Yu Fu

6.4k total citations · 2 hit papers
84 papers, 4.2k citations indexed

About

Yu Fu is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Yu Fu has authored 84 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Cellular and Molecular Neuroscience, 30 papers in Cognitive Neuroscience and 17 papers in Molecular Biology. Recurrent topics in Yu Fu's work include Neuroscience and Neuropharmacology Research (18 papers), Neural dynamics and brain function (14 papers) and Advanced MRI Techniques and Applications (8 papers). Yu Fu is often cited by papers focused on Neuroscience and Neuropharmacology Research (18 papers), Neural dynamics and brain function (14 papers) and Advanced MRI Techniques and Applications (8 papers). Yu Fu collaborates with scholars based in China, United States and Singapore. Yu Fu's co-authors include Z. Josh Huang, Michael P. Stryker, Jiangteng Lu, Miao He, Sacha B. Nelson, Sang Yong Kim, Yasuyuki Shima, Goichi Miyoshi, Gord Fishell and Raehum Paik and has published in prestigious journals such as Nature, Science and Cell.

In The Last Decade

Yu Fu

76 papers receiving 4.1k citations

Hit Papers

A Resource of Cre Driver Lines for Genetic Targeting of G... 2011 2026 2016 2021 2011 2014 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yu Fu China 28 2.4k 1.7k 1.2k 374 330 84 4.2k
Sabina Berretta United States 37 2.2k 0.9× 982 0.6× 1.3k 1.0× 458 1.2× 304 0.9× 74 4.1k
Xiangmin Xu United States 33 2.3k 1.0× 2.0k 1.2× 1.2k 1.0× 523 1.4× 250 0.8× 123 4.2k
Fumino Fujiyama Japan 35 3.4k 1.4× 1.7k 1.0× 1.4k 1.2× 381 1.0× 545 1.7× 79 4.8k
Hiroyuki Hioki Japan 36 3.0k 1.3× 1.8k 1.0× 1.7k 1.4× 459 1.2× 711 2.2× 93 5.4k
Vivien Chevaleyre France 29 3.1k 1.3× 1.6k 0.9× 1.3k 1.1× 321 0.9× 241 0.7× 44 4.3k
Miwako Yamasaki Japan 36 2.8k 1.2× 1.1k 0.6× 1.8k 1.4× 597 1.6× 305 0.9× 94 4.3k
Ken Sugino United States 28 2.4k 1.0× 1.5k 0.9× 2.0k 1.6× 598 1.6× 415 1.3× 33 4.7k
Ekrem Dere Germany 35 1.5k 0.6× 1.5k 0.9× 1.3k 1.0× 459 1.2× 592 1.8× 83 4.0k
Jun Ding United States 40 4.5k 1.9× 1.9k 1.1× 2.1k 1.7× 386 1.0× 352 1.1× 70 6.8k
Alev Erişir United States 32 2.3k 1.0× 1.3k 0.8× 1.3k 1.1× 327 0.9× 379 1.1× 70 3.6k

Countries citing papers authored by Yu Fu

Since Specialization
Citations

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

Fields of papers citing papers by Yu Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Fu

