Araya’s google scholar page

2024

Nakai, T., Constant-Varlet, C., & Prado, J. (2024), Encoding models for developmental cognitive computational neuroscience: Promise, challenges, and potential, Developmental Cognitive Neuroscience, 70, 101470.
Torresan, F., & Baltieri, M. (2024), Disentangled representations for causal cognition, Physics of Life Reviews, 51, 343-381.
Nakamura, D., Kaji, S., Kanai, R., & Hayashi, R. (2024), Unsupervised method for representation transfer from one brain to another, Frontiers in Neuroinformatics.
Hagita, N., Kanai, R., Ishiguro, H., Minamizawa, K., Arai, F., Shimpo, F. Matsumura, T., & Yamanishi, Y. (2024), Cybernetic Avatars: Teleoperation Technologies Applicable from In-body Monitoring to Social Interaction, Science Robotics.
García-Marín L. M., Campos A. I., Diaz-Torres S., Rabinowitz J. A., Ceja Z., Mitchell B. L., Grasby K. L., Thorp J. G., Agartz I., Alhusaini S., Ames D., Amouyel P., Andreassen O. A., Arfanakis K., Arias-Vasquez A., Armstrong N. J., Athanasiu L., Bastin M. E., Beiser A. S., Bennett D. A., Bis J. C., Boks M. P. M., Boomsma D. I., Brodaty H., Brouwer R. M., Buitelaar J. K., Burkhardt R., Cahn W., Calhoun V. D., Carmichael O. T., Chakravarty M., Chen Q., Ching C. R. K., Cichon S., Crespo-Facorro B., Crivello F., Dale A. M., Davey Smith G., de Geus E. J. C., De Jager P. L., de Zubicaray G. I., Debette S., DeCarli C., Depondt C., Desrivières S., Djurovic S., Ehrlich S., Erk S., Espeseth T., Fernández G., Filippi I., Fisher S. E., Fleischman D. A., Fletcher E., Fornage M., Forstner A. J., Francks C., Franke B., Ge T., Goldman A. L., Grabe H. J., Green R. C., Grimm O., Groenewold N. A., Gruber O., Gudnason V., Håberg A. K., Haukvik U. K., Heinz A., Hibar D. P., Hilal S., Himali J. J., Ho B. C., Hoehn D. F., Hoekstra P. J., Hofer E., Hoffmann W., Holmes A. J., Homuth G., Hosten N., Ikram M. K., Ipser J. C., Jack C. R. Jr, Jahanshad N., Jönsson E. G., Kahn R. S., Kanai R., Klein M., Knol M. J., Launer L. J., Lawrie S. M., Le Hellard S., Lee P. H., Lemaître H., Li S., Liewald D. C. M., Lin H., Longstreth W. T. Jr, Lopez O. L., Luciano M., Maillard P., Marquand A. F., Martin N. G., Martinot J.-L., Mather K. A., Mattay V. S., McMahon K. L., Mecocci P., Melle I., Meyer-Lindenberg A., Mirza-Schreiber N., Milaneschi Y., Mosley T. H., Mühleisen T. W., Müller-Myhsok B., Muñoz Maniega S., Nauck M., Nho K., Niessen W. J., Nöthen M. M., Nyquist P. A., Oosterlaan J., Pandolfo M., Paus T., Pausova Z., Penninx B. W. J. H., Pike G. B., Psaty B. M., Pütz B., Reppermund S., Rietschel M. D., Risacher S. L., Romanczuk-Seiferth N., Romero-Garcia R., Roshchupkin G. V., Rotter J. I., Sachdev P. S., Sämann P. G., Saremi A., Sargurupremraj M., Saykin A. J., Schmaal L., Schmidt H., Schmidt R., Schofield P. R., Scholz M., Schumann G., Schwarz E., Shen L., Shin J., Sisodiya S. M., Smith A. V., Smoller J. W., Soininen H. S., Steen V. M., Stein D. J., Stein J. L., Thomopoulos S. I., Toga A. W., Tordesillas-Gutiérrez D., Trollor J. N., Valdes-Hernandez M. C., van ′t Ent D., van Bokhoven H., van der Meer D., van der Wee N. J. A., Vázquez-Bourgon J., Veltman D. J., Vernooij M. W., Villringer A., Vinke L. N., Völzke H., Walter H., Wardlaw J. M., Weinberger D. R., Weiner M. W., Wen W., Westlye L. T., Westman E., White T., Witte A. V., Wolf C., Yang J., Zwiers M. P., Ikram M. A., Seshadri S., Thompson P. M., Satizabal C. L., Medland S. E., & Rentería M. E. (2024), Genomic analysis of intracranial and subcortical brain volumes yields polygenic scores accounting for variation across ancestries, Nature Genetics.
Fujisawa, I., Nobe, S., Seto, H., Onda, R., Uchida, Y., Ikoma, H., Chien, P., & Kanai R. (2024), ProcBench: Benchmark for Multi-Step Reasoning and Following Procedure, arXiv.
