidney Lehky, A Phan, A Cichocki, and Keiji Tanaka, Coding of faces by tensor components, ポスター発表 BibTeX
  60. 60. Kurikawa T, Handa T, Fukai T, Instability of neural trajectories in medial frontal cortex predicts individual differences in perceptual decision making., 2017 BibTeX
  61. 61. J. Matsumoto, H. Nishimaru, Y. Takamura, K. Mimura, A. Asaba, W. Suzuki, N. Ichinohe, T. Minamimoto, T. Ono, H. Nishijo, 3D-Tracker, an open-source 3D video based behavioral analysis system for laboratory animals for neuroscience, 2017 November BibTeX
  62. 62. Macpherson T, *Hikida T, Nucleus Accumbens D1 receptor expressing neurons control autoshaping behavior, 2017 BibTeX
  63. 63. *Nakajima H, Nakamura S, Kita A, Itakura M, Senami C, Kuwamura M, Hikida T, Azuma Y-T, Takeuchi T, Blockade of GAPDH nuclear translocation in the hippocampus contributes to anti-depressant-like action in stressed mice, 2017 BibTeX
  64. 64. Inoue K, Pathway-selective optogenetics for elucidating neural network function in primates., 2017 11, invited, Washington, DC, USA BibTeX
  65. 65. *Parmas P, Peters J, Doya K, The optimal-baseline estimator is not the optimal baseline-estimator, 2017 , Tokyo, Japan, 2017.11.10 BibTeX
  66. 66. T. Yoshimoto, J. Chikazoe, S. Okazaki, M. Sumiya, H. K. Takahashi, E. Nakagawa, T. Koike, R. Kitada, S. Okamoto, M. Nakata, H, Kosaka, T. Yada, N. Sadato, Abstractness of value representation in orbitofrontal cortex, 2017 , poster BibTeX
  67. 67. Mao Noguchi, So Fujimoto, Akihiko Nikkuni, Yutaka Komura*, Core of neural network for conscious states and percepts in primate, 2017 , invited BibTeX
  68. 68. *Sayuri Hashimoto and Ichiro Kobayashi, A Basic Study on Action Control Using Deep Reinforcement Learning, 2017 , reviewed BibTeX
  69. 69. *Hikida T, Neuronal circuit mechanisms in reward and aversive behavior., 2017 , invited BibTeX
  70. 70. *Masumori, A., Maruyama, N., Mita, T., Bakkum, D., Frey, U., Takahashi, H.,& Ikegami, T, Learning by Stimulation Avoidance in Cultured Neuronal Cells, 2017 , reviewed BibTeX
  71. 71. *Maruyama, N., Hashimoto, Y., Mototake, Y., Saito, D., & Ikegami, T, Revisiting Classification of Large Scale Flocking, 2017 , reviewed BibTeX
  72. 72. *Hashimoto, Y., Ikegami, T, Novelty production in tagging crowds, 2017 , reviewed BibTeX
  73. 73. Tanno, T., Horie, K., Izawa, J. and Morita, M, Robustness of Selective Desensitization Perceptron Against Irrelevant and Partially Relevant Features in Pattern Classification, 2017 11, 520-529 BibTeX
  74. 74. *Reinke C, Uchibe E, Doya K, Average Reward Optimization with Multiple Discounting Reinforcement Learners, 2017 , Guangzhou, China, 2017.10.24 BibTeX
  75. 75. Doya K, Artificial Intelligence and Brain Science, 2017 , Kyungpook National University, Daegu, Korea, 2017.10.19 BibTeX
  76. 76. Hidehiko Takahashi, Altered decision-making as endophenotypes to bridge the gap between phenomenology and neurobiology, 2017 , invited BibTeX
  77. 77. Takanori Uka, Neural mechanism of flexible sensory decision making, invited BibTeX
  78. 78. BibTeX
  79. 79. Masamichi Sakagami, Dissociable functions of reward inference in the lateral prefrontal cortex and the striatum, 2017 10, invited BibTeX
  80. 80. *Yoshizawa T, Ito M, Doya K, Coding of value information in the striatal striosome compartment, 2017 , CHÂTERAISÉ Gateaux Kingdom SAPPORO, Japan, 2017.10.4 BibTeX
  81. 81. Macpherson T, *Hikida T, Nucleus accumbens dopamine D1-receptor-expresing neurons control Pavlovian approach behaviour., 2017 BibTeX
  82. 82. Takufumi Yanagisawa, BMI robotic hand controls phantom limb pain, 2017 , invited BibTeX
  83. 83. *Masashi HAMAYA, Takamitsu MATSUBARA, Tomoyuki NODA,Tatsuya TERAMAE,Jun MORIMOTO, User-robot collaborative excitation for PAM model identification in exoskeleton robots, 2017 9, reviewed# BibTeX
