Gregor Kovačič

1.6k total citations
62 papers, 1.1k citations indexed

About

Gregor Kovačič is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Gregor Kovačič has authored 62 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Statistical and Nonlinear Physics, 22 papers in Computer Networks and Communications and 17 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Gregor Kovačič's work include Nonlinear Dynamics and Pattern Formation (22 papers), Neural dynamics and brain function (15 papers) and Quantum chaos and dynamical systems (15 papers). Gregor Kovačič is often cited by papers focused on Nonlinear Dynamics and Pattern Formation (22 papers), Neural dynamics and brain function (15 papers) and Quantum chaos and dynamical systems (15 papers). Gregor Kovačič collaborates with scholars based in United States, China and United Arab Emirates. Gregor Kovačič's co-authors include Gino Biondini, Stephen Wiggins, David Cai, Tasso J. Kaper, Roberto Camassa, Darryl D. Holm, Douglas Zhou, Aaditya V. Rangan, Thomas A. Wettergren and Katherine A. Newhall and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Scientific Reports.

In The Last Decade

Gregor Kovačič

60 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gregor Kovačič United States 18 740 256 238 205 163 62 1.1k
James Murdock United States 11 461 0.6× 229 0.9× 153 0.6× 269 1.3× 37 0.2× 34 1.3k
Wolf‐Jürgen Beyn Germany 18 365 0.5× 315 1.2× 102 0.4× 278 1.4× 43 0.3× 57 1.3k
Mark Levi United States 20 686 0.9× 301 1.2× 143 0.6× 324 1.6× 32 0.2× 62 1.2k
Yoshisuke Ueda Japan 16 838 1.1× 615 2.4× 76 0.3× 210 1.0× 47 0.3× 54 1.3k
Piotr Kowalczyk Poland 12 814 1.1× 390 1.5× 31 0.1× 332 1.6× 72 0.4× 28 1.6k
Shin‐ichi Sasa Japan 20 1.6k 2.2× 114 0.4× 641 2.7× 251 1.2× 178 1.1× 65 2.1k
P. S. Landa Russia 18 1.2k 1.6× 901 3.5× 359 1.5× 106 0.5× 159 1.0× 74 1.7k
Peter Szmolyan Austria 20 1.2k 1.6× 1.0k 4.0× 78 0.3× 101 0.5× 188 1.2× 50 1.9k
Hong Zhao China 23 1.3k 1.7× 180 0.7× 558 2.3× 63 0.3× 26 0.2× 141 2.1k
D W Jordan United Kingdom 7 300 0.4× 162 0.6× 136 0.6× 133 0.6× 33 0.2× 14 1.0k

Countries citing papers authored by Gregor Kovačič

Since Specialization
Citations

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

Fields of papers citing papers by Gregor Kovačič

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gregor Kovačič

This figure shows the co-authorship network connecting the top 25 collaborators of Gregor Kovačič. A scholar is included among the top collaborators of Gregor Kovačič 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 Gregor Kovačič. Gregor Kovačič 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.
Newhall, Katherine A., et al.. (2021). Network mechanism for insect olfaction. Cognitive Neurodynamics. 15(1). 103–129. 4 indexed citations
2.
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
3.
Banks, Jeffrey W., William D. Henshaw, Alexander V. Kildishev, et al.. (2020). A High-order Accurate Scheme for the Dispersive Maxwell's Equations and Material Interfaces on Overset Grids. 1–2. 1 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.
Kovačič, Gregor, et al.. (2018). Cascade model of wave turbulence. Physical review. E. 97(6). 62140–62140. 2 indexed citations
7.
Kovačič, Gregor, et al.. (2018). The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic. Frontiers in Computational Neuroscience. 12. 47–47. 6 indexed citations
8.
Kovačič, Gregor, et al.. (2016). Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling. Scientific Reports. 6(1). 31976–31976. 13 indexed citations
9.
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
10.
Kovačič, Gregor, et al.. (2014). Sparsity and Compressed Coding in Sensory Systems. PLoS Computational Biology. 10(8). e1003793–e1003793. 19 indexed citations
11.
Kovačič, Gregor, et al.. (2014). Network dynamics for optimal compressive-sensing input-signal recovery. Physical Review E. 90(4). 42908–42908. 6 indexed citations
12.
Johnson, Daniel C., et al.. (2014). Dynamics of the exponential integrate-and-fire model with slow currents and adaptation. Journal of Computational Neuroscience. 37(1). 161–180. 22 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.
Newhall, Katherine A., Gregor Kovačič, Peter R. Kramer, & David Cai. (2010). Cascade-induced synchrony in stochastically driven neuronal networks. Physical Review E. 82(4). 41903–41903. 33 indexed citations
16.
Kovačič, Gregor, Louis Tao, Aaditya V. Rangan, & David Cai. (2009). Fokker-Planck description of conductance-based integrate-and-fire neuronal networks. Physical Review E. 80(2). 21904–21904. 16 indexed citations
17.
Kovačič, Gregor, et al.. (2009). Renormalized Resonance Quartets in Dispersive Wave Turbulence. Physical Review Letters. 103(2). 24502–24502. 16 indexed citations
18.
Tao, Louis, et al.. (2009). Multiscale modeling of the primary visual cortex. IEEE Engineering in Medicine and Biology Magazine. 28(3). 19–24. 14 indexed citations
19.
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
20.
Rangan, Aaditya V., Gregor Kovačič, & David Cai. (2008). Kinetic theory for neuronal networks with fast and slow excitatory conductances driven by the same spike train. Physical Review E. 77(4). 41915–41915. 25 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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