Yamane, Y., Li, Y., Matsumoto, K., Kanai, R., Desforges, M., Gutierrez, C. E., & Doya K. (2024), Optical Neuroimage Studio (OptiNiSt): intuitive, scalable, extendable framework for optical neuroimage data analysis, bioRxiv.
Taniguchi, T., Takagi, S., Otsuka, J., Hayashi, Y., & Hamada, H. T. (2024), Collective Predictive Coding as Model of Science: Formalizing Scientific Activities Towards Generative Science, arXiv.
Aguilera, M., Morales, P. A., Rosas, F. E., & Shimazaki, H. (2024), Explosive neural networks via higher-order interactions in curved statistical manifolds, arXiv.
Sato, M., Tomeoka, K., Horiguchi, I., Arulkumaran, K., Kanai, R., & Sasai S. (2024), Scaling Law in Neural Data: Non-Invasive Speech Decoding with 175 Hours of EEG Data, arXiv.
Nishida, S., Hamada, H. T., Niikawa, T., & Miyahara, K. (2024), Neural correlates of phenomenological attitude toward perceptual experience, bioRxiv.
Nakai, T., Tirou, C., & Prado, J. (2024), From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data, Imaging Neuroscience, 2, 1-24.
Pham, T. Q., Tran, H. X., Ly, H. H., Ishizuka, H., & Chikazoe, J. (2024), Decoding passive tactile shape from functional MRI signals, zenodo
Torresan, F., & Baltieri, M. (2024), Disentangled Representations for Causal Cognition, arXiv.
Kanai, R., & Fujisawa, I. (2024), Toward a universal theory of consciousness, Neuroscience of Consciousness, 2024(1).
Dossa, R. F. J., Arulkumaran,K., Juliani, A., Sasai, S., & Kanai, R. (2024), Design and Evaluation of a Global Workspace Agent Embodied in a Realistic Multimodal Environment, Frontiers in Computational Neuroscience, 18.
Hamada, H. T., Abe, Y., Tanaka, N., Taira, M., Tanaka, K. F., & Doya, K. (2024), Optogenetic activation of dorsal raphe serotonin neurons induces brain-wide activation, nature communications.
Taguchi, T., Kitazono, J., Sasai, S., & Oizumi, M. (2024), Association of bidirectional network cores in the brain with conscious perception and cognition, bioRxiv.
Constant-Vararlet, C., Nakai, T., & Prado, J. (2024), Intergenerational transmission of brain structure and function in humans: a narrative review of designs, methods, and findings, Brain Structure and Function.
Sato, M., Kabe, Y., Nobe, S., Yoshida, A., Inoue, M., Shimizu, M., Tomeoka., K., & Sasai, S. (2024), Delineating neural contributions to electroencephalogram-based speech decoding, bioRxiv.
Yoshida, A., Dossa, R. F. J., Sujit, S., Arulkumaran, K., Vincenzo, M. D., & Kuwabara, M. (2024), The Extensible Multi-Robot Multi-Goal Manipulation Benchmark for Human-Robot Interfaces, Supervised Autonomy Workshop
Arulkumaran, K., Vincenzo, M. D., Dossa R. F. J., Akiyama, S., Lillrank, D. O., Sato, M., Tomeoka, K., & Sasai, S. (2024), A Comparison of Visual and Auditory EEG Interfaces for Robot Multi-stage Task Control, Frontiers in Robotics and AI, 11.
Sun, Y., Fujisawa, I., Juliani, A., Sakuma, J., & Kanai, R. (2024), Remembering Transformer for Continual Learning, arXiv.
Akiyama, S., Dossa, R. F. J., Arulkumaran, K., Sujit, S., & Johns, E. (2024), Open-loop VLM Robot Planning: An Investigation of Fine-tuning and Prompt Engineering Strategies, Navigation and Manipulation Workshop
Morales, P. A., & Castro-Villarreal, P. (2024), Emergent Elastic Surfaces from Two-Dimensional Dirac Materials, arXiv.
Nakai, T., Kubo, R., & Nishimoto, S. (2024), Cortical representational geometry of diverse tasks reveals subject-specific and subject-invariant cognitive structures, bioRxiv.
Kanai, R., Takatsuki, R., & Fujisawa, I. (2024), Meta-Representations as Representations of Processes, PsyArXiv.
Kuwabara, M., & Kanai, R. (2024), Stimulation technology for brain and nerves, now and future, arXiv.
Hamada, H. T. (2024), 分散型科学が拓く新たなエコシステム：DeSci.Tokyoが果たす役割, 情報の科学と技術, 74(3), 86-91.
Arulkumaran, K., & Lillrank, D. O. (2024), A Pragmatic Look at Deep Imitation Learning, Asian Conference on Machine Learning , 58-73