  84. 84. *Yuki Tada, Yoshinobu Hagiwara, +Tadahiro Taniguchi, Comparative Study of Feature Extraction Methods for Direct Word Discovery with NPB-DAA from Natural Speech Signals, 2017 9, reviewed# BibTeX
  85. 85. *Yuusuke Miyuki, Yoshinobu Hagiwara and +Tadahiro Taniguchi, Unsupervised Learning for Spoken Word Production based on Simultaneous Word and Phoneme Discovery without Transcribed Data, 2017 9, reviewed# BibTeX
  86. 86. Hayakawa T, Fukai T, Interplay of microscopic and macroscopic dynamics in randomly connected neuronal networks. , 2017 BibTeX
  87. 87. *Sinapayen, L., & Ikegami, T, Online fitting of computational cost to environmental complexity: Predictive coding with the ε-network, 2017 , reviewed BibTeX
  88. 88. *Hiroki Kojima,Takashi Ikegami, VAEGAN as a New Perception Model, 2017 , reviewed BibTeX
  89. 89. *Masumori, A., Sinapayen, L., & Ikegami, T, Learning by Stimulation Avoidance Scale to Large Neural Networks, 14th European Conference on Artificial Life, 2017 , reviewed BibTeX
  90. 90. *Doi, I., Ikegami, T., Masumori, A., Kojima, H., Ogawa K., & Ishiguro, H, A new design principle for an autonomous robot, 2017 , reviewed BibTeX
  91. 91. Nakahara H., Reinforcement learning with environmental structures and mind of others, 2017 , invited talk BibTeX
  92. 92. Akihiko Nikkuni, Yutaka Komura*, Self-evaluation in vision in monkeys and humans, 2017 , invited BibTeX
  93. 93. Macpherson T, *Hikida T, Nucleus accumbens dopamine D1-receptor-expresing neurons control attribution of incentive salience in an autoshaping task., 2017 BibTeX
  94. 94. Doya K, Reinforcement learning: basic concepts and recent advances, 2017 , Beijing Institute of Technology, China, 2017.8.16-18, invited BibTeX
  95. 95. Doya K, Neural mechanisms of reinforcement learning and mental simulation, 2017 , Beijing Institute of Technology, China, 2017.8.16-18, invited BibTeX
  96. 96. T. Yanagisawa, R. Fukuma, B. Seymour, K. Hosomi, H. Kishima, T. Shimizu, H. Yokoi, M. Hirata, T. Yoshimine, Y. Kamitani, Y. Saitoh, Magnetoencephalographic-based brain–machine interface robotic hand for controlling sensorimotor cortical plasticity and phantom limb pain, 2017 , invited BibTeX
  97. 97. *Shirasuna, M., Honda, H., Matsuka, T., & Ueda, K., Familiarity-matching in decision making: Experimental studies on cognitive processes and analyses of its ecological rationality, 2017 7, reviewed# BibTeX
  98. 98. *Honda, H., Matsuka, T., & Ueda, K., Decisions based on verbal probabilities: Decision bias or decision by sampling?, 2017 7, reviewed# BibTeX
  99. 99. Koki Mimura, Chika Sato, Jumpei Matsumoto, Ichio Aoki, Noritaka Ichinohe, Suhara Testuya, Takafumi Minamimoto, Atypical Behavioral and Neural Phenotypes in a Common Marmoset Model of Autism Spectrum Disorder, 2017 July, invited BibTeX
  100. 100. Doya K, Decoding the contents of mental simulation, 2017 , University of Tokyo, Tokyo, 2017.7.24, invited BibTeX
  101. 101. Asabuki T, Hiratani N, Fukai T, Chunking by mutual supervision in reservoir computing., 2017 , invited BibTeX
  102. 102. Doya K, Exploring the deep brain network for reinforcement learning, 2017 , Makuhari Messe, Chiba, 2017.7.21, invited BibTeX
  103. 103. Ma N, Harasawa N, Ueno K, Ichinohe N, Haruno M, Cheng K, Nakahara H., Neural mechanisms of predicting others’ decisions for one’s better decisions, 2017 BibTeX
  104. 104. Fukuda H, Ma N, Suzuki S, Harasawa N, Ueno K, Gardner JL, Ichinohe N, Haruno M, Cheng K, Nakahara H. , Neural processes for converting social value into one’s own decision value, 2017 BibTeX
  105. 105. Tanaka S, Kawasaki K, Hasegawa I, Suzuki T, Kawato M and Sakagami M, Distribution of value related information in the multiple areas of the macaque prefrontal cortex, 2017 7 BibTeX
