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 bioRxiv, 2022
Gallotta, R., Arulkumaran, K., & Soros, L. B. 2022 Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation arXiv:2205.05834
Juliani, A., Arulkumaran, K., Sasai, S., & Kanai, R. 2022 On the link between conscious function and general intelligence in humans and machines Association for the Scientific Study of Consciousness
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
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:2203.13673
Hamada, H.T., & Kanai, R. 2022 AI agents for facilitating social interactions and wellbeing AAAI 2022 spring symposia
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
Matsui, T., Pham, Q,T. Jimura, K., & Chikazoe, J. 2022 On co-activation pattern analysis and non-stationarity of resting brain activity NeuroImage, 118904
Dai, T., Arulkumaran, K., Gerbert, T., Tukra, S., Behbahani, F., & Bharath, AA. 2022 Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation Neurocomputing
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, 2nd edition, 84-96
Bruineberg, J.,Dolega, K., Dewhurst, J., & Baltieri, M. 2021 The Emperor’s New Markov Blankets Cambridge Core
Shintaki, R.,Tanaka, D., Suzuki, S., Yoshimoto, T. Sadato, N., & Chikazoe, J. 2021 Anticipatory dynamics in the human brain guide foraging for primary rewards bioRxiv
Matsui, T., Taki,M., Pham, TQ., Chikazoe, J., Jimura, K. 2021 Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network arXiv:2110.14927 
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 bioRxiv
Highnam, K., Arulkumaran, K., Hanif, Z., & Jennings, N.R. 2021 BETH Dataset: Real Cybersecurity Data for Unsupervised Anomaly Detection Research Conference on Applied Machine Learning for Information Security
Pham, TQ., Nishiyama, S., Sadato, N. & Chikazoe, J. 2021 Distillation of Regional Activity Reveals Hidden Content of Neural Information in Visual Processing Front. Hum. Neurosci.
Morales, P.A., & Rosas, F.E. 2021 Generalization of the maximum entropy principle for curved statistical manifolds Phys. Rev. Research 3, 033216
Arulkumaran, K., & Lillrank, D.O. 2021 A Pragmatic Look at Deep Imitation Learning arXiv:2108.01867
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
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 2021
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 2021
VanRullen, R., & Kanai, R. 2021 Deep learning and the Global Workspace Theory Trends Neurosci. 2021;S0166-2236(21)00077-1
Morales, P.A., & Rosas, F.E. 2021 A generalization of the maximum entropy principle for curved statistical manifolds Physical Review Research 3, 033216
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, 036004
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
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
Biehl, M., & Kanai, R. 2020 Non-trivial informational closure of a Bayesian hyperparameter IEEE Symposium on Artificial Life (IEEE ALIFE)
Niikawa, T., Miyahara, K., Hamada, H.T., & Nishida, S. 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:1806.06505 [cs.AI]
Guttenberg, N., & Kanai, R. 2018 Learning to generate classifiers arXiv:1803.11373 [cs.LG]
Yu, Y., Chang, A., & Kanai, R. 2018 Boredom-Driven Curious Learning by Homeo-Heterostatic Value Gradients 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 1st Symposium on Advances in Approximate Bayesian Inference, 1–5
arXiv:1811.08241 [cs.AI]
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:1703.00039 [stat.ML]
Guttenberg, N., Yu, Y., & Kanai, R. 2017 Counterfactual Control for Free from Generative Models arXiv:1702.06676 [cs.LG]
Guttenberg, N., Biehl, M., & Kanai, R. 2017 Learning body-affordances to simplify action spaces arXiv:1708.04391 [cs]
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), ENEURO.0085-17.2017
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:1612.04530 [cs.CV]
Guttenberg, N., Biehl, M. & Kanai, R. 2016 Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks arXiv:1609.00116 [cs.AI]
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(1): 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