Kenichi Ohki
Affiliations: | Kyushu University, Fukuoka-shi, Fukuoka-ken, Japan |
Website:
http://www.med.kyushu-u.ac.jp/physiol2/Google:
"Kenichi Ohki"Mean distance: 13.14 (cluster 6) | S | N | B | C | P |
Parents
Sign in to add mentor| Yasushi Miyashita | post-doc | University of Tokyo | |
| R Clay Reid | post-doc | Harvard Medical School |
Children
Sign in to add trainee| Kenta M. Hagihara | research assistant | 2011-2014 | Kyushu University |
| Takashi Kawashima | grad student | 2011-2013 | Kyushu University |
| Takashi Yoshida | post-doc | 2011- | Kyushu University |
| Gen Ohtsuki | post-doc | 2011-2015 |
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Publications
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| Matsui T, Hashimoto T, Murakami T, et al. (2024) Orthogonalization of spontaneous and stimulus-driven activity by hierarchical neocortical areal network in primates. Nature Communications. 15: 10055 |
| Frey T, Murakami T, Maki K, et al. (2023) Age-associated reduction of nuclear shape dynamics in excitatory neurons of the visual cortex. Aging Cell. e13925 |
| Murakami T, Ohki K. (2023) Thalamocortical circuits for the formation of hierarchical pathways in the mammalian visual cortex. Frontiers in Neural Circuits. 17: 1155195 |
| Tezuka Y, Hagihara KM, Ohki K, et al. (2022) Developmental stage-specific spontaneous activity contributes to callosal axon projections. Elife. 11 |
| Murakami T, Matsui T, Uemura M, et al. (2022) Modular strategy for development of the hierarchical visual network in mice. Nature. 608: 578-585 |
| Kondo S, Kiyohara Y, Ohki K. (2022) Response Selectivity of the Lateral Posterior Nucleus Axons Projecting to the Mouse Primary Visual Cortex. Frontiers in Neural Circuits. 16: 825735 |
| Chatterjee S, Ohki K, Reid RC. (2021) Chromatic micromaps in primary visual cortex. Nature Communications. 12: 2315 |
| Hagihara KM, Ishikawa AW, Yoshimura Y, et al. (2020) Long-Range Interhemispheric Projection Neurons Show Biased Response Properties and Fine-Scale Local Subnetworks in Mouse Visual Cortex. Cerebral Cortex (New York, N.Y. : 1991) |
| Yoshida T, Ohki K. (2020) Natural images are reliably represented by sparse and variable populations of neurons in visual cortex. Nature Communications. 11: 872 |
| Ukita J, Yoshida T, Ohki K. (2019) Characterisation of nonlinear receptive fields of visual neurons by convolutional neural network. Scientific Reports. 9: 3791 |