Tilo Schwalger

918 total citations
34 papers, 626 citations indexed

About

Tilo Schwalger is a scholar working on Cognitive Neuroscience, Statistical and Nonlinear Physics and Computer Networks and Communications. According to data from OpenAlex, Tilo Schwalger has authored 34 papers receiving a total of 626 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Cognitive Neuroscience, 26 papers in Statistical and Nonlinear Physics and 14 papers in Computer Networks and Communications. Recurrent topics in Tilo Schwalger's work include Neural dynamics and brain function (29 papers), stochastic dynamics and bifurcation (24 papers) and Nonlinear Dynamics and Pattern Formation (13 papers). Tilo Schwalger is often cited by papers focused on Neural dynamics and brain function (29 papers), stochastic dynamics and bifurcation (24 papers) and Nonlinear Dynamics and Pattern Formation (13 papers). Tilo Schwalger collaborates with scholars based in Germany, Switzerland and Japan. Tilo Schwalger's co-authors include Benjamin Lindner, Wulfram Gerstner, Moritz Deger, Jan Benda, Lutz Schimansky-Geier, Andreas V. M. Herz, Richard Naud, Bastian Pietras, LieJune Shiau and Rodrigo Quian Quiroga and has published in prestigious journals such as Physical Review Letters, Journal of Neuroscience and Scientific Reports.

In The Last Decade

Tilo Schwalger

32 papers receiving 616 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tilo Schwalger Germany 14 530 435 182 171 137 34 626
Aaditya V. Rangan United States 15 475 0.9× 320 0.7× 228 1.3× 124 0.7× 100 0.7× 46 641
Jun-nosuke Teramae Japan 15 558 1.1× 459 1.1× 189 1.0× 438 2.6× 142 1.0× 31 846
Zachary P. Kilpatrick United States 16 459 0.9× 257 0.6× 130 0.7× 175 1.0× 74 0.5× 48 624
Pulin Gong Australia 19 816 1.5× 309 0.7× 289 1.6× 192 1.1× 90 0.7× 54 1.0k
Douglas Zhou China 15 344 0.6× 200 0.5× 181 1.0× 65 0.4× 95 0.7× 56 570
Hatsuo Hayashi Japan 13 350 0.7× 261 0.6× 181 1.0× 161 0.9× 87 0.6× 37 524
Maksim Bazhenov United States 9 444 0.8× 346 0.8× 228 1.3× 296 1.7× 47 0.3× 15 682
Kazuyuki Aihara Japan 14 507 1.0× 391 0.9× 151 0.8× 201 1.2× 109 0.8× 35 870
Toshio Aoyagi Japan 16 564 1.1× 358 0.8× 171 0.9× 467 2.7× 102 0.7× 63 919
Yasuhiro Tsubo Japan 11 364 0.7× 203 0.5× 176 1.0× 130 0.8× 74 0.5× 19 477

Countries citing papers authored by Tilo Schwalger

Since Specialization
Citations

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

Fields of papers citing papers by Tilo Schwalger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tilo Schwalger

This figure shows the co-authorship network connecting the top 25 collaborators of Tilo Schwalger. A scholar is included among the top collaborators of Tilo Schwalger 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 Tilo Schwalger. Tilo Schwalger 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.
Pleßer, Hans Ekkehard, Andrew P. Davison, Markus Diesmann, et al.. (2025). Building on models—a perspective for computational neuroscience. Cerebral Cortex. 35(11). 1 indexed citations
2.
Schwalger, Tilo, et al.. (2024). Efficient coding in biophysically realistic excitatory-inhibitory spiking networks. eLife. 13. 1 indexed citations
3.
Löcherbach, Eva, et al.. (2023). On a Finite-Size Neuronal Population Equation. SIAM Journal on Applied Dynamical Systems. 22(2). 996–1029. 5 indexed citations
4.
Pietras, Bastian, et al.. (2022). Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity. PLoS Computational Biology. 18(12). e1010809–e1010809. 9 indexed citations
5.
Schwalger, Tilo. (2021). Mapping input noise to escape noise in integrate-and-fire neurons: a level-crossing approach. PUBLISSO (German National Library of Medicine). 5 indexed citations
6.
Schwalger, Tilo, et al.. (2021). When shared concept cells support associations: Theory of overlapping memory engrams. PLoS Computational Biology. 17(12). e1009691–e1009691. 22 indexed citations
7.
Pietras, Bastian, et al.. (2020). Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons. Physical review. E. 102(2). 22407–22407. 13 indexed citations
8.
Gerstner, Wulfram, et al.. (2019). How single neuron properties shape chaotic dynamics and signal transmission in random neural networks. PLoS Computational Biology. 15(6). e1007122–e1007122. 37 indexed citations
9.
Schwalger, Tilo, Moritz Deger, & Wulfram Gerstner. (2017). Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. PLoS Computational Biology. 13(4). e1005507–e1005507. 92 indexed citations
10.
Kastner, David B., et al.. (2016). A Model of Synaptic Reconsolidation. Frontiers in Neuroscience. 10. 206–206. 7 indexed citations
11.
Schwalger, Tilo, et al.. (2015). Statistical structure of neural spiking under non-Poissonian or other non-white stimulation. Journal of Computational Neuroscience. 39(1). 29–51. 47 indexed citations
12.
Deger, Moritz, Tilo Schwalger, Richard Naud, & Wulfram Gerstner. (2014). Fluctuations and information filtering in coupled populations of spiking neurons with adaptation. Physical Review E. 90(6). 62704–62704. 30 indexed citations
13.
Schwalger, Tilo & Benjamin Lindner. (2013). Patterns of interval correlations in neural oscillators with adaptation. Frontiers in Computational Neuroscience. 7. 164–164. 32 indexed citations
14.
Schwalger, Tilo, et al.. (2013). Characteristic Effects of Stochastic Oscillatory Forcing on Neural Firing: Analytical Theory and Comparison to Paddlefish Electroreceptor Data. PLoS Computational Biology. 9(8). e1003170–e1003170. 18 indexed citations
15.
Schwalger, Tilo, et al.. (2012). Channel Noise from Both Slow Adaptation Currents and Fast Currents Is Required to Explain Spike-Response Variability in a Sensory Neuron. Journal of Neuroscience. 32(48). 17332–17344. 49 indexed citations
16.
Schwalger, Tilo, et al.. (2011). Relation between cooperative molecular motors and active Brownian particles. Physical Review E. 83(5). 51913–51913. 15 indexed citations
17.
Schwalger, Tilo, et al.. (2010). How Noisy Adaptation of Neurons Shapes Interspike Interval Histograms and Correlations. PLoS Computational Biology. 6(12). e1001026–e1001026. 61 indexed citations
18.
Schwalger, Tilo & Benjamin Lindner. (2008). Higher-order statistics of a bistable system driven by dichotomous colored noise. Physical Review E. 78(2). 21121–21121. 11 indexed citations
19.
Schwalger, Tilo & Lutz Schimansky-Geier. (2008). Interspike interval statistics of a leaky integrate-and-fire neuron driven by Gaussian noise with large correlation times. Physical Review E. 77(3). 31914–31914. 37 indexed citations
20.
Lindner, Benjamin & Tilo Schwalger. (2007). Correlations in the Sequence of Residence Times. Physical Review Letters. 98(21). 210603–210603. 10 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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