2023

Denk, I. T., Takagi, Y., Matsuyama, T., Agostinelli, A., Nakai, T., Frank, C., & Nishimoto, S. (2023), BRAIN2MUSIC: RECONSTRUCTING MUSIC FROM HUMAN BRAIN ACTIVITY, arXiv.
Nakai, T., & Prado, J. (2023), From brain to education through machine learning: Predicting literacy and numeracy skills from neuroimaging data, PsyArXiv.
Matsumoto, D., & Nakai, T. (2023), Syntactic theory of mathematical expressions, Cognitive Psychology, Volume 146.
Lillrank, D. O., Akiyama, S., & Arulkumaran, K. (2023), Zero-Shot Object Manipulation with Semantic 3D Image Augmentation for Perceiver-Actor,
Akiyama, S., Lillrank, D. O., & Arulkumaran, K. (2023), Fine-Grained Object Detection and Manipulation with Segmentation-Conditioned Perceiver-Actor, ICRA2023 Workshop on Pretraining for Robotics (PT4R)
Kawamoto, M., Takagishi, H., Ishihara, T., Takagi, S., Kanai, R., Sugihara, G., Takahashi, H., & Matsuda, T. (2023), Hippocampal volume mediates the relationship of parental rejection in childhood with social cognition in healthy adults, Scientific Reports, 13.
Sun, Y., Ochiai, H., Wu, Y., Lin, S., & Kanai, R. (2023), Associative Transformer is a Sparse Representation Learner, arXiv.
Pham, T. Q., Matsui, T., & Chikazoe, J. (2023), Evaluation of the Hierarchical Correspondence between the Human Brain and Artificial Neural Networks, MDPI, 12(10), 1330.
Kanai, R., & Fujisawa, I. (2023), Towards a Universal Theory of Consciousness, PsyArXiv.
Hamada, H. T. (2023), 研究開発における新たな科学運営モデル, Jxiv, JST.
Matsumoto, D., & Nakai, T. (2023), Syntactic theory of mathematical expressions, Cognitive Psychology | Journal | ScienceDirect.com by Elsevier, 146.
Sato, A., Chikazoe, J., Funai, S., Mochihashi, D., Shikano, Y., Asahara, M., Iso, S., & Kobayashi, I. (2023), Investigation of Information Processing Mechanisms in the Human Brain During Reading Tanka Poetry, Springer Nature Switzerland, 407-418
Copinger, P. , & Morales, P. A. (2023), Emergent spacetime from a Berry-inspired dynamical gauge field coupled to electromagnetism, Physical Review D, 108(6).
Lee, D. H., & Chikazoe, J. (2023), A clearing in the objectivity of aesthetics?, Frontiers in Neuroimaging.
Saito, Y., Kamagata, K., Akashi, T., Wada, A., Shimoji, K., Hori, M., Kuwabara, M., Kanai, R., & Aoki, S. (2023), Review of Performance Improvement of a Noninvasive Brain-computer Interface in Communication and Motor Control for Clinical Applications, Juntendo Medical Journal, 69(4), 319-326.
Negi, R., Yoshida, A., Kuwabara, M., & Kanai, R. (2023), A Deep Learning Approach to Detecting Temporal Characteristics of Cortical Regions, bioRxiv.
Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constan, A., Deane,G., Fleming, S. M., Frith, C., Ji, X., Kanai, R., Klein, C., Lindsay, G., Michel, M., Mudrik, L., Peters, M. A. K., Schwitzgebel, E., Simon, J., & VanRullen, R. (2023), Consciousness in Artificial Intelligence: Insights from the Science of Consciousness, arXiv.
Oka, T., Takashima, K., Ueda, K., Mori, Y., Sasaki, K., Hamada, H. T., Yamagata, M., & Yamada, Y. (2023), Autonomous, bidding, credible, decentralized, ethical, and funded (ABCDEF) publishing [version 1; peer review: awaiting peer review], F1000Research.
Morales, P. A., Korbel, J., & Rosas, F. E. (2023), Thermodynamics of exponential Kolmogorov-Nagumo averages, New Journal of Physics - IOPscience.
Juliani, A., Safron, A., & Kanai, R. (2023), Deep CANALs: A Deep Learning Approach to Refining the Canalization Theory of Psychopathology, PsyArXiv.
Koyama, Y., Yamamoto, T., Hirayama, J., Jimura, K., Sadato, N., & Chikazoe, J. (2023), Cognitive Dynamics Estimation: A whole-brain spatial regression paradigm for extracting the temporal dynamics of cognitive processes, bioRxiv.
Hata, J., Nakae, K., Tsukada, H., Woodward, A., Haga, Y., Iida, M., Uematsu, A., Seki, F., Ichinohe, N., Gong, R., Kaneko, T., Yoshimaru, D., Watakabe, A., Abe, H., Tani, T., Hamada, H.T., Gutierrez, C.E., Skibbe, H., Maeda, M., Papazian, F., Hagiya, K., Kishi, N., Ishii, S., Doya, K., Shimogori, T., Yamamori, T., Tanaka, K., Okano H.J., & Okano, H. (2023), Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range, Scientific Data, 10, 1-8.
Murakami, S., Dan, K., Seo, T., Yamazaki, T., Cho, M., Higuchi, M., Matsuyoshi, D., Kanai, R., Aizawa, Y., & Yamada, M. (2023), A human machine interface suggested from neuroscientific analysis of human factor, 27th International Technical Conference on the Enhanced Safety of Vehicles (ESV)
Morales, P. A., Korbel, J., & Rosas, F. E. (2023), Geometric Structures Induced by Deformations of the Legendre Transform, Entropy, 25(4), 678.
Gallotta, R., Arulkumaran, K., & Soros, L. B. (2023), Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms, IEEE Transactions on Games, 16(2), 303 - 316.
Morales, P. A., & Copinger, P. (2023), Curvature-induced pseudogauge fields from time-dependent geometries in graphene, PHYSICAL REVIEW B, 107(7), 75432.
Kamiya, S., Kawakita, D., Sasai, S., Kitazono, J., & Oizumi, M. (2023), Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems, Journal of Neuroscience, 43, 270-281.
Marshall, W., Grasso, M., Mayner, W. G. P., Zaeemzadeh, A., Barbosa, S. L., Chastain, E., Findlay, G., Sasai, S., Albantakis, L., & Tononi, G. (2023), System Integrated Information, Entropy, 25(2), 335.
Baltieri, M., Iizuka, H., Witkowski, O., Sinapayen, L., & Suzuki, K. (2023), Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems, WIREs Cognitive Science.

