Teppei Ebina

641 total citations
18 papers, 438 citations indexed

About

Teppei Ebina is a scholar working on Molecular Biology, Cognitive Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, Teppei Ebina has authored 18 papers receiving a total of 438 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 9 papers in Cognitive Neuroscience and 8 papers in Cellular and Molecular Neuroscience. Recurrent topics in Teppei Ebina's work include Neural dynamics and brain function (7 papers), Neuroscience and Neuropharmacology Research (7 papers) and Machine Learning in Bioinformatics (6 papers). Teppei Ebina is often cited by papers focused on Neural dynamics and brain function (7 papers), Neuroscience and Neuropharmacology Research (7 papers) and Machine Learning in Bioinformatics (6 papers). Teppei Ebina collaborates with scholars based in Japan and Iran. Teppei Ebina's co-authors include Yutaka Kuroda, Hiroyuki Toh, M Matsuzaki, Tadaharu Tsumoto, Yuchio Yanagawa, Kazuhiro Sohya, Kosuke Maki, Kunitsugu Soda, Kunihiro Kuwajima and Atsushi Kato and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Journal of Neuroscience.

In The Last Decade

Teppei Ebina

18 papers receiving 436 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Teppei Ebina Japan 10 212 156 150 43 32 18 438
Stephan Junek Germany 11 170 0.8× 259 1.7× 84 0.6× 114 2.7× 18 0.6× 16 525
Ben Johnson United States 8 247 1.2× 151 1.0× 51 0.3× 26 0.6× 19 0.6× 10 393
Linlin Z. Fan United States 11 234 1.1× 412 2.6× 167 1.1× 41 1.0× 10 0.3× 13 575
Michael D. Menz United States 7 134 0.6× 175 1.1× 115 0.8× 40 0.9× 59 1.8× 7 422
Yoav Adam United States 12 165 0.8× 218 1.4× 93 0.6× 18 0.4× 9 0.3× 14 423
Zoltán Raics Switzerland 5 164 0.8× 232 1.5× 161 1.1× 15 0.3× 10 0.3× 5 365
Reiko Nakatomi Japan 8 283 1.3× 99 0.6× 43 0.3× 15 0.3× 18 0.6× 11 434
Marcel Beining Germany 8 71 0.3× 168 1.1× 137 0.9× 11 0.3× 20 0.6× 9 335
Francesca Macchi Italy 18 252 1.2× 192 1.2× 75 0.5× 30 0.7× 16 0.5× 29 782
David Liao United States 9 123 0.6× 170 1.1× 119 0.8× 8 0.2× 16 0.5× 16 311

Countries citing papers authored by Teppei Ebina

Since Specialization
Citations

This map shows the geographic impact of Teppei Ebina's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Teppei Ebina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Teppei Ebina more than expected).

Fields of papers citing papers by Teppei Ebina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Teppei Ebina. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Teppei Ebina. The network helps show where Teppei Ebina may publish in the future.

Co-authorship network of co-authors of Teppei Ebina

This figure shows the co-authorship network connecting the top 25 collaborators of Teppei Ebina. A scholar is included among the top collaborators of Teppei Ebina based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Teppei Ebina. Teppei Ebina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Terada, Shin-Ichiro, Teppei Ebina, Masato Uemura, et al.. (2024). ARViS: a bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset cerebral cortex. Nature Communications. 15(1). 7633–7633. 1 indexed citations
2.
Ebina, Teppei, Rieko Setsuie, Yoshito Masamizu, et al.. (2024). Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning. Nature Communications. 15(1). 7127–7127. 1 indexed citations
3.
Ebina, Teppei, Shin-Ichiro Terada, Takanori Uka, et al.. (2023). Change detection in the primate auditory cortex through feedback of prediction error signals. Nature Communications. 14(1). 6981–6981. 7 indexed citations
4.
Okamoto, Kazuki, Teppei Ebina, Kuniaki Konishi, et al.. (2021). Tb 3+ -doped fluorescent glass for biology. Science Advances. 7(2). 13 indexed citations
5.
Matsuzaki, M & Teppei Ebina. (2020). Common marmoset as a model primate for study of the motor control system. Current Opinion in Neurobiology. 64. 103–110. 8 indexed citations
6.
Ebina, Teppei, et al.. (2020). Structural dynamics and stability of corticocortical and thalamocortical axon terminals during motor learning. PLoS ONE. 15(6). e0234930–e0234930. 20 indexed citations
7.
Ebina, Teppei, Akiya Watakabe, Yoshito Masamizu, et al.. (2019). Arm movements induced by noninvasive optogenetic stimulation of the motor cortex in the common marmoset. Proceedings of the National Academy of Sciences. 116(45). 22844–22850. 33 indexed citations
8.
Ebina, Teppei, Yoshito Masamizu, Yasuhiro Tanaka, et al.. (2018). Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks. Nature Communications. 9(1). 1879–1879. 60 indexed citations
9.
Tambi, Richa, et al.. (2016). Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers. Journal of Computer-Aided Molecular Design. 31(2). 237–244. 2 indexed citations
10.
Ebina, Teppei, Kazuhiro Sohya, Itaru Imayoshi, et al.. (2014). 3D Clustering of GABAergic Neurons Enhances Inhibitory Actions on Excitatory Neurons in the Mouse Visual Cortex. Cell Reports. 9(5). 1896–1907. 16 indexed citations
11.
Ebina, Teppei, et al.. (2014). H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection. Journal of Computer-Aided Molecular Design. 28(8). 831–839. 5 indexed citations
12.
Kimura, Rie, Mir-Shahram Safari, Javad Mirnajafi‐Zadeh, et al.. (2014). Curtailing Effect of Awakening on Visual Responses of Cortical Neurons by Cholinergic Activation of Inhibitory Circuits. Journal of Neuroscience. 34(30). 10122–10133. 21 indexed citations
13.
Ebina, Teppei, et al.. (2013). IS-Dom: a dataset of independent structural domains automatically delineated from protein structures. Journal of Computer-Aided Molecular Design. 27(5). 419–426. 3 indexed citations
14.
Kameyama, Katsuro, Kazuhiro Sohya, Teppei Ebina, et al.. (2010). Difference in Binocularity and Ocular Dominance Plasticity between GABAergic and Excitatory Cortical Neurons. Journal of Neuroscience. 30(4). 1551–1559. 74 indexed citations
15.
Ebina, Teppei, Hiroyuki Toh, & Yutaka Kuroda. (2010). DROP: an SVM domain linker predictor trained with optimal features selected by random forest. Bioinformatics. 27(4). 487–494. 54 indexed citations
16.
Ebina, Teppei, Hiroyuki Toh, & Yutaka Kuroda. (2008). Loop‐length‐dependent SVM prediction of domain linkers for high‐throughput structural proteomics. Biopolymers. 92(1). 1–8. 38 indexed citations
17.
Ebina, Teppei, Hidehiro Toh, & Yutaka Kuroda. (2008). Loop Length Dependent SVM Prediction of Domain Linkers. Journal of Proteomics & Bioinformatics. S2(1). 167–168. 1 indexed citations
18.
Kato, Atsushi, Kosuke Maki, Teppei Ebina, et al.. (2006). Mutational analysis of protein solubility enhancement using short peptide tags. Biopolymers. 85(1). 12–18. 81 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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