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Morales, P.A., & Rosas, F.E. A generalization of the maximum entropy principle for curved statistical manifolds, arXiv:2105.07953, https://arxiv.org/abs/2105.07953 (2021)
Biehl, M., Pollock, F., & Kanai, R. A Technical Critique of Some Parts of the Free Energy Principle, Entropy, 23(3), 293, https://www.mdpi.com/1099-4300/23/3/293 (2021)
Rosas, F. E., Mediano, P.A.M., Biehl, M., Chandaria, S., & Polani, D.: Causal blankets: Theory and algorithmic framework, International Workshop on Active Inference (IWAI) 2020: Active Inference, 187-198, https://link.springer.com/chapter/10.1007/978-3-030-64919-7_19 (2020)
Biehl, M., & Kanai, R.: Non-trivial informational closure of a Bayesian hyperparameter, IEEE Symposium on Artificial Life (IEEE ALIFE), https://ieeexplore.ieee.org/document/9308480 (2020)
Niikawa, T., Miyahara, K., Hamada, H.T., & Nishida, S. : A new experimental phenomenological method to explore the subjective features of psychological phenomena: its application to binocular rivalry , Neuroscience of Consciousness, 2020(1), niaa018, https://academic.oup.com/nc/article/2020/1/niaa018/5917596 (2020)
Abe, Y., Takata, N., Sakai, Y., Hamada, H.T., Hiraoka Y., Aida, T., Tanaka, K., Le Bihan, D., Doya, K., & Tanaka, K.F.: Diffusion functional MRI reveals global brain network functional abnormalities driven by targeted local activity in a neuropsychiatric disease mouse model, NeuroImage, 223, 117318, https://www.sciencedirect.com/science/article/pii/S1053811920308041 (2020)
Miyahara, K., Niikawa T., Hamada, H.T., & Nishida, S.: Developing a Short-term Phenomenological Training Program: A Report of Methodological Lessons., New Ideas in Psychology, 58, 100780, https://doi.org/10.1016/j.newideapsych.2020.100780 (2020)
Grasby, K.L., Jahanshad, N., Painter, J.N., Colodro-Conde, L.,., …, Kanai, R., …, Thompson, P.M., & Medland, S.E.: The genetic architecture of the human cerebral cortex, Science, 367(6484), eaay6690, https://science.sciencemag.org/content/367/6484/eaay6690.abstract (2019)
Kumrai, T., Korpela, J., Maekawa, T., Yu, Y., & Kanai, R.: 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, https://ieeexplore.ieee.org/abstract/document/9127376 (2019)
Satizabal, C.L., Adams, H.H.H., Hibar, D.P., White, C.C., …, Kanai, R., …, & Ikram, M.A.: Genetic architecture of subcortical brain structures in 38,851 individuals, Nature Genetics, 51, 1624-1636, https://www.nature.com/articles/s41588-019-0511-y (2019)
Protopapa, F., Hayashi, M.J., van der Zwaag, D., Battistella, G., Murray, M.M., Kanai, R., & Bueti, D.: Chronotopic maps in human supplementary motor area, PLoS Biology, 17(3), e3000026, https://doi.org/10.1371/journal.pbio.3000026 (2019)
Eguchi, A., Horii, T., Nagai, T., Kanai, R., & Oizumi, M.: An information theoretic approach to reveal the formation of shared representation, Frontiers in Computational Neuroscience, 14, 1, https://www.frontiersin.org/articles/10.3389/fncom.2020.00001/full (2019)
Magrans de Abril, I., & Kanai, R.: A unified strategy for implementing curiosity and empowerment driven reinforcement learning, arXiv:1806.06505 [cs.AI], https://arxiv.org/abs/1806.06505 (2018)
Guttenberg, N., & Kanai, R.: Learning to generate classifiers, arXiv:1803.11373 [cs.LG], https://arxiv.org/abs/1803.11373 (2018)
Hayashi, M., van der Zwaag, W., Bueti, D., & Kanai, R.: Representations of time in human frontoparietal cortex, Communications Biology, 1(1), 233, https://www.nature.com/articles/s42003-018-0243-z (2018)