2022

Albantakis, L., Barbosa, L., Findlay, D., Grasso, M., Haun, M. A., Marshall, W., Mayner, W. G. P., Zaeemzadeh, A., Boly, M., Juel, E. B., Sasai, S., Fujii, K., David, I., Hendren, J., Lang, P. J., & Tononi, G. (2022), Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms, arXiv.
Fujisawa, I., & Kanai, R. (2022), Logical Tasks for Measuring Extrapolation and Rule Comprehension, arXiv.
Terai, A., Yamamura, N., Chikazoe, J., Yoshimoto, T., Sadato, N., & Jimura, K. (2022), On the role of shape features in metaphor generation for abstract images, 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS&ISIS), 1-5
Fermin, A. S. R., Kiyonari, T., Matsumoto, Y., Takagishi, H., Li, Y., Kanai, R., Sakagami, M., Akaishi, R., Ichikawa,N., Takamura, M., Yokoyama, S., Machizawa, M. G., Chan, H., Matani, A., Yamawaki, S., Okada, G., Okamoto, Y., & Yamagishi, T. (2022), The neuroanatomy of social trust predicts depression vulnerability, Scientific Reports, 12, 16724.
Bruineberg, J., Dołęga, K., Dewhurst, J., & Baltieri, M. (2022), The Emperor Is Naked: Replies to commentaries on the target article, Behavioral and Brain Sciences, 45, e219.
Fordson H. P., Gardhouse, K., Cicero, N., Chikazoe ,J. Anderson, A., & Derosa, E. (2022), A Novel Deep Learning Based Emotion Recognition Approach to well Being from Fingertip Blood Volume Pulse, 2022 International Conference on Machine Learning and Cybernetics (ICMLC), 130-137
Fordson H. P., Gardhouse, K., Cicero, N., Chikazoe ,J. Anderson, A., & Derosa, E. (2022), A Novel Deep Learning Based Emotion Recognition Approach to well Being from Fingertip Blood Volume Pulse, 2022 International Conference on Machine Learning and Cybernetics (ICMLC), 130-137
Pham, T. Q., Ly H. H., Hiroki, I., & Chikazoe, J. (2022), Design of an fMRI-compatible pneumatic tactile array for spatiotemporal stimulation, 2022 61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 1343-1346
Pham, T. Q., Ly H. H., Hiroki, I., & Chikazoe, J. (2022), Design of an fMRI-compatible pneumatic tactile array for spatiotemporal stimulation, 2022 61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 1343-1346
Hamada, H. T. (2022), Reconstruction of Science with Web3 Technology, Jxiv
Morales, P. A., & Copinger, P. (2022), Curvature induced pseudo-gauge fields from time-dependent geometries in graphene, arXiv.
Hamada, H. T., Abe, Y., Takata, N., Taira, M., Tanaka, K. F., & Doya, K. (2022), Optogenetic activation of dorsal raphe serotonin neurons induces a brain-wide response in reward network, bioRxiv.
Juliani, A., Arulkumaran, K., Sasai, S., & Kanai, R. (2022), On the link between conscious function and general intelligence in humans and machines, Transactions on Machine Learning Research.
Song, C., Sandberg, K., Rutiku, R., & Kanai, R. (2022), Linking human behaviour to brain structure: further challenges and possible solutions, Nature Reviews Neuroscience.
Juliani, A., Kanai, R., & Sasai, S. (2022), The Perceiver Architecture is a Functional Global Workspace, Proceedings of the Annual Meeting of the Cognitive Science Society, 44
Chikazoe, J. (2022), Refining the negative into general and specific, Nature Neuroscience, 25, 678–683.
