Ken Nakae

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Shin IshiiKyoto UniversityVerified email at i.kyoto-u.ac.jp
Hideyuki OkanoKeio UniversityVerified email at keio.jp
Henrik SkibbeRIKEN, Center for Brain Science, Brain Image Analysis Unit, JapanVerified email at sys.i.kyoto-u.ac.jp
Masanori KoyamaPreferred NetworksVerified email at preferred.jp
Alexander WoodwardRIKEN Center for Brain ScienceVerified email at sacral.c.u-tokyo.ac.jp
Akiya WatakabeRIKEN CBSVerified email at riken.jp
Takeru MiyatoUniversity of Tübingen, Preferred Networks, Inc.Verified email at uni-tuebingen.de
Kenji DoyaOkinawa Institute of Science and TechnologyVerified email at oist.jp
Carlos GutierrezOkinawa Institute of Science and TechnologyVerified email at oist.jp
Rui GongUniversity of AucklandVerified email at aucklanduni.ac.nz
Shin-ichi MaedaPreferred Networks, Inc.Verified email at preferred.jp
Marco ReisertMedical Physics and Stereotactic/Functional Neurosurgery, University Medical Center FreiburgVerified email at uniklinik-freiburg.de
Sotetsu KOYAMADAATRVerified email at atr.jp
Muhammad Febrian RachmadiResearch Scientist and LecturerVerified email at cs.ui.ac.id
Shigeyuki ObaHR science institute, Miidas co., Japan.Verified email at miidas.jp
Honda NaokiHiroshima UniversityVerified email at hiroshima-u.ac.jp
Yuji IkegayaGraduate School of Pharmaceutical Sciences, The University of TokyoVerified email at ikegaya.jp
Piotr MajkaNencki Institute of Experimental BiologyVerified email at nencki.gov.pl
Marcello RosaDepartment of Physiology and Biomedicine Discovery Institute, Monash UniversityVerified email at monash.edu
Charissa PoonPostdoctoral Fellow at RIKEN CBS, Brain Image Analysis UnitVerified email at riken.jp

Ken Nakae

Exploratory Research Center on Life and Living Systems, National Institutes of Natural Science

Verified email at nips.ac.jp

Neuroscience Statistics Machine Learning Computational Neuroscience


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Distributional smoothing with virtual adversarial training T Miyato, S Maeda, M Koyama, K Nakae, S Ishii arXiv preprint arXiv:1507.00677, 2015	552	2015
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	80	2015
Semi-supervised deep learning of brain tissue segmentation R Ito, K Nakae, J Hata, H Okano, S Ishii Neural Networks 116, 25-34, 2019	65	2019
Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference CE Gutierrez, H Skibbe, K Nakae, H Tsukada, J Lienard, A Watakabe, ... Scientific reports 10 (1), 21285, 2020	19	2020
Bayesian estimation of phase response curves K Nakae, Y Iba, Y Tsubo, T Fukai, T Aoyagi Neural Networks 23 (6), 752-763, 2010	19	2010
Local and long-distance organization of prefrontal cortex circuits in the marmoset brain A Watakabe, H Skibbe, K Nakae, H Abe, N Ichinohe, MF Rachmadi, ... Neuron 111 (14), 2258-2273. e10, 2023	17	2023
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	17	2018
Model-based prediction of spatial gene expression via generative linear mapping Y Okochi, S Sakaguchi, K Nakae, T Kondo, H Naoki Nature Communications 12 (1), 3731, 2021	16	2021
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	15	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 computational biology 10 (11), e1003949, 2014	15	2014
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	12	2019
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 225, 1225-1243, 2020	10	2020
Principal sensitivity analysis S Koyamada, M Koyama, K Nakae, S Ishii Pacific-Asia Conference on Knowledge Discovery and Data Mining, 621-632, 2015	10	2015
Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range J Hata, K Nakae, H Tsukada, A Woodward, Y Haga, M Iida, A Uematsu, ... Scientific Data 10 (1), 221, 2023	8	2023
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), 12342, 2018	8	2018
The Brain/MINDS Marmoset Connectivity Resource: An open-access platform for cellular-level tracing and tractography in the primate brain H Skibbe, MF Rachmadi, K Nakae, CE Gutierrez, J Hata, H Tsukada, ... PLoS biology 21 (6), e3002158, 2023	7	2023
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	6	2019
The Brain/MINDS Marmoset Connectivity Atlas: exploring bidirectional tracing and tractography in the same stereotaxic space H Skibbe, MF Rachmadi, K Nakae, CE Gutierrez, J Hata, H Tsukada, ... bioRxiv, 2022.06. 06.494999, 2022	4	2022
Empirical Bayesian significance measure of neuronal spike response S Oba, K Nakae, Y Ikegaya, S Aki, J Yoshimoto, S Ishii BMC neuroscience 17, 1-21, 2016	4	2016
Connectional architecture of the prefrontal cortex in the marmoset brain A Watakabe, H Skibbe, K Nakae, H Abe, N Ichinohe, J Wang, M Takaji, ... bioRxiv, 2021, 2021	3	2021

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