  106. 106. Keiji Tanaka, Object recognition in inferotemporal cortex: from visual features to semantics, 基調講演, invited BibTeX
  107. 107. Eiji Uchibe* and Kenji Doya, Model-free deep inverse reinforcement learning by logistic regression, 2017 , reviewed# BibTeX
  108. 108. Doya K, What should we further learn from the brain?, 2017 , NYU Shanghai, Shanghai, China, 2017.7.7, invited BibTeX
  109. 109. Nakahara H., Learning to make reward-guided decisions: sequential, successive, and social, 2017 , invited talk BibTeX
  110. 110. Doya K, What should we further learn from the brain?, 2017 , Kyungpook National University, Daegu, Korea, 2017.7.3, invited BibTeX
  111. 111. Keiji Tanaka, Object recognition in inferotemporal cortex: from visual features to semantics, 基調講演, invited BibTeX
  112. 112. Inoue K, Fujiwara M, Uezono S, Tanabe S, Ishida H, Hoshi E, Takada M, Organization of multisynaptic inputs from the basal ganglia to the premotor cortex in macaque monkeys – Retrograde transneuronal dual tracing using rabies viral vectors., 2017 6, 蘇州市, 中華人民共和国 BibTeX
  113. 113. Macpherson T, *Hikida T, Nucleus accumbens dopamine D1-receptor-expresing neurons control incentive salience to reward-predictive cues., 2017 , invited BibTeX
  114. 114. *Hikida T, Morita M, Macpherson T, D2L receptor-expressing striatal neurons control visual discrimination learning in a touchscreen operant system., 2017 BibTeX
  115. 115. Ishida H, Inoue K, Hoshi E, Takada M, Cells of origin of multisynaptic projections from amygdala to ventral premotor cortex in macaques , 2017 6, Sicily, Italy BibTeX
  116. 116. Masamichi Sakagami, Categorical coding of stimulus and inference of the value in the monkey lateral prefrontal cortex, 2017 6, invited BibTeX
  117. 117. *Reinke C, Uchibe E, Doya K, Fast Adaptation of Behavior to Changing Goals with a Gamma Ensemble, 2017 , University of Michigan, Ann Arbor, Michigan, USA, 2017.6.12 BibTeX
  118. 118. Fukuda H, Ma N, Suzuki S, Harasawa N, Ueno K, Gardner JL, Ichinohe N, Haruno M, Cheng K, Nakahara H. , Neural mechanisms for social value conversion in decision-making, 2017 BibTeX
  119. 119. *Koji ISHIHARA,Jun-ichiro FURUKAWA,Jun MORIMOTO, A forward and inverse optimal control framework to generate humanoid robot movements with hierarchical MPC, 2017 6, reviewed# BibTeX
  120. 120. *Tadahiro Taniguchi, Semantic Segmentation of Driving Behavior Data: Double Articulation Analyzer and its Application, 2017 , invited BibTeX
  121. 121. *Masashi HAMAYA, Takamitsu MATSUBARA, Tomoyuki NODA,Tatsuya TERAMAE,Jun MORIMOTO, Learning task-parametrized assistive strategies for exoskeleton robots by multi-task reinforcement learning, 2017 5,6, reviewed# BibTeX
  122. 122. Koki Mimura, Keiko Nakagaki, Noritaka Ichinohe, Distrbed Vocal Communication in Common Marmoset Family with an Autism-Model Child, 2017 May, invited BibTeX
  123. 123. Nakahara H., Neural computations underlying social decision-making, 2017 , invited talk BibTeX
  124. 124. Nakahara H., Learning to make reward-guided decisions: sequential, successive, and social, 2017 , invited talk BibTeX
  125. 125. Hidehiko Takahashi, Interface between AI and psychiatric research. , 2017 , invited BibTeX
  126. 126. Masamichi Sakagami, Categorical Coding of Stimulus and Inference of the Value in the Monkey Lateral Prefrontal Cortex, 2017 5 BibTeX
  127. 127. Shubham Gupta, R Allen Waggoner, Keiji Tanaka, and Kang Cheng, Variation of RF-induced temperature increase in a phantom: comparison of numerical simulatitons, MR thermometry and measurements from temperature sensors, ポスター発表 BibTeX
  128. 128. R Allen Waggoner, Ken Ueno, J Pfeuffer, Keiji Tanaka, Kang Cheng, High-resolution fMRI of the visual system at 3T using zoomed excitation via Tx-SENSE, ポスター発表 BibTeX
