Gordon J Berman

2.6k total citations · 2 hit papers
21 papers, 1.4k citations indexed

About

Gordon J Berman is a scholar working on Ecology, Evolution, Behavior and Systematics, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Gordon J Berman has authored 21 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Ecology, Evolution, Behavior and Systematics, 8 papers in Cellular and Molecular Neuroscience and 4 papers in Cognitive Neuroscience. Recurrent topics in Gordon J Berman's work include Neurobiology and Insect Physiology Research (8 papers), Animal Behavior and Reproduction (7 papers) and Insect and Arachnid Ecology and Behavior (3 papers). Gordon J Berman is often cited by papers focused on Neurobiology and Insect Physiology Research (8 papers), Animal Behavior and Reproduction (7 papers) and Insect and Arachnid Ecology and Behavior (3 papers). Gordon J Berman collaborates with scholars based in United States, Japan and France. Gordon J Berman's co-authors include Z. Jane Wang, Joshua W. Shaevitz, William Bialek, Daniel M. Choi, Itai Cohen, Leif Ristroph, Attila Bergou, Jessica Cande, David L. Stern and Gunnar Ristroph and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Neuron and NeuroImage.

In The Last Decade

Gordon J Berman

21 papers receiving 1.4k citations

Hit Papers

Energy-minimizing kinematics in hovering insect flight 2007 2026 2013 2019 2007 2014 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gordon J Berman United States 13 536 400 359 230 203 21 1.4k
York Winter Germany 31 818 1.5× 344 0.9× 1.1k 3.1× 174 0.8× 235 1.2× 88 2.9k
Malcolm A. MacIver United States 29 927 1.7× 335 0.8× 192 0.5× 66 0.3× 283 1.4× 63 3.3k
Steven N. Fry Switzerland 14 776 1.4× 558 1.4× 451 1.3× 324 1.4× 221 1.1× 26 1.4k
Matthew J. McHenry United States 27 680 1.3× 151 0.4× 330 0.9× 95 0.4× 113 0.6× 66 2.3k
Douglas L. Altshuler United States 32 1.2k 2.2× 168 0.4× 1.5k 4.2× 480 2.1× 302 1.5× 82 3.0k
Andrew Straw United States 27 458 0.9× 1.5k 3.7× 1.1k 2.9× 774 3.4× 49 0.2× 66 3.0k
Richard A. Satterlie United States 25 209 0.4× 851 2.1× 379 1.1× 113 0.5× 28 0.1× 79 1.9k
Stefan Schuster Germany 21 98 0.2× 263 0.7× 268 0.7× 86 0.4× 32 0.2× 64 1.2k
Uwe Koch Germany 21 133 0.2× 478 1.2× 487 1.4× 307 1.3× 72 0.4× 68 1.4k

Countries citing papers authored by Gordon J Berman

Since Specialization
Citations

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

Fields of papers citing papers by Gordon J Berman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gordon J Berman. 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 Gordon J Berman. The network helps show where Gordon J Berman may publish in the future.

Co-authorship network of co-authors of Gordon J Berman

This figure shows the co-authorship network connecting the top 25 collaborators of Gordon J Berman. A scholar is included among the top collaborators of Gordon J Berman 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 Gordon J Berman. Gordon J Berman 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.
Berman, Gordon J, et al.. (2024). Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences. Scientific Reports. 14(1). 19730–19730. 4 indexed citations
2.
Walum, Hasse, et al.. (2024). Caregiver greeting to infants under 6 months already reflects emerging differences in those later diagnosed with autism. Proceedings of the Royal Society B Biological Sciences. 291(2024). 20232494–20232494. 2 indexed citations
3.
Kesar, Trisha M., et al.. (2023). Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics. PLoS Computational Biology. 19(10). e1011556–e1011556. 12 indexed citations
4.
Nande, Anjalika, et al.. (2022). Bottlenecks, Modularity, and the Neural Control of Behavior. Frontiers in Behavioral Neuroscience. 16. 835753–835753. 2 indexed citations
5.
Boender, Arjen J., Jidong Guo, Hong Zhu, et al.. (2022). Social experience alters oxytocinergic modulation in the nucleus accumbens of female prairie voles. Current Biology. 32(5). 1026–1037.e4. 27 indexed citations
6.
Choi, Daniel M., et al.. (2022). Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster. PLoS Computational Biology. 18(2). e1009867–e1009867. 12 indexed citations
7.
Cande, Jessica, et al.. (2021). A framework for studying behavioral evolution by reconstructing ancestral repertoires. eLife. 10. 25 indexed citations
8.
Macke, Jakob H., et al.. (2020). Long timescale dynamics in freely behaving rats. Bulletin of the American Physical Society. 1 indexed citations
9.
Marshall, Jesse D., Diego Aldarondo, Timothy Dunn, et al.. (2020). Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire. Neuron. 109(3). 420–437.e8. 74 indexed citations
10.
Ding, Yun, Joshua L. Lillvis, Jessica Cande, et al.. (2019). Neural Evolution of Context-Dependent Fly Song. Current Biology. 29(7). 1089–1099.e7. 64 indexed citations
11.
Jacob, Amanda, et al.. (2019). Dopamine Depletion Affects Vocal Acoustics and Disrupts Sensorimotor Adaptation in Songbirds. eNeuro. 6(3). ENEURO.0190–19.2019. 12 indexed citations
12.
Cande, Jessica, Shigehiro Namiki, Wyatt Korff, et al.. (2018). Optogenetic dissection of descending behavioral control in Drosophila. eLife. 7. 94 indexed citations
13.
Billings, Jacob, Sadia Shakil, Xiaohong Shen, et al.. (2017). Instantaneous brain dynamics mapped to a continuous state space. NeuroImage. 162. 344–352. 13 indexed citations
14.
Berman, Gordon J, William Bialek, & Joshua W. Shaevitz. (2016). Predictability and hierarchy in Drosophila behavior. Proceedings of the National Academy of Sciences. 113(42). 11943–11948. 119 indexed citations
15.
Deny, Stéphane, Emily L. Mackevicius, Tatsuo S. Okubo, et al.. (2016). Learning stable representations in a changing world with on-line t-SNE: proof of concept in the songbird. 3 indexed citations
16.
17.
Berman, Gordon J, Daniel M. Choi, William Bialek, & Joshua W. Shaevitz. (2014). Mapping the stereotyped behaviour of freely moving fruit flies. Journal of The Royal Society Interface. 11(99). 20140672–20140672. 308 indexed citations breakdown →
18.
Ristroph, Leif, Attila Bergou, Gunnar Ristroph, et al.. (2010). Discovering the flight autostabilizer of fruit flies by inducing aerial stumbles. Proceedings of the National Academy of Sciences. 107(11). 4820–4824. 144 indexed citations
19.
Ristroph, Leif, Gordon J Berman, Attila Bergou, Z. Jane Wang, & Itai Cohen. (2009). Automated hull reconstruction motion tracking (HRMT) applied to sideways maneuvers of free-flying insects. Journal of Experimental Biology. 212(9). 1324–1335. 84 indexed citations
20.
Berman, Gordon J & Z. Jane Wang. (2007). Energy-minimizing kinematics in hovering insect flight. Journal of Fluid Mechanics. 582. 153–168. 367 indexed citations breakdown →

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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