Youssef, M. M. M., Hamada, H. T., Lai, E. S. K., Kiyama, Y., Tabbal, M. E., Kiyonari, H., Nakano, K., Kuhn, B., & Yamamoto, T. (2022), TOB is an effector of the hippocampus-mediated acute stress response, Translational Psychiatry, 12(302).
Gallotta, R., Arulkumaran, K., & Soros, L. B. (2022), Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation, IEEE Conference on Games
Kawakita, G., Kamiya, S., Sasai, S., Kitazono, J., & Oizumi, M. (2022), Quantifying brain state transition cost via Schrödinger’s bridge, Network Neuroscience, 6(1), 118–134.
Niikawa, T., Miyahara, K., Hamada, H. T., & Nishida, S. (2022), Functions of consciousness: conceptual clarification, Neuroscience of Consciousness, 2022(1).
Arulkumaran, K., Ashley, D. R., Schmidhuber, J., & Srivastava, R. K. (2022), All You Need Is Supervised Learning: From Imitation Learning to Meta-RL With Upside Down RL, Multi-disciplinary Conference on Reinforcement Learning and Decision Making.
Ashley, D. R., Arulkumaran, K., Schmidhuber, J., & Srivastava, R. K. (2022), Learning Relative Return Policies With Upside-Down Reinforcement Learning, Multi-disciplinary Conference on Reinforcement Learning and Decision Making.
Galotta, R., Arulkumaran, K., & Soros, L. B. (2022), Evolving Spaceships with a Hybrid L-system Constrained Optimisation Evolutionary Algorithm, Genetic and Evolutionary Computation Conference
Arulkumaran, K., & Nguyen-Phuoc, T. (2022), Minimal Criterion Artist Collective, Genetic and Evolutionary Computation Conference
Morales, P. A., Korbel, J., & Rosas, F. E. (2022), Ode to Legendre: Geometric and thermodynamic implications on curved statistical manifolds, arXiv.
Hamada, H. T., & Kanai, R. (2022), AI agents for facilitating social interactions and wellbeing, arXiv.
Yoshimoto, T., Okazaki, S., Sumiya, M., Takahashi, K. H., Nakagawa, E., Koike, T., Kitada, R., Okamoto, S., Nakata, M., Yada, T., Kosaka, H., Sadato, N., & Chikazoe, J. (2022), Coexistence of sensory qualities and value representations in human orbitofrontal cortex, Neuroscience Research, 180, 48-57.
Matsui, T., Pham, T. Q., Jimura,K., & Chikazoe, J. (2022), On co-activation pattern analysis and non-stationarity of resting brain activity, NeuroImage, 249, 118904.
Matsui, T., Taki, M., Pham, T. Q., Chikazoe, J., & Jimura, K. (2022), Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network, Frontiers in Neuroinformatics, 15.
Shintaki, R., Tanaka, D., Suzuki, S., Yoshimoto, T., Sadato, N., & Chikazoe, J. (2022), Subjectivity of time perception alters choice preference for future rewards through fronto-striatal value signal dynamics, bioRxiv.
Dai, T., Arulkumaran, K., Gerbert, T., Tukra, S., Behbahani, F., & Bharath, A. A. (2022), Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation, Neurocomputing, 493, 143-165.
Langdon, A., Botvinick, M., Nakahara, H., Tanaka, K., Matsumoto, M., & Kanai, R. (2022), Meta-learning, social cognition and consciousness in brains and machines, Neural Networks, 145, 80-89.
Hamada, H. T., Matsuyoshi, D., & Kanai, R. (2022), Gray matter analysis of MRI images: Introduction to current research practice, Encyclopedia of Behavioral Neuroscience, 2, 84-96