  129. 129. Haga T, Fukai T, Dendritic computing gives a meso-scopic level framework of brain’s leaning rule. , 2017 , invited BibTeX
  130. 130. J. Chikazoe, Integrated Taste Type Representations in Human Insula, 2017 , oral BibTeX
  131. 131. T. Yoshimoto, J. Chikazoe, S. Okazaki, M. Sumiya, H. K. Takahashi, E. Nakagawa, T. Koike, R. Kitada, S. Okamoto, M. Nakata, H, Kosaka, T. Yada, N. Sadato, State-dependent and -independent representations of food revealed by multi voxel pattern analysis, 2017 , poster BibTeX
  132. 132. Masamichi Sakagami, The reward prediction error signal of midbrain dopamine neuron is modulated by the cost paid for the reward, 2017 4, invited BibTeX
  133. 133. Doya K, Coding of action and state values in the striatal compartments, 2017 , Merida, Mexico, 2017.3.29, invited BibTeX
  134. 134. Keiji Tanaka, Changes in deoxygenation level of bloods and cortical tissues following neuronal activity changes as the bases for intrinsic optical recordings and fMRI, シンポジウム, invited BibTeX
  135. 135. Abekawa Naotoshi, Gomi Hiroaki, Modulation difference in visuomotor responses in implicit and explicit motor tasks depending on postural stability, 2017 BibTeX
  136. 136. Ito Sho, Gomi Hiroaki, Rotated visual feedback of self-movement affects long-latency stretch reflex, 2017 BibTeX
  137. 137. Takamuku Shinya, Abekawa Naotoshi,Gomi Hiroaki, Automatic adjustment of walking speed by optic flow benefits from binocular vision, 2017 BibTeX
  138. 138. Ueda Hiroshi, Abekawa Naotoshi,Gomi Hiroaki, Temporal development of an interaction effect between internal motion and contour signals of drifting target on reaching adjustment, 2017 BibTeX
  139. 139. *Tadahiro Taniguchi, Symbol Emergence in Robotics: Representation Learning for Real-world Communication and Collaboration, 2017 , invited BibTeX
  140. 140. Ikegami, T, THE FUTURE OF AI IN THE ARTS , 2017 , invited BibTeX
  141. 141. Ikegami, T, 2017 , invited BibTeX
  142. 142. Ikegami, T, 2017 , invited BibTeX
  143. 143. S. Shinomoto, Emergence of cascades in the linear and nonlinear Hawkes processes, 2017 , invited BibTeX
  144. 144. S. Shinomoto, Inferring the source of fluctuation in neuronal activity, 2017 June, invited BibTeX
  145. 145. Wataru Shimoda and Keiji Yanai, Predicting Segmentation Easiness from the Consistency for Weakly-Supervised Segmentation, 2017 11, reviewed BibTeX
  146. 146. Takumi Ege and Keiji Yanai, Estimating Food Calories for Multiple-dish Food Photos, 2017 11, reviewed# BibTeX
  147. 147. Takumi Ege and Keiji Yanai, Image-Based Food Calorie Estimation Using Knowledge on Food Categories, Ingredients and Cooking Directions, 2017 10, reviewed# BibTeX
  148. 148. Takumi Ege and Keiji Yanai, Comparison of Two Approaches for Direct Food Calorie Estimation, 2017 9, reviewed# BibTeX
  149. 149. Shin Matsuo, Wataru Shimoda and Keiji Yanai, Partial Style Transfer Using Weakly-Supervised Semantic Segmentation, 2017 7, reviewed BibTeX
  150. 150. Keiji Yanai and Ryosuke Tanno, Conditional Fast Style Transfer Network, 2017 6, reviewed BibTeX
  151. 151. Takumi Ege and Keiji Yanai, Simultaneous Estimation of Food Categories and Calories with Multi-task CNN, 2017 5, reviewed BibTeX
  152. 152. Keiji Yanai, Unseen Style Transfer Based on a Conditional Fast Style Transfer Network, 2017 4, reviewed BibTeX
  153. 153. Shin Matsuo, Wataru Shimoda and Keiji Yanai, Twitter Photo Geo-Localization Using Both Textual and Visual Features, 2017 4, reviewed BibTeX
  154. 154. Wataru Shimoda and Keiji Yanai, Learning Food Image Similarity for Food Image Retrieval, 2017 4, reviewed BibTeX
  155. 155. *Koji Ishihara,Jun Morimoto, A hierarchical model predictive control approach to generate biped robot movements in real-time, 2016 12, reviewed# BibTeX
