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
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.
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
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 2021: Trends in Artificial Intelligence pp 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 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
Kawakita, G., Kamiya, S., Sasai, S., Kitazono, J., & Oizumi M. 2021 Quantifying brain state transition cost via Schrödinger’s bridge bioRxiv, 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