2021

Virgo N., Biehl M., & McGregor, S. (2021), Interpreting Dynamical Systems as Bayesian Reasoners, In: , et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, 1524
Bruineberg, J.,Dolega, K., Dewhurst, J., & Baltieri, M. (2021), The Emperor's New Markov Blankets, Behavioral and Brain Sciences.
Shintaki, R., Tanaka, D., Suzuki, S., Yoshimoto, T., Sadato, N., & Chikazoe, J. (2021), Continuous decision to wait for a future reward is guided by fronto-hippocampal anticipatory dynamics, bioRxiv.
Shintaki, R., Tanaka, D., Suzuki, S., Yoshimoto, T., Sadato, N., & Chikazoe, J. (2021), Human foraging for primary rewards is guided by fronto-hippocampal dynamics of anticipation, bioRxiv.
Tsumura, K., Shintaki, R., Takeda, M., Chikazoe, J., Nakahara, K., & Jimura, K. (2021), Perceptual uncertainty alternates top-down and bottom-up fronto-temporal network signaling during response inhibition, The Journal of Neuroscience, 42(22), 4567-4579.
Pham, T. Q., Nishiyama, S., Sadato, N., & Chikazoe, J. (2021), Distillation of Regional Activity Reveals Hidden Content of Neural Information in Visual Processing, Front. Hum. Neurosci., 26 November 2021.
Highnam, K., Arulkumaran, K., Hanif, Z., & Jennings, N. R. (2021), BETH Dataset: Real Cybersecurity Data for Anomaly Detection Research, Conference on Applied Machine Learning for Information Security
Morales, P. A., & Rosas, F. E. (2021), Generalization of the maximum entropy principle for curved statistical manifolds, Phys. Rev. Research, 3(3), 33216.
Arulkumaran, K., & Lillrank, D. O. (2021), bioRxiv, arXiv.
Arulkumaran, K., & Lillrank, D. O. (2021), bioRxiv, arXiv.
Dai, T., Liu, H., Arulkumaran, K., Ren, G., & Bharath, A. A. (2021), Diversity-Based Trajectory and Goal Selection with Hindsight Experience Replay, Pacific Rim International Conference on Artificial Intelligence, 32–45
Matsumoto, K., Tamai, S., & Kanai, R. (2021), Goal-Directed Planning by Predictive-Coding based Variational Recurrent Neural Network from Small Training Samples, IEEE International Conference on Development and Learning, 1‐6
Massari, F., Biehl, M., Meeden, L. & Kanai, R. (2021), Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments, IEEE International Conference on Development and Learning, 1‐6
VanRullen, R., & Kanai, R. (2021), Deep learning and the Global Workspace Theory, Trends Neurosci, 44(9), 692‐704.
Biehl, M., Pollock, F., & Kanai, R. (2021), A Technical Critique of Some Parts of the Free Energy Principle, Entropy , 23(3), 293.
Copinger, P., & Morales, P. (2021), Schwinger pair production in SL(2,C) topologically nontrivial fields via non-Abelian worldline instantons, Physical Review D, 103, 36004.