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Fu. A scholar is included among the top collaborators of Yu Fu 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 Yu Fu. Yu Fu 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.
Lasbleiz, Adèle, Patricia Ancel, Patrice Darmon, et al.. (2025). Structural alterations of individual hypothalamic nuclei in young females with obesity and anorexia nervosa: an in vivo 7-T MRI study. American Journal of Clinical Nutrition. 121(5). 1186–1198.
2.
Wang, Hailiang, Yu Fu, Guodong Xue, et al.. (2025). Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing. Nature Communications. 16(1). 4340–4340. 13 indexed citations
3.
Fu, Yu, et al.. (2024). From aberrant neurodevelopment to neurodegeneration: Insights into the hub gene associated with autism and alzheimer's disease. Brain Research. 1838. 148992–148992. 2 indexed citations
4.
Dong, Yuan, et al.. (2024). Role of GABAB receptors in cognition and EEG activity in aged APP and PS1 transgenic mice. Neurochemistry International. 175. 105718–105718. 1 indexed citations
5.
Fu, Yu, et al.. (2024). Update Review of the Relationship Between Gut Microbiota and Neurodegenerative Diseases. Zenodo (CERN European Organization for Nuclear Research). 4(1). 14–30.
6.
Tint, Mya Thway, Marissa R. Lee, Peter D. Gluckman, et al.. (2023). Functional activity of the caudate mediates the relation between early childhood microstructural variations and elevated metabolic syndrome scores. NeuroImage. 278. 120273–120273. 1 indexed citations
7.
Zheng, Kai, et al.. (2023). Analysis of Risk Factors for White Matter Hyperintensity in Older Adults without Stroke. Brain Sciences. 13(5). 835–835. 6 indexed citations
8.
Yu, Kai, Xian Zhang, Xiong Xiao, et al.. (2023). Plastic and stimulus-specific coding of salient events in the central amygdala. Nature. 616(7957). 510–519. 26 indexed citations
9.
Wang, Kun, Yu Fu, Tianhui Wang, et al.. (2022). State-dependent modulation of thalamocortical oscillations by gamma light flicker with different frequencies, intensities, and duty cycles. Frontiers in Neuroinformatics. 16. 968907–968907. 3 indexed citations
11.
Mohammad, Hasan, Martin Graf, Chun-Yao Lee, et al.. (2021). A neural circuit for excessive feeding driven by environmental context in mice. Nature Neuroscience. 24(8). 1132–1141. 25 indexed citations
12.
Fu, Yu, et al.. (2020). A new mouse tool for studying dopaminergic neurons. Journal of Neuroscience Methods. 347. 108968–108968. 2 indexed citations
13.
Luo, Sarah, Ju Huang, Qin Li, et al.. (2018). Regulation of feeding by somatostatin neurons in the tuberal nucleus. Science. 361(6397). 76–81. 82 indexed citations
14.
Wang, Zhengchun, et al.. (2018). Aging Potentiates Lateral but Not Local Inhibition of Orientation Processing in Primary Visual Cortex. Frontiers in Aging Neuroscience. 10. 14–14. 9 indexed citations
15.
Fu, Yu, Jason Tucciarone, J. Sebastian Espinosa, et al.. (2014). A Cortical Circuit for Gain Control by Behavioral State. Cell. 156(6). 1139–1152. 625 indexed citations breakdown →
16.
Li, Kai, Hongbin Han, Kai Zhu, et al.. (2013). Real-time magnetic resonance imaging visualization and quantitative assessment of diffusion in the cerebral extracellular space of C6 glioma-bearing rats. Neuroscience Letters. 543. 84–89. 28 indexed citations
17.
Fu, Yu, Xiaoyun Wu, Jiangteng Lu, & Z. Josh Huang. (2012). Presynaptic GABAB Receptor Regulates Activity-Dependent Maturation and Patterning of Inhibitory Synapses through Dynamic Allocation of Synaptic Vesicles. Frontiers in Cellular Neuroscience. 6. 57–57. 26 indexed citations
18.
Taniguchi, Hiroki, Miao He, Priscilla Wu, et al.. (2011). A Resource of Cre Driver Lines for Genetic Targeting of GABAergic Neurons in Cerebral Cortex. Neuron. 71(6). 995–1013. 1335 indexed citations breakdown →
19.
Fu, Yu & Z. Josh Huang. (2010). Differential dynamics and activity-dependent regulation of α- and β-neurexins at developing GABAergic synapses. Proceedings of the National Academy of Sciences. 107(52). 22699–22704. 53 indexed citations
20.
Fu, Yu, et al.. (2008). LONG‐TERM EXPOSURE TO EXTREMELY LOW‐FREQUENCY MAGNETIC FIELDS IMPAIRS SPATIAL RECOGNITION MEMORY IN MICE. Clinical and Experimental Pharmacology and Physiology. 35(7). 797–800. 37 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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