  156. 156. Nakahara H., Neural computations for making decisions with others’ choice and reward, 2016 , invited talk BibTeX
  157. 157. *Hikida T, Yao S, Fukakusa A, Morita M, Kimura H, Hirai K, Ando T, Toyoshiba H, Sawa A, Expression changes in prefrontal cortex after neurotransmission blocking of the nucleus accumbens pathways., 2016 BibTeX
  158. 158. Masamichi Sakagami, Decoding the value of juice from electrocorticographic signals in monkey prefrontal cortices and its modulation through the decoded neurofeedback, 2016 12 BibTeX
  159. 159. Masamichi Sakagami, Signal interaction between primate prefrontal cortex and striatum in an asymmetric reward task, 2016 12 BibTeX
  160. 160. Mineki Oguchi and Masamichi Sakagami,, Elucidating the Function of the Prefronto-striatal Circuit of the Macaque Brain Using the Chemogenetic Double Virus Vector Infection　, 2016 12, invited BibTeX
  161. 161. *Hikida T, Activity in dopamine D2-receptor-expressing nucleus accumbens is necessary for behavioral flexibility in an IntelliCage place discrimination task., 2016 BibTeX
  162. 162. Ma N, Harasawa N, Ueno K, Ichinohe N, Haruno M, Cheng K, Nakahara H., Neural mechanisms for deciding with predicting others, 2016 BibTeX
  163. 163. Fukuda H, Ma N, Suzuki S, Harasawa N, Ueno K, Gardner JL, Ichinohe N, Haruno M, Cheng K, Nakahara H, Neural mechanisms and computation that mediates value by others’ reward for decision making, 2016 BibTeX
  164. 164. Mineki Oguchi, Shingo Tanaka, Xiaochuan Pan, Takefumi Kikusui, Shigeki Kato, Kazuto Kobayashi, and Masamichi Sakagami, Elucidating the Function of the Prefronto-striatal Circuit of the Macaque Brain Using the Double Virus Vector Infection , 2016 11 BibTeX
  165. 165. *Hikida T, Yao S, Fukakusa A, Morita M, Kimura H, Hirai K, Ando T, Toyoshiba H, Sawa A, Expression changes in prefrontal cortex after neurotransmission blocking of the nucleus accumbens pathways., 2016 BibTeX
  166. 166. Morita M, Macpherson T, Sawa A, *Hikida T, Learning deficits involving nucleus accumbens D2-receptor expressing neurons in a DISC1 mouse model., 2016 BibTeX
  167. 167. Itakura M, Kubo T, Kaneshige A, Azuma Y, Hikida T, Takeuchi T, *Nakajima H., A novel therapeutic target for stroke by inhibition of GAPDH aggregation., 2016 BibTeX
  168. 168. Macpherson T, Morita M, Wang Y, Sasaoka T, Sawa A, *Hikida T, Activity in dopamine D2-receptor-expressing nucleus accumbens is necessary for behavioral flexibility in an IntelliCage place discrimination task., 2016 , invited BibTeX
  169. 169. *Akira Taniguchi, Tadahiro Taniguchi, Angelo Cangelosi, Multiple Categorization by iCub: Learning Relationships between Multiple Modalities and Words, 2016 , reviewed BibTeX
  170. 170. *Tadahiro Taniguchi, Nonparametric Bayesian Word Discovery for Symbol Emergence in Robotics the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities, 2016 , invited BibTeX
  171. 171. *HaiLong Liu, Tadahiro Taniguchi, Kazuhito Takenaka, Yuusuke Tanaka, and Takashi Bando, Reducing the Negative Effect of Defective Data on Driving Behavior Segmentation Via a Deep Sparse Autoencoder, 2016 , reviewed BibTeX
  172. 172. *Ales Ude,Rok VugaA,Bojan Nemec,Jun Morimoto, Trajectory representation by nonlinear scaling of dynamic movement primitives, 2016 10, reviewed# BibTeX
  173. 173. *Masashi Hamaya, Takamitsu Matsubara, Tomoyuki Noda, Tatsuya Teramae, Jun Morimoto, Learning assistive strategies from a few user-robot interactions: Model-based reinforcement learning approac, 2016 10, reviewed# BibTeX
  174. 174. Masamichi Sakagami, Elucidating the Function of the Prefronto-striatal Circuit of the Macaque Brain Using the Double Virus Vector Infection, 2016 10, invited BibTeX
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