Tatsuya Haga

10.6k total citations · 2 hit papers
157 papers, 8.6k citations indexed

About

Tatsuya Haga is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Tatsuya Haga has authored 157 papers receiving a total of 8.6k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Molecular Biology, 80 papers in Cellular and Molecular Neuroscience and 17 papers in Cognitive Neuroscience. Recurrent topics in Tatsuya Haga's work include Receptor Mechanisms and Signaling (88 papers), Neuroscience and Neuropharmacology Research (45 papers) and Ion channel regulation and function (26 papers). Tatsuya Haga is often cited by papers focused on Receptor Mechanisms and Signaling (88 papers), Neuroscience and Neuropharmacology Research (45 papers) and Ion channel regulation and function (26 papers). Tatsuya Haga collaborates with scholars based in Japan, United States and United Kingdom. Tatsuya Haga's co-authors include Kazuko Haga, Kimihiko Kameyama, Arata Ichiyama, Takashi Okuda, Haruhiko Noda, Masanori Kurokawa, Hideo Takahashi, Shosaku Numa, Kenji Kangawa and Hisayuki Matsuo and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Tatsuya Haga

153 papers receiving 8.2k citations

Hit Papers

Cloning, sequencing and e... 1986 2026 1999 2012 1986 2012 250 500 750

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Tatsuya Haga 6.3k 4.4k 689 608 607 157 8.6k
Randy A. Hall 6.1k 1.0× 4.0k 0.9× 1.2k 1.8× 507 0.8× 332 0.5× 133 8.5k
John K. Northup 5.4k 0.9× 2.0k 0.5× 1.3k 2.0× 484 0.8× 279 0.5× 84 7.1k
R. A. John Challiss 5.9k 0.9× 3.5k 0.8× 1.2k 1.7× 357 0.6× 371 0.6× 252 8.8k
Mark R. Brann 7.8k 1.2× 6.4k 1.5× 401 0.6× 216 0.4× 207 0.3× 112 10.3k
Stefan R. Nahorski 8.8k 1.4× 5.6k 1.3× 1.5k 2.2× 574 0.9× 442 0.7× 277 11.9k
Naoaki Saito 5.7k 0.9× 3.0k 0.7× 1.4k 2.0× 350 0.6× 564 0.9× 245 9.1k
Laurent Prézeau 5.1k 0.8× 5.0k 1.1× 298 0.4× 838 1.4× 289 0.5× 97 6.9k
Joachim W. Deitmer 5.1k 0.8× 3.9k 0.9× 429 0.6× 204 0.3× 402 0.7× 208 8.6k
Neville N. Osborne 7.9k 1.2× 4.8k 1.1× 642 0.9× 390 0.6× 175 0.3× 315 13.1k
Catherine D. Strader 9.8k 1.6× 6.7k 1.5× 487 0.7× 1.0k 1.7× 229 0.4× 144 14.1k

Countries citing papers authored by Tatsuya Haga

Since Specialization
Citations

This map shows the geographic impact of Tatsuya Haga'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 Tatsuya Haga with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tatsuya Haga more than expected).

Fields of papers citing papers by Tatsuya Haga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tatsuya Haga. 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 Tatsuya Haga. The network helps show where Tatsuya Haga may publish in the future.

