Baltieri, M., Iizuka, H., Witkowski, O., Sinapayen, L., & Suzuki, K. (2022), Hybrid Life: Integrating Biological, Artificial, and Cognitive Systems, arXiv.
Copinger, P. , Morales, P. A. (2022), Emergent spacetime from a momentum gauge and electromagnetism, arXiv.
Fujisawa, I., Kanai, R. (2022), Logical Tasks for Measuring Extrapolation and Rule Comprehension, arXiv.
Gallotta, R., Arulkumaran, K., & Soros, L. B. (2022), Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms, arXiv.
Oka,T., Takashima,K., Ueda,K., Mori,Y., Sasaki,K., Hamada,H.T., Yamagata,M., Yamada,Y. (2022), Autonomous, bidding, credible, decentralized, ethical, and funded (ABCDEF) publishing, PsyArXiv.
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.
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.
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


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), 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), A Pragmatic Look at Deep Imitation Learning, 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.


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.


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.


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.


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.


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.


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