David Cai

3.2k total citations
106 papers, 2.3k citations indexed

About

David Cai is a scholar working on Statistical and Nonlinear Physics, Cognitive Neuroscience and Cellular and Molecular Neuroscience. According to data from OpenAlex, David Cai has authored 106 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Statistical and Nonlinear Physics, 48 papers in Cognitive Neuroscience and 27 papers in Cellular and Molecular Neuroscience. Recurrent topics in David Cai's work include Neural dynamics and brain function (47 papers), Nonlinear Photonic Systems (27 papers) and stochastic dynamics and bifurcation (23 papers). David Cai is often cited by papers focused on Neural dynamics and brain function (47 papers), Nonlinear Photonic Systems (27 papers) and stochastic dynamics and bifurcation (23 papers). David Cai collaborates with scholars based in United States, China and United Arab Emirates. David Cai's co-authors include Niels Grønbech‐Jensen, David W. McLaughlin, A. R. Bishop, Aaditya V. Rangan, Douglas Zhou, Dan Hu, Andrew J. Majda, Gregor Kovačič, Louis Tao and Esteban G. Tabak and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Physical review. B, Condensed matter.

In The Last Decade

David Cai

105 papers receiving 2.3k 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 Cai United States 28 1.1k 736 548 393 337 106 2.3k
L. de Arcangelis Italy 36 1.0k 0.9× 902 1.2× 292 0.5× 189 0.5× 218 0.6× 136 4.3k
Alessandro Torcini Italy 31 1.5k 1.3× 932 1.3× 590 1.1× 1.3k 3.4× 207 0.6× 125 2.8k
Oreste Piro Spain 27 1.0k 0.9× 349 0.5× 219 0.4× 740 1.9× 79 0.2× 96 2.4k
Govindan Rangarajan India 26 676 0.6× 953 1.3× 150 0.3× 369 0.9× 183 0.5× 82 2.7k
Hermann Riecke United States 23 556 0.5× 427 0.6× 183 0.3× 1.0k 2.6× 382 1.1× 83 1.8k
Mark L. Spano United States 32 2.9k 2.6× 1.0k 1.4× 367 0.7× 2.2k 5.5× 411 1.2× 90 5.0k
Elisha Moses Israel 37 919 0.8× 698 0.9× 598 1.1× 713 1.8× 659 2.0× 91 4.7k
Gabriel B. Mindlin Argentina 30 765 0.7× 355 0.5× 236 0.4× 692 1.8× 59 0.2× 146 2.8k
Ricardo L. Viana Brazil 32 2.6k 2.3× 837 1.1× 154 0.3× 1.7k 4.4× 165 0.5× 279 3.9k
H. G. E. Hentschel United States 29 1.0k 0.9× 149 0.2× 294 0.5× 553 1.4× 118 0.4× 110 4.1k

Countries citing papers authored by David Cai

Since Specialization
Citations

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

Fields of papers citing papers by David Cai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Cai

This figure shows the co-authorship network connecting the top 25 collaborators of David Cai. A scholar is included among the top collaborators of David Cai 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 Cai. David Cai 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.
Li, Zhiyu, et al.. (2023). Detecting the influence of the Chinese guiding cases: a text reuse approach. Artificial Intelligence and Law. 32(2). 463–486. 2 indexed citations
2.
Xu, Zhi‐Qin John, Xiaowei Gu, Chengyu Li, et al.. (2020). Neural networks of different species, brain areas and states can be characterized by the probability polling state. European Journal of Neuroscience. 52(7). 3790–3802. 1 indexed citations
3.
Zhou, Douglas, et al.. (2020). A computational investigation of electrotonic coupling between pyramidal cells in the cortex. Journal of Computational Neuroscience. 48(4). 387–407. 3 indexed citations
4.
Zhou, Douglas, et al.. (2019). A Role for Electrotonic Coupling Between Cortical Pyramidal Cells. Frontiers in Computational Neuroscience. 13. 33–33. 2 indexed citations
5.
Zhou, Douglas, et al.. (2019). Effective dispersion in the focusing nonlinear Schrödinger equation. Physical review. E. 100(2). 22215–22215. 5 indexed citations
6.
Xu, Zhi‐Qin John, Guo‐Qiang Bi, Douglas Zhou, & David Cai. (2017). A dynamical state underlying the second order maximum entropy principle in neuronal networks. Communications in Mathematical Sciences. 15(3). 665–692. 3 indexed citations
7.
Zhou, Douglas, et al.. (2016). Compressive sensing reconstruction of feed-forward connectivity in pulse-coupled nonlinear networks. Physical review. E. 93(6). 60201–60201. 7 indexed citations
8.
Newhall, Katherine A., et al.. (2015). Synchrony in stochastically driven neuronal networks with complex topologies. Physical Review E. 91(5). 52806–52806. 1 indexed citations
9.
Zhou, Douglas, Yaoyu Zhang, Yanyang Xiao, & David Cai. (2014). Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics. Frontiers in Computational Neuroscience. 8. 75–75. 10 indexed citations
10.
Kovačič, Gregor, et al.. (2014). Sparsity and Compressed Coding in Sensory Systems. PLoS Computational Biology. 10(8). e1003793–e1003793. 19 indexed citations
11.
Hu, Dan & David Cai. (2013). Adaptation and Optimization of Biological Transport Networks. Physical Review Letters. 111(13). 138701–138701. 94 indexed citations
12.
Chen, Qi, et al.. (2012). Haemodynamics-Driven Developmental Pruning of Brain Vasculature in Zebrafish. PLoS Biology. 10(8). e1001374–e1001374. 180 indexed citations
13.
Kovačič, Gregor, et al.. (2012). Topological effects on dynamics in complex pulse-coupled networks of integrate-and-fire type. Physical Review E. 85(3). 36104–36104. 2 indexed citations
14.
Cai, David, Gregor Kovačič, Peter R. Kramer, et al.. (2010). Dynamics of current-based, Poisson driven, integrate-and-fire neuronal networks. Communications in Mathematical Sciences. 8(2). 541–600. 36 indexed citations
15.
Sun, Yi, Douglas Zhou, Aaditya V. Rangan, & David Cai. (2009). Library-based numerical reduction of the Hodgkin–Huxley neuron for network simulation. Journal of Computational Neuroscience. 27(3). 369–390. 14 indexed citations
16.
Kovačič, Gregor, Louis Tao, David Cai, & Michael Shelley. (2008). Theoretical analysis of reverse-time correlation for idealized orientation tuning dynamics. Journal of Computational Neuroscience. 25(3). 401–438. 2 indexed citations
17.
Rangan, Aaditya V. & David Cai. (2006). Maximum-Entropy Closures for Kinetic Theories of Neuronal Network Dynamics. Physical Review Letters. 96(17). 178101–178101. 40 indexed citations
18.
Rangan, Aaditya V. & David Cai. (2006). Fast numerical methods for simulating large-scale integrate-and-fire neuronal networks. Journal of Computational Neuroscience. 22(1). 81–100. 25 indexed citations
19.
Gershgorin, Boris, Yuri V. Lvov, & David Cai. (2005). Renormalized Waves and Discrete Breathers inβ-Fermi-Pasta-Ulam Chains. Physical Review Letters. 95(26). 264302–264302. 49 indexed citations
20.
Cai, David, A. R. Bishop, & Niels Grønbech‐Jensen. (1996). Spatially localized, temporally quasi-periodic, discrete nonlinear excitations. APS March Meeting Abstracts. 5 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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