Magrans de Abril, I., & Kanai, R.: Curiosity-driven reinforcement learning with homeostatic regulation, 2018 International Joint Conference on Neural Networks (IJCNN), 1-6, https://ieeexplore.ieee.org/abstract/document/8489075 (2018)
Amari, S., Karakida, R., & Oizumi, M.: Information geometry connecting Wasserstein distance and Kullback–Leibler divergence via the entropy-relaxed transportation problem, Information Geometry, 1, 13-37, https://link.springer.com/article/10.1007/s41884-018-0002-8 (2018)
Guttenberg, N., Biehl, M., Virgo, N., & Kanai, R.: Being curious about the answers to questions: novelty search with learned attention, Artificial Life Conference Proceedings, 30, 518-525, https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00095 (2018)
“Biehl, M.: Geometry of Friston’s active inference, 1st Symposium on Advances in Approximate Bayesian Inference, 1–5
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Biehl, M., Guckelsberger, C., Salge, C., Smith, S. C., & Polani, D.: Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop, Frontiers in Neurorobotics, 12, 45, https://www.frontiersin.org/articles/10.3389/fnbot.2018.00045/full (2018)
Kitazono, J., Kanai, R., & Oizumi, M. : Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory, Entropy, 20(3), 173, https://www.mdpi.com/1099-4300/20/3/173 (2018)
Mizutani, H., & Kanai, R. : A description length approach to determining the number of k-means clusters, arXiv:1703.00039 [stat.ML], https://arxiv.org/abs/1703.00039 (2017)
Guttenberg, N., Yu, Y., & Kanai, R.: Counterfactual control for free with generative models, arXiv:1702.06676 [cs.LG], https://arxiv.org/abs/1702.06676 (2017)
Guttenberg, N., Biehl, M., & Kanai, R. : Learning body-affordances to simplify action spaces, arXiv:1708.04391 [cs], https://arxiv.org/abs/1708.04391 (2017)
Haun, A. M., Oizumi, M., Kovach, C. K., Kawasaki, H., Oya, H., Howard, M. A., Adolphs, R., & Tsuchiya, N.: Conscious Perception as Integrated Information Patterns in Human Electrocorticography, eNeuro, 4(5), ENEURO.0085-17.2017, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5659238/ (2017)
Magrans de Abril, I., & Kanai, R.: Intrinsically-motivated reinforcement learning for control with continuous actions, 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), 212-214, https://ieeexplore.ieee.org/abstract/document/8279714/ (2017)
Biehl, M., Ikegami, T., & Polani, D.: Specific and Complete Local Integration of Patterns in Bayesian Networks, Entropy, 19(5), 230, https://www.mdpi.com/1099-4300/19/5/230 (2017)
Otten, M., Pinto, Y., Paffen, C.L.E., Seth, A.K., & Kanai, R.: The uniformity illusion: central stimuli can determine peripheral perception, Psychological Science, 28(1), 56–68, https://journals.sagepub.com/doi/10.1177/0956797616672270 (2017)
Guttenberg, N., Virgo, N., Witkowski, O., Aoki, H. & Kanai, R.: Permutation-equivariant neural networks applied to dynamics prediction, arXiv:1612.04530 [cs.CV], https://arxiv.org/abs/1612.04530 (2016)
Guttenberg, N., Biehl, M. & Kanai, R.: Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks, arXiv:1609.00116 [cs.AI], https://arxiv.org/abs/1609.00116 (2016)
Sherman, M.T., Seth, A.K., & Kanai, R.: Predictions shape confidence in right inferior frontal gyrus, Journal of Neuroscience, 36, 10323-10336, https://www.jneurosci.org/content/36/40/10323 (2016)
Kanai, R.: 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, https://dl.acm.org/doi/abs/10.1145/2800835.2815382 (2015)
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