Michael Winding

1.3k total citations · 1 hit paper
17 papers, 657 citations indexed

About

Michael Winding is a scholar working on Cell Biology, Molecular Biology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Michael Winding has authored 17 papers receiving a total of 657 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Cell Biology, 7 papers in Molecular Biology and 7 papers in Cellular and Molecular Neuroscience. Recurrent topics in Michael Winding's work include Microtubule and mitosis dynamics (8 papers), Neurobiology and Insect Physiology Research (7 papers) and Insect and Arachnid Ecology and Behavior (5 papers). Michael Winding is often cited by papers focused on Microtubule and mitosis dynamics (8 papers), Neurobiology and Insect Physiology Research (7 papers) and Insect and Arachnid Ecology and Behavior (5 papers). Michael Winding collaborates with scholars based in United States, United Kingdom and Canada. Michael Winding's co-authors include Vladimir I. Gelfand, Wen Lü, Urko del Castillo, Jill Wildonger, Margot Lakonishok, Marta Zlatic, Albert Cardona, Akira Fushiki, Richard D. Fetter and Javier Valdés-Alemán and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Michael Winding

17 papers receiving 651 citations

Hit Papers

The connectome of an insect brain 2023 2026 2024 2025 2023 50 100 150

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Winding United States 11 289 252 244 112 83 17 657
Reo Maeda Japan 13 197 0.7× 98 0.4× 423 1.7× 83 0.7× 102 1.2× 19 737
Orkun Akin United States 10 328 1.1× 292 1.2× 231 0.9× 49 0.4× 38 0.5× 13 633
Guangwei Si China 12 105 0.4× 307 1.2× 249 1.0× 176 1.6× 44 0.5× 14 751
Lea Goentoro United States 11 148 0.5× 147 0.6× 822 3.4× 147 1.3× 111 1.3× 17 1.2k
Joseph L. Dynes United States 14 296 1.0× 287 1.1× 601 2.5× 71 0.6× 39 0.5× 17 1.1k
Honda Naoki Japan 16 274 0.9× 237 0.9× 468 1.9× 32 0.3× 48 0.6× 40 885
Tom Kazimiers United States 5 109 0.4× 334 1.3× 120 0.5× 147 1.3× 97 1.2× 6 556
Amir Fayyazuddin United States 8 123 0.4× 570 2.3× 549 2.3× 117 1.0× 88 1.1× 8 947
Stanislav Nagy United States 18 131 0.5× 264 1.0× 242 1.0× 63 0.6× 50 0.6× 26 772
Jan Felix Evers United Kingdom 17 114 0.4× 617 2.4× 284 1.2× 173 1.5× 116 1.4× 21 947

Countries citing papers authored by Michael Winding

Since Specialization
Citations

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

Fields of papers citing papers by Michael Winding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Winding

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

All Works

17 of 17 papers shown
1.
Winding, Michael, Benjamin D. Pedigo, Christopher L. Barnes, et al.. (2023). The connectome of an insect brain. Science. 379(6636). eadd9330–eadd9330. 155 indexed citations breakdown →
2.
Pedigo, Benjamin D., Michael Powell, Eric Bridgeford, et al.. (2023). Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome. eLife. 12. 2 indexed citations
3.
Pedigo, Benjamin D., Michael Winding, Carey E. Priebe, & Joshua T Vogelstein. (2022). Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes. Network Neuroscience. 7(2). 522–538. 2 indexed citations
4.
Giachello, Carlo NG, Sebastian Cachero, Michael Winding, et al.. (2022). Electrophysiological Validation of Monosynaptic Connectivity between Premotor Interneurons and the aCC Motoneuron in the Drosophila Larval CNS. Journal of Neuroscience. 42(35). 6724–6738. 4 indexed citations
5.
Clayton, Michael S., et al.. (2022). High-throughput automated methods for classical and operant conditioning of Drosophila larvae. eLife. 11. 7 indexed citations
6.
Basu, Amitabh, Avanti Athreya, Youngser Park, et al.. (2022). Distance-based positive and unlabeled learning for ranking. Pattern Recognition. 134. 109085–109085. 3 indexed citations
7.
Eschbach, Claire, Akira Fushiki, Michael Winding, et al.. (2020). Recurrent architecture for adaptive regulation of learning in the insect brain. Nature Neuroscience. 23(4). 544–555. 89 indexed citations
8.
Eschbach, Claire, Akira Fushiki, Michael Winding, et al.. (2020). Circuits for integrating learned and innate valences in the insect brain.. Apollo (University of Cambridge). 31 indexed citations
9.
Jovanic, Tihana, Michael Winding, Albert Cardona, et al.. (2019). Neural Substrates of Drosophila Larval Anemotaxis. Current Biology. 29(4). 554–566.e4. 26 indexed citations
10.
Engelke, Martin F., Michael Winding, Yang Yue, et al.. (2016). Engineered kinesin motor proteins amenable to small-molecule inhibition. Nature Communications. 7(1). 26 indexed citations
11.
Jolly, Amber L., Chi‐Hao Luan, Sara F. Dunne, et al.. (2016). A Genome-wide RNAi Screen for Microtubule Bundle Formation and Lysosome Motility Regulation in Drosophila S2 Cells. Cell Reports. 14(3). 611–620. 5 indexed citations
12.
Lü, Wen, Michael Winding, Margot Lakonishok, Jill Wildonger, & Vladimir I. Gelfand. (2016). Microtubule–microtubule sliding by kinesin-1 is essential for normal cytoplasmic streaming in Drosophila oocytes. Proceedings of the National Academy of Sciences. 113(34). E4995–5004. 63 indexed citations
13.
Winding, Michael, Michael T. Kelliher, Wen Lü, Jill Wildonger, & Vladimir I. Gelfand. (2016). Role of kinesin-1–based microtubule sliding in Drosophila nervous system development. Proceedings of the National Academy of Sciences. 113(34). E4985–94. 55 indexed citations
14.
Castillo, Urko del, Michael Winding, Wen Lü, & Vladimir I. Gelfand. (2015). Interplay between kinesin-1 and cortical dynein during axonal outgrowth and microtubule organization in Drosophila neurons. eLife. 4. e10140–e10140. 72 indexed citations
15.
Castillo, Urko del, Wen Lü, Michael Winding, Margot Lakonishok, & Vladimir I. Gelfand. (2014). Pavarotti/MKLP1 Regulates Microtubule Sliding and Neurite Outgrowth in Drosophila Neurons. Current Biology. 25(2). 200–205. 46 indexed citations
16.
Bader, Jason R., Patricia S. Vaughan, Sinji Borges Ferreira Tauhata, et al.. (2011). Zwint-1 is a novel Aurora B substrate required for the assembly of a dynein-binding platform on kinetochores. Molecular Biology of the Cell. 22(18). 3318–3330. 43 indexed citations
17.
Bader, Jason R., et al.. (2011). Polo-like Kinase1 Is Required for Recruitment of Dynein to Kinetochores during Mitosis. Journal of Biological Chemistry. 286(23). 20769–20777. 28 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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