Douglas Zhou

856 total citations
56 papers, 570 citations indexed

About

Douglas Zhou is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, Douglas Zhou has authored 56 papers receiving a total of 570 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Cognitive Neuroscience, 25 papers in Cellular and Molecular Neuroscience and 23 papers in Statistical and Nonlinear Physics. Recurrent topics in Douglas Zhou's work include Neural dynamics and brain function (37 papers), stochastic dynamics and bifurcation (17 papers) and Photoreceptor and optogenetics research (11 papers). Douglas Zhou is often cited by papers focused on Neural dynamics and brain function (37 papers), stochastic dynamics and bifurcation (17 papers) and Photoreceptor and optogenetics research (11 papers). Douglas Zhou collaborates with scholars based in China, United States and United Arab Emirates. Douglas Zhou's co-authors include David Cai, Aaditya V. Rangan, Yanyang Xiao, Gregor Kovačič, Pingwen Zhang, Yi Sun, Yaoyu Zhang, David W. McLaughlin, Zhi‐Qin John Xu and E Weinan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and The Journal of Chemical Physics.

In The Last Decade

Douglas Zhou

55 papers receiving 551 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Douglas Zhou China 15 344 200 181 95 65 56 570
Louis Tao China 16 480 1.4× 243 1.2× 224 1.2× 137 1.4× 77 1.2× 66 876
Sorinel A. Oprisan United States 14 540 1.6× 146 0.7× 190 1.0× 32 0.3× 78 1.2× 65 717
V. V. Makarov Russia 12 355 1.0× 69 0.3× 79 0.4× 80 0.8× 63 1.0× 59 674
Tobias Wagner Germany 13 610 1.8× 81 0.4× 166 0.9× 61 0.6× 102 1.6× 18 809
Serafim Rodrigues Spain 15 448 1.3× 264 1.3× 240 1.3× 52 0.5× 177 2.7× 45 819
Alicia d’Anjou Spain 15 320 0.9× 400 2.0× 128 0.7× 125 1.3× 260 4.0× 48 749
S. Camalet France 12 135 0.4× 217 1.1× 43 0.2× 202 2.1× 101 1.6× 28 1.0k
Yoshiki Kashimori Japan 11 203 0.6× 75 0.4× 75 0.4× 51 0.5× 50 0.8× 68 509
Aaditya V. Rangan United States 15 475 1.4× 320 1.6× 228 1.3× 100 1.1× 124 1.9× 46 641
Asya Shpiro United States 8 373 1.1× 71 0.4× 86 0.5× 43 0.5× 49 0.8× 10 547

Countries citing papers authored by Douglas Zhou

Since Specialization
Citations

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

Fields of papers citing papers by Douglas Zhou

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Douglas Zhou

This figure shows the co-authorship network connecting the top 25 collaborators of Douglas Zhou. A scholar is included among the top collaborators of Douglas Zhou 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 Douglas Zhou. Douglas Zhou 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.
Qian, Dandan, Wei Li, Ziling Wang, et al.. (2022). A striatal SOM-driven ChAT-iMSN loop generates beta oscillations and produces motor deficits. Cell Reports. 40(3). 111111–111111. 5 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). Compressive Sensing Inference of Neuronal Network Connectivity in Balanced Neuronal Dynamics. Frontiers in Neuroscience. 13. 1101–1101. 9 indexed citations
5.
Zhou, Douglas, et al.. (2019). A Role for Electrotonic Coupling Between Cortical Pyramidal Cells. Frontiers in Computational Neuroscience. 13. 33–33. 2 indexed citations
6.
Zhou, Douglas, et al.. (2019). Effective dispersion in the focusing nonlinear Schrödinger equation. Physical review. E. 100(2). 22215–22215. 5 indexed citations
7.
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
8.
Zhang, Yaoyu, Yanyang Xiao, Douglas Zhou, & David Cai. (2017). Spike-Triggered Regression for Synaptic Connectivity Reconstruction in Neuronal Networks. Frontiers in Computational Neuroscience. 11. 101–101. 3 indexed citations
9.
Xu, Jiamin, et al.. (2017). The characterization of hippocampal theta-driving neurons — a time-delayed mutual information approach. Scientific Reports. 7(1). 5637–5637. 7 indexed citations
10.
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
11.
Zhang, Yaoyu, Yanyang Xiao, Douglas Zhou, & David Cai. (2016). Granger causality analysis with nonuniform sampling and its application to pulse-coupled nonlinear dynamics. Physical review. E. 93(4). 42217–42217. 1 indexed citations
12.
Zhou, Douglas, et al.. (2015). Low-rank network decomposition reveals structural characteristics of small-world networks. Physical Review E. 92(6). 62822–62822. 16 indexed citations
13.
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
14.
Kovačič, Gregor, et al.. (2014). Sparsity and Compressed Coding in Sensory Systems. PLoS Computational Biology. 10(8). e1003793–e1003793. 19 indexed citations
15.
Kovačič, Gregor, et al.. (2014). Network dynamics for optimal compressive-sensing input-signal recovery. Physical Review E. 90(4). 42908–42908. 6 indexed citations
16.
Zhou, Douglas, et al.. (2014). Renormalized dispersion relations ofβ-Fermi-Pasta-Ulam chains in equilibrium and nonequilibrium states. Physical Review E. 90(3). 32925–32925. 7 indexed citations
17.
Liu, Nan, et al.. (2014). Bilinearity in Spatiotemporal Integration of Synaptic Inputs. PLoS Computational Biology. 10(12). e1004014–e1004014. 8 indexed citations
18.
Zhou, Douglas, Yanyang Xiao, Yaoyu Zhang, Zhi‐Qin John Xu, & David Cai. (2014). Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems. PLoS ONE. 9(2). e87636–e87636. 23 indexed citations
19.
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
20.
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

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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