Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015 | 285 | 2015 |

Distributional Smoothing by Virtual Adversarial Examples. T Miyato, S Maeda, M Koyama, K Nakae, S Ishii ICLR (Poster), 2016 | 56 | 2016 |

Deep learning of fMRI big data: a novel approach to subject-transfer decoding S Koyamada, Y Shikauchi, K Nakae, M Koyama, S Ishii arXiv preprint arXiv:1502.00093, 2015 | 45 | 2015 |

Bayesian estimation of phase response curves K Nakae, Y Iba, Y Tsubo, T Fukai, T Aoyagi Neural networks 23 (6), 752-763, 2010 | 14 | 2010 |

Semi-supervised deep learning of brain tissue segmentation R Ito, K Nakae, J Hata, H Okano, S Ishii Neural Networks 116, 25-34, 2019 | 11 | 2019 |

Uncertainty-dependent extinction of fear memory in an amygdala-mPFC neural circuit model Y Li, K Nakae, S Ishii, H Naoki PLoS computational biology 12 (9), e1005099, 2016 | 10 | 2016 |

A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging K Nakae, Y Ikegaya, T Ishikawa, S Oba, H Urakubo, M Koyama, S Ishii PLoS Comput Biol 10 (11), e1003949, 2014 | 9 | 2014 |

Pat—probabilistic axon tracking for densely labeled neurons in large 3-d micrographs H Skibbe, M Reisert, K Nakae, A Watakabe, J Hata, H Mizukami, H Okano, ... IEEE transactions on medical imaging 38 (1), 69-78, 2018 | 6 | 2018 |

Principal sensitivity analysis S Koyamada, M Koyama, K Nakae, S Ishii Pacific-Asia Conference on Knowledge Discovery and Data Mining, 621-632, 2015 | 5 | 2015 |

MarmoNet: a pipeline for automated projection mapping of the common marmoset brain from whole-brain serial two-photon tomography H Skibbe, A Watakabe, K Nakae, CE Gutierrez, H Tsukada, J Hata, ... arXiv preprint arXiv:1908.00876, 2019 | 3 | 2019 |

Zero-shot fMRI decoding with three-dimensional registration based on diffusion tensor imaging T Fuchigami, Y Shikauchi, K Nakae, M Shikauchi, T Ogawa, S Ishii Scientific reports 8 (1), 1-11, 2018 | 2 | 2018 |

Empirical Bayesian significance measure of neuronal spike response S Oba, K Nakae, Y Ikegaya, S Aki, J Yoshimoto, S Ishii BMC neuroscience 17 (1), 27, 2016 | 2 | 2016 |

Construction of subject-independent brain decoders for human FMRI with deep learning S Koyamada, Y Shikauchi, K Nakae, S Ishii Proc. Int. Conf. Data Mining, Internet Comput., Big Data (BigData), 60-68, 2014 | 2 | 2014 |

The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain A Woodward, R Gong, H Abe, K Nakae, J Hata, H Skibbe, Y Yamaguchi, ... Brain Structure and Function, 1-19, 2020 | 1 | 2020 |

Statistical estimation of phase response curves using data transformation K Nakae Journal of the Physical Society of Japan 88 (8), 084003, 2019 | 1 | 2019 |

Model-based prediction of spatial gene expression via generative linear mapping Y Okochi, S Sakaguchi, K Nakae, T Kondo, H Naoki bioRxiv, 2020 | | 2020 |

Multi-objective Parameter Optimization of DWI-based Global Fiber Tracking with Neuronal Tracer Signal as a Reference CE Gutierrez, H Skibbe, K Nakae, H Tsukada, J Lienard, A Watakabe, ... arXiv preprint arXiv:1911.13215, 2019 | | 2019 |

System level analysis of motor-related neural activities in larval *Drosophila*Y Yoon, J Park, A Taniguchi, H Kohsaka, K Nakae, S Nonaka, S Ishii, ... Journal of neurogenetics 33 (3), 179-189, 2019 | | 2019 |

The NanoZoomer Connectomics Pipeline for Tracer Injection Studies of the Marmoset Brain A Woodward, R Gong, H Abe, K Nakae, J Hata, H Skibbe, Y Yamaguchi, ... bioRxiv, 748376, 2019 | | 2019 |

Analysis of Structure-Function Relationship Using a Whole-Brain Dynamic Model Based on MRI Images of the Common Marmoset H Tsukada, H Hamada, K Nakae, S Ishii, J Hata, H Okano, K Doya Advances in Cognitive Neurodynamics (VI), 97-102, 2018 | | 2018 |