David G. C. Hildebrand

2.7k total citations
23 papers, 988 citations indexed

About

David G. C. Hildebrand is a scholar working on Structural Biology, Cognitive Neuroscience and Biophysics. According to data from OpenAlex, David G. C. Hildebrand has authored 23 papers receiving a total of 988 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Structural Biology, 5 papers in Cognitive Neuroscience and 5 papers in Biophysics. Recurrent topics in David G. C. Hildebrand's work include Neural dynamics and brain function (5 papers), Advanced Electron Microscopy Techniques and Applications (5 papers) and Zebrafish Biomedical Research Applications (4 papers). David G. C. Hildebrand is often cited by papers focused on Neural dynamics and brain function (5 papers), Advanced Electron Microscopy Techniques and Applications (5 papers) and Zebrafish Biomedical Research Applications (4 papers). David G. C. Hildebrand collaborates with scholars based in United States, South Korea and Germany. David G. C. Hildebrand's co-authors include Tran Minh Quan, Won‐Ki Jeong, Jeff W. Lichtman, Daniel R. Berger, Richard Schalek, Josh Morgan, Kenneth J. Hayworth, Jennifer Li, João C. Marques and Dal Hyung Kim and has published in prestigious journals such as Nature, Cell and Nature Communications.

In The Last Decade

David G. C. Hildebrand

22 papers receiving 974 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
David G. C. Hildebrand United States 14 220 203 197 187 171 23 988
Jan Funke United States 13 214 1.0× 64 0.3× 84 0.4× 153 0.8× 219 1.3× 29 757
Joergen Kornfeld Germany 11 100 0.5× 75 0.4× 149 0.8× 126 0.7× 216 1.3× 13 628
Masashi Tanimoto Japan 17 266 1.2× 177 0.9× 196 1.0× 133 0.7× 253 1.5× 39 1.2k
Michał Januszewski United States 12 80 0.4× 58 0.3× 102 0.5× 104 0.6× 163 1.0× 23 678
Kenneth J. Hayworth United States 15 559 2.5× 343 1.7× 379 1.9× 669 3.6× 312 1.8× 30 2.0k
Edward Soucy United States 13 777 3.5× 174 0.9× 473 2.4× 296 1.6× 177 1.0× 19 1.5k
Shawn Mikula United States 13 154 0.7× 28 0.1× 196 1.0× 183 1.0× 246 1.4× 21 860
Zongcai Ruan China 9 114 0.5× 45 0.2× 234 1.2× 231 1.2× 480 2.8× 16 914
Srinivas C. Turaga United States 16 668 3.0× 133 0.7× 549 2.8× 856 4.6× 760 4.4× 25 2.4k
Nathan Clack United States 9 358 1.6× 48 0.2× 438 2.2× 188 1.0× 282 1.6× 10 933

Countries citing papers authored by David G. C. Hildebrand

Since Specialization
Citations

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

Fields of papers citing papers by David G. C. Hildebrand

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David G. C. Hildebrand. 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 David G. C. Hildebrand. The network helps show where David G. C. Hildebrand may publish in the future.

Co-authorship network of co-authors of David G. C. Hildebrand

This figure shows the co-authorship network connecting the top 25 collaborators of David G. C. Hildebrand. A scholar is included among the top collaborators of David G. C. Hildebrand 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 David G. C. Hildebrand. David G. C. Hildebrand 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.
Kuan, Aaron T., Laura Driscoll, David G. C. Hildebrand, et al.. (2024). Synaptic wiring motifs in posterior parietal cortex support decision-making. Nature. 627(8003). 367–373. 12 indexed citations
2.
Nguyen, Tri, Logan A. Thomas, Jeff L. Rhoades, et al.. (2022). Structured cerebellar connectivity supports resilient pattern separation. Nature. 613(7944). 543–549. 24 indexed citations
3.
Hildebrand, David G. C., et al.. (2022). Organization of the gravity-sensing system in zebrafish. Nature Communications. 13(1). 5060–5060. 29 indexed citations
4.
Phelps, Jasper S., David G. C. Hildebrand, Brett J. Graham, et al.. (2021). Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell. 184(3). 759–774.e18. 117 indexed citations
5.
Hildebrand, David G. C., et al.. (2021). High plasticity in marmoset monkey vocal development from infancy to adulthood. Science Advances. 7(27). 25 indexed citations
6.
Kimura, Yukiko, et al.. (2021). Central vestibular tuning arises from patterned convergence of otolith afferents. Neuron. 109(5). 905–905. 3 indexed citations
7.
Dunn, Timothy, Jesse D. Marshall, Kyle S. Severson, et al.. (2021). Geometric deep learning enables 3D kinematic profiling across species and environments. Nature Methods. 18(5). 564–573. 111 indexed citations
8.
Choi, Junyoung, et al.. (2021). ZeVis: A Visual Analytics System for Exploration of a Larval Zebrafish Brain in Serial-Section Electron Microscopy Images. IEEE Access. 9. 78755–78763. 4 indexed citations
9.
Quan, Tran Minh, David G. C. Hildebrand, & Won‐Ki Jeong. (2021). FusionNet: A Deep Fully Residual Convolutional Neural Network for Image Segmentation in Connectomics. Frontiers in Computer Science. 3. 108 indexed citations
10.
Kimura, Yukiko, et al.. (2020). Central Vestibular Tuning Arises from Patterned Convergence of Otolith Afferents. Neuron. 108(4). 748–762.e4. 23 indexed citations
11.
Swinburne, Ian A., Kishore Mosaliganti, Srigokul Upadhyayula, et al.. (2018). Lamellar projections in the endolymphatic sac act as a relief valve to regulate inner ear pressure. eLife. 7. 20 indexed citations
12.
Wolf, Sébastien, Alexis Dubreuil, Tommaso Bertoni, et al.. (2017). Sensorimotor computation underlying phototaxis in zebrafish. Nature Communications. 8(1). 651–651. 71 indexed citations
13.
Kim, Dal Hyung, Jung Soo Kim, João C. Marques, et al.. (2017). Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish. Nature Methods. 14(11). 1107–1114. 143 indexed citations
14.
Cicconet, Marcelo, David G. C. Hildebrand, & Hunter Elliott. (2017). Finding Mirror Symmetry via Registration and Optimal Symmetric Pairwise Assignment of Curves. 1749–1758. 21 indexed citations
15.
Reed, Michael D., David G. C. Hildebrand, Gabrielle Santangelo, et al.. (2015). Assessing contributions of nucleus accumbens shell subregions to reward-seeking behavior. Drug and Alcohol Dependence. 153. 369–373. 11 indexed citations
16.
Hayworth, Kenneth J., Josh Morgan, Richard Schalek, et al.. (2014). Imaging ATUM ultrathin section libraries with WaferMapper: a multi-scale approach to EM reconstruction of neural circuits. Frontiers in Neural Circuits. 8. 68–68. 173 indexed citations
17.
Choi, Woohyuk, et al.. (2014). Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems. IEEE Transactions on Visualization and Computer Graphics. 20(12). 2407–2416. 28 indexed citations
18.
Somarowthu, Srinivas, et al.. (2011). High‐performance prediction of functional residues in proteins with machine learning and computed input features. Biopolymers. 95(6). 390–400. 39 indexed citations
19.
Hildebrand, David G. C., et al.. (2010). High Conservation of Amino Acids with Anomalous Protonation Behavior. Current Bioinformatics. 5(2). 134–140. 3 indexed citations
20.
Hildebrand, David G. C., et al.. (2008). Simulation of elastic rods using conformal geometric algebra. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft).

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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