2020

Rosas, F. E., Mediano, P. A. M., Biehl, M., Chandaria, S., & Polani, D. (2020), Causal Blankets: Theory and Algorithmic Framework, International Workshop on Active Inference (IWAI) 2020: Active Inference, 187-198, 187–198
Biehl, M., & Kanai, R. (2020), Dynamics of a Bayesian Hyperparameter in a Markov Chain, International Workshop on Active Inference (IWAI) 2020: Active Inference, 35-41, 35–41
Biehl, M., & Kanai, R. (2020), Non-trivial informational closure of a Bayesian hyperparameter, IEEE Symposium on Artificial Life (IEEE ALIFE)
Yoshimoto, T., Okazaki, S., Sumiya, M., Takahashi, K. H., Nakagawa, E., Koike, T., Kitada, R., Okamoto, S., Nakata, M,. Yada, T., Kosaka, H., Sadato, N., & Chikazoe, J. (2020), A new experimental phenomenological method to explore the subjective features of psychological phenomena: its application to binocular rivalry, Neuroscience of Consciousness, 2020(1), niaa018.
Abe, Y., Takata, N., Sakai, Y., Hamada, H. T., Hiraoka Y., Aida, T., Tanaka, K., Le Bihan, D., Doya, K., & Tanaka, K. F. (2020), Diffusion functional MRI reveals global brain network functional abnormalities driven by targeted local activity in a neuropsychiatric disease mouse model, NeuroImage, 223, 117318.
Kitazono, J., Kanai, R., & Oizumi, M. (2020), Efficient search for informational cores in complex systems: Application to brain networks, Neural Networks, 132, 232-244.
Chang, A. Y. C., Biehl, M., Yu, Y., & Kanai, R. (2020), Information Closure Theory of Consciousness, Frontiers in Psychology, 11, 1504.
Miyahara, K., Niikawa, T., Hamada, H. T., & Nishida, S. (2020), Developing a short-term phenomenological training program: A report of methodological lessons., New Ideas in Psychology, 58, 100780.

2019

Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., ., …, Kanai, R., …, Thompson, P. M., & Medland, S. E. (2019), The genetic architecture of the human cerebral cortex, Science, 367(6484), eaay6690.
Kumrai, T., Korpela, J., Maekawa, T., Yu, Y., & Kanai, R. (2019), Human Activity Recognition with Deep Reinforcement Learning using the Camera of a Mobile Robot, 2020 IEEE International Conference on Pervasive Computing and Communications, 125-134
Satizabal, C. L., Adams, H. H. H., Hibar, D. P., White, C. C., …, Kanai, R., …, & Ikram, M. A. (2019), Genetic architecture of subcortical brain structures in 38,851 individuals, Nature Genetics, 51, 1624-1636.
Kanai, R., Chang, A., Yu, Y., Magrans de Abril, I., Biehl, M., & Guttenberg, N. (2019), Information generation as a functional basis of consciousness, Neuroscience of Consciousness, 5(1), niz016.
Protopapa, F., Hayashi, M. J., van der Zwaag, D., Battistella, G., Murray, M. M., Kanai, R., & Bueti, D. (2019), Chronotopic maps in human supplementary motor area, PLoS Biology, 17(3), e3000026.
Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M. (2019), An Information Theoretic Approach to Reveal the Formation of Shared Representation, Frontiers in Computational Neuroscience, 14(1).
Mao, Y., Kanai, R., Ding, C., Bi, T., & Qiu, J. (2019), Temporal variability of brain networks predicts individual differences in bistable perception, Neuropsychologia, 142, 107426.

