Tatjana Tchumatchenko

1.1k total citations
39 papers, 574 citations indexed

About

Tatjana Tchumatchenko is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Statistical and Nonlinear Physics. According to data from OpenAlex, Tatjana Tchumatchenko has authored 39 papers receiving a total of 574 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Cognitive Neuroscience, 24 papers in Cellular and Molecular Neuroscience and 13 papers in Statistical and Nonlinear Physics. Recurrent topics in Tatjana Tchumatchenko's work include Neural dynamics and brain function (27 papers), Neuroscience and Neuropharmacology Research (13 papers) and stochastic dynamics and bifurcation (12 papers). Tatjana Tchumatchenko is often cited by papers focused on Neural dynamics and brain function (27 papers), Neuroscience and Neuropharmacology Research (13 papers) and stochastic dynamics and bifurcation (12 papers). Tatjana Tchumatchenko collaborates with scholars based in Germany, United States and United Kingdom. Tatjana Tchumatchenko's co-authors include Fred Wolf, Maxim Volgushev, A. Yu. Malyshev, Claudia Clopath, T. Geisel, Erin M. Schuman, Andreas Nold, Mike Heilemann, Anne‐Sophie Hafner and Chao Sun and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Physical Review Letters.

In The Last Decade

Tatjana Tchumatchenko

37 papers receiving 570 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tatjana Tchumatchenko Germany 13 394 297 134 112 79 39 574
Javier G. Orlandi Spain 12 280 0.7× 242 0.8× 86 0.6× 113 1.0× 61 0.8× 28 500
Roman R. Poznański Malaysia 15 313 0.8× 254 0.9× 99 0.7× 146 1.3× 47 0.6× 60 578
Sami El‐Boustani France 13 624 1.6× 412 1.4× 138 1.0× 81 0.7× 112 1.4× 19 736
Michel A. Picardo France 12 628 1.6× 625 2.1× 78 0.6× 125 1.1× 66 0.8× 14 951
Michiel W. H. Remme Germany 14 484 1.2× 390 1.3× 74 0.6× 89 0.8× 63 0.8× 26 630
Irina Erchova United Kingdom 13 564 1.4× 489 1.6× 129 1.0× 103 0.9× 82 1.0× 24 853
Mario Dipoppa United States 11 366 0.9× 241 0.8× 37 0.3× 117 1.0× 57 0.7× 14 556
Idan Segev Israel 2 378 1.0× 244 0.8× 156 1.2× 75 0.7× 104 1.3× 3 505
Rodrigo F. Oliveira Brazil 13 246 0.6× 223 0.8× 49 0.4× 191 1.7× 40 0.5× 31 538
Nicolas M. Brunet United States 16 999 2.5× 439 1.5× 79 0.6× 144 1.3× 76 1.0× 28 1.3k

Countries citing papers authored by Tatjana Tchumatchenko

Since Specialization
Citations

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

Fields of papers citing papers by Tatjana Tchumatchenko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tatjana Tchumatchenko

This figure shows the co-authorship network connecting the top 25 collaborators of Tatjana Tchumatchenko. A scholar is included among the top collaborators of Tatjana Tchumatchenko 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 Tatjana Tchumatchenko. Tatjana Tchumatchenko 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.
Rizzoli, Silvio O., et al.. (2025). How energy determines spatial localisation and copy number of molecules in neurons. Nature Communications. 16(1). 1424–1424. 2 indexed citations
2.
Tchumatchenko, Tatjana, et al.. (2024). A framework for the emergence and analysis of language in social learning agents. Nature Communications. 15(1). 7590–7590.
3.
Henneberger, Christian, et al.. (2024). Astrocytes enhance plasticity response during reversal learning. Communications Biology. 7(1). 852–852. 7 indexed citations
4.
Chater, Thomas E., et al.. (2024). Competitive processes shape multi-synapse plasticity along dendritic segments. Nature Communications. 15(1). 7572–7572. 3 indexed citations
5.
Tchumatchenko, Tatjana, et al.. (2023). Targeting operational regimes of interest in recurrent neural networks. PLoS Computational Biology. 19(5). e1011097–e1011097. 3 indexed citations
6.
Chater, Thomas E., et al.. (2023). Linking spontaneous and stimulated spine dynamics. Communications Biology. 6(1). 930–930. 4 indexed citations
7.
Rose, Tobias, et al.. (2022). How to incorporate biological insights into network models and why it matters. The Journal of Physiology. 601(15). 3037–3053. 6 indexed citations
8.
Nold, Andreas, et al.. (2022). Synaptic plasticity controls the emergence of population-wide invariant representations in balanced network models. Physical Review Research. 4(1). 3 indexed citations
9.
Spacek, Martin A., et al.. (2022). In vivo extracellular recordings of thalamic and cortical visual responses reveal V1 connectivity rules. Proceedings of the National Academy of Sciences. 119(41). e2207032119–e2207032119. 5 indexed citations
10.
Nold, Andreas, et al.. (2022). Modulation of working memory duration by synaptic and astrocytic mechanisms. PLoS Computational Biology. 18(10). e1010543–e1010543. 9 indexed citations
11.
Tchumatchenko, Tatjana, et al.. (2022). Dopamine and serotonin interplay for valence-based spatial learning. Cell Reports. 39(2). 110645–110645. 12 indexed citations
12.
Nold, Andreas, et al.. (2022). Unbiased choice of global clustering parameters for single-molecule localization microscopy. Scientific Reports. 12(1). 22561–22561. 6 indexed citations
13.
Sun, Chao, Andreas Nold, Claudia M. Fusco, et al.. (2021). The prevalence and specificity of local protein synthesis during neuronal synaptic plasticity. Science Advances. 7(38). eabj0790–eabj0790. 54 indexed citations
14.
Kolb, Simon, et al.. (2021). The orbitofrontal cortex maps future navigational goals. Nature. 599(7885). 449–452. 47 indexed citations
15.
Nold, Andreas, et al.. (2020). How Repair-or-Dispose Decisions Under Stress Can Initiate Disease Progression. iScience. 23(11). 101701–101701. 1 indexed citations
16.
Tchumatchenko, Tatjana, et al.. (2016). Temporal pairwise spike correlations fully capture single-neuron information. Nature Communications. 7(1). 13805–13805. 14 indexed citations
17.
Bernardino, Éléna Di, José R. León, & Tatjana Tchumatchenko. (2014). Cross-Correlations and Joint Gaussianity in Multivariate Level Crossing Models. PubMed. 4(1). 22–22. 3 indexed citations
18.
Rosenbaum, Robert, Tatjana Tchumatchenko, & Rubén Moreno‐Bote. (2014). Correlated neuronal activity and its relationship to coding, dynamics and network architecture. Frontiers in Computational Neuroscience. 8. 102–102. 6 indexed citations
19.
Tchumatchenko, Tatjana, A. Yu. Malyshev, Fred Wolf, & Maxim Volgushev. (2011). Ultrafast Population Encoding by Cortical Neurons. Journal of Neuroscience. 31(34). 12171–12179. 71 indexed citations
20.
Tchumatchenko, Tatjana, A. Yu. Malyshev, T. Geisel, Maxim Volgushev, & Fred Wolf. (2010). Correlations and Synchrony in Threshold Neuron Models. Physical Review Letters. 104(5). 58102–58102. 60 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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