Co-authorship network of co-authors of Tatsuya Haga

This figure shows the co-authorship network connecting the top 25 collaborators of Tatsuya Haga. A scholar is included among the top collaborators of Tatsuya Haga 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 Tatsuya Haga. Tatsuya Haga is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Haga, Tatsuya, Noriaki Ohkawa, Chi Chung Alan Fung, et al.. (2025). Parallel processing of past and future memories through reactivation and synaptic plasticity mechanisms during sleep. Nature Communications. 16(1). 3618–3618. 1 indexed citations
2.
Haga, Tatsuya, Yohei Oseki, & Tomoki Fukai. (2025). A unified neural representation model for spatial and conceptual computations. Proceedings of the National Academy of Sciences. 122(11). e2413449122–e2413449122.
3.
Haga, Tatsuya & Tomoki Fukai. (2021). Multiscale representations of community structures in attractor neural networks. PLoS Computational Biology. 17(8). e1009296–e1009296. 5 indexed citations
4.
Fukai, Tomoki, et al.. (2021). Neural mechanisms for learning hierarchical structures of information. Current Opinion in Neurobiology. 70. 145–153. 3 indexed citations
5.
Haga, Tatsuya, et al.. (2019). Unsupervised Detection of Cell-Assembly Sequences by Similarity-Based Clustering. Frontiers in Neuroinformatics. 13. 39–39. 6 indexed citations
6.
Haga, Tatsuya, et al.. (2018). Neuronal stability in medial frontal cortex sets individual variability in decision-making. Nature Neuroscience. 21(12). 1764–1773. 17 indexed citations
7.
Haga, Tatsuya & Tomoki Fukai. (2018). Recurrent network model for learning goal-directed sequences through reverse replay. eLife. 7. 20 indexed citations
8.
Haga, Tatsuya & Tomoki Fukai. (2018). Dendritic processing of spontaneous neuronal sequences for single-trial learning. Scientific Reports. 8(1). 15166–15166. 11 indexed citations
9.
Shiroishi, Mitsunori, Hirokazu Tsujimoto, Hisayoshi Makyio, et al.. (2012). Platform for the rapid construction and evaluation of GPCRs for crystallography in Saccharomyces cerevisiae. Microbial Cell Factories. 11(1). 78–78. 39 indexed citations
10.
Sakai, Miyo, Yutaka Sadakane, Tatsuya Haga, et al.. (2008). Differential rate constants of racemization of aspartyl and asparaginyl residues in human alpha A-crystallin mutants. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1784(9). 1192–1199. 14 indexed citations
11.
Sugiura, Hiroko, Masato Matsuoka, Hiroshi Hayashi, et al.. (2004). Inhibitory Role of Endophilin 3 in Receptor-mediated Endocytosis. Journal of Biological Chemistry. 279(22). 23343–23348. 21 indexed citations
12.
Takeda, Shigeki, et al.. (2003). Identification of surrogate ligands for orphan G protein-coupled receptors. Life Sciences. 74(2-3). 367–377. 50 indexed citations
13.
Tsuga, Hirofumi, et al.. (2002). Effects of Toluene Exposure on Signal Transduction: Toluene Reduced the Signaling via Stimulation of Human Muscarinic Acetylcholine Receptor m2 Subtypes in CHO Cells. The Japanese Journal of Pharmacology. 89(3). 282–289. 7 indexed citations
14.
Misawa, Hidemi, et al.. (2001). Distribution of the high-affinity choline transporter in the central nervous system of the rat. Neuroscience. 105(1). 87–98. 85 indexed citations
15.
Haga, Tatsuya, Kazuko Haga, & Kimihiko Kameyama. (1994). G Protein‐Coupled Receptor Kinases. Journal of Neurochemistry. 63(2). 400–412. 92 indexed citations
16.
Ohara, Koichi, Koichi Ohara, Kenshiro Ohara, et al.. (1990). Interaction of deglycosylate muscarinic receptors with ligands and G proteins. European Journal of Pharmacology Molecular Pharmacology. 189(6). 341–346. 14 indexed citations
17.
Haga, Kazuko, et al.. (1990). Interaction of atrial muscarinic receptors with three kinds of GTP-binding proteins. Journal of Molecular and Cellular Cardiology. 22(3). 343–351. 23 indexed citations
18.
Ohara, Koichi, Kazuko Haga, Gabriel Berstein, et al.. (1988). The interaction between D-2 dopamine receptors and GTP-binding proteins.. Molecular Pharmacology. 33(3). 290–296. 53 indexed citations
19.
Haga, Tatsuya, et al.. (1988). Biochemical Studies on the Muscarinic Acetylcholine Receptor. Advances in experimental medicine and biology. 236. 239–254.

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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