2018

Magrans de Abril, I., & Kanai, R. (2018), A unified strategy for implementing curiosity and empowerment driven reinforcement learning, arXiv.
Guttenberg, N., & Kanai, R. (2018), Learning to generate classifiers, arXiv.
Yu, Y., Chang, A., & Kanai, R. (2018), Boredom-Driven Curious Learning by Homeo-Heterostatic Value Gradient, Frontiers in Neurorobotics, 12, 88.
Hayashi, M., van der Zwaag, W., Bueti, D., & Kanai, R. (2018), Representations of time in human frontoparietal cortex, Communications Biology, 1(1), 233.
Hidaka, S., & Oizumi, M. (2018), Fast and exact search for the partition with minimal information loss, PLoS One, 13(9), e0201126.
Magrans de Abril, I., & Kanai, R. (2018), Curiosity-Driven Reinforcement Learning with Homeostatic Regulation, 2018 International Joint Conference on Neural Networks (IJCNN) , 1-6
Amari, S., Karakida, R., & Oizumi, M. (2018), Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem, Information Geometry, 1, 13-37.
Guttenberg, N., Biehl, M., Virgo, N., & Kanai, R. (2018), Being curious about the answers to questions: novelty search with learned attention, Artificial Life Conference Proceedings, 30, 518-525
Biehl, M. (2018), Geometry of Friston’s active inference, arXiv
Biehl, M., Guckelsberger, C., Salge, C., Smith, S. C., & Polani, D. (2018), Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop, Frontiers in Neurorobotics, 12, 45.
Mori, H., & Oizumi, M. (2018), Information integration in a globally coupled chaotic system, Artificial Life Conference Proceedings, 384-385
Kitazono, J., Kanai, R., & Oizumi, M. (2018), Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory, Entropy, 20(3), 173.

2017

Mizutani, H., & Kanai, R. (2017), A description length approach to determining the number of k-means clusters, arXiv.
Guttenberg, N., Yu, Y., & Kanai, R. (2017), Counterfactual Control for Free with Generative Models, arXiv.
Guttenberg, N., Biehl, M., & Kanai, R. (2017), Learning body-affordances to simplify action spaces, arXiv.
Haun, A. M., Oizumi, M., Kovach, C. K., Kawasaki, H., Oya, H., Howard, M. A., Adolphs, R., & Tsuchiya, N. (2017), Conscious Perception as Integrated Information Patterns in Human Electrocorticography, eNeuro, 4(5), 0085-17.
Magrans de Abril, I., & Kanai, R. (2017), Intrinsically-motivated reinforcement learning for control with continuous actions, 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 212-214
Tajima, S., & Kanai, R. (2017), Integrated information and dimensionality in continuous attractor dynamics, Neuroscience of Consciousness, 2017(1), nix011.
Biehl, M., & Polani, D. (2017), Action and perception for spatiotemporal patterns, Proceedings of ECAL 2017 the 14th European Conference on Artificial Life, 14, 68–75
Biehl, M., Ikegami, T., & Polani, D. (2017), Specific and Complete Local Integration of Patterns in Bayesian Networks, Entropy, 19(5), 230.
Otten, M., Pinto, Y., Paffen, C. L. E., Seth, A. K., & Kanai, R. (2017), The Uniformity Illusion: Central Stimuli Can Determine Peripheral Perception, Psychological Science, 28(1), 56–68.

2016

Guttenberg, N., Virgo, N., Witkowski, O., Aoki, H., & Kanai, R. (2016), Permutation-equivariant neural networks applied to dynamics prediction, arXiv.
Guttenberg, N., Biehl, M., & Kanai, R. (2016), Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks, arXiv.
Wiener, M., & Kanai, R. (2016), Frequency tuning for temporal perception and prediction, Current Opinion in Behavioral Sciences, 8, 1-6.
Oizumi, M., Yanagawa, T., Amari, S., Fujii, N., & Tsuchiya, N. (2016), Measuring Integrated Information from the Decoding Perspective, PLoS Computational Biology, 12(11), e1004654.
Sherman, M. T., Seth, A. K., & Kanai, R. (2016), Predictions Shape Confidence in Right Inferior Frontal Gyrus, Journal of Neuroscience, 36, 10323-10336.

2015

Kanai, R. (2015), Neuroprofile: a web-based service for personalized neuroprediction from anatomical brain scans, UbiComp '15: The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 915–918