Anna Levina

1.9k total citations
38 papers, 1.0k citations indexed

About

Anna Levina is a scholar working on Cognitive Neuroscience, Statistical and Nonlinear Physics and Artificial Intelligence. According to data from OpenAlex, Anna Levina has authored 38 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Cognitive Neuroscience, 11 papers in Statistical and Nonlinear Physics and 10 papers in Artificial Intelligence. Recurrent topics in Anna Levina's work include Neural dynamics and brain function (22 papers), Advanced Memory and Neural Computing (10 papers) and stochastic dynamics and bifurcation (7 papers). Anna Levina is often cited by papers focused on Neural dynamics and brain function (22 papers), Advanced Memory and Neural Computing (10 papers) and stochastic dynamics and bifurcation (7 papers). Anna Levina collaborates with scholars based in Germany, United States and United Kingdom. Anna Levina's co-authors include J. Michael Herrmann, T. Geisel, Viola Priesemann, Marc Timme, Jan Nagler, Jürgen Jost, Tatiana A. Engel, Johannes Zierenberg, Menahem Segal and Elisha Moses and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nature Communications.

In The Last Decade

Anna Levina

34 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Anna Levina Germany	15	677	443	231	195	121	38	1.0k
Bruno Cessac France	15	474 0.7×	276 0.6×	134 0.6×	159 0.8×	177 1.5×	54	712
Thomas Petermann Switzerland	11	854 1.3×	415 0.9×	283 1.2×	145 0.7×	91 0.8×	17	1.1k
Ruedi Stoop Switzerland	17	290 0.4×	354 0.8×	46 0.2×	73 0.4×	141 1.2×	83	898
Antônio M. Batista Brazil	23	867 1.3×	1.2k 2.8×	213 0.9×	188 1.0×	80 0.7×	147	1.9k
Michael A. Buice United States	16	763 1.1×	261 0.6×	397 1.7×	126 0.6×	109 0.9×	25	989
R. Stoop Switzerland	23	318 0.5×	482 1.1×	52 0.2×	531 2.7×	106 0.9×	104	1.7k
Juan G. Restrepo United States	24	519 0.8×	774 1.7×	265 1.1×	197 1.0×	80 0.7×	60	2.0k
Eckehard Olbrich Germany	18	567 0.8×	424 1.0×	77 0.3×	38 0.2×	233 1.9×	60	1.3k
N. V. Agudov Russia	16	251 0.4×	759 1.7×	192 0.8×	410 2.1×	73 0.6×	25	1.2k
Ernest Barreto United States	21	929 1.4×	782 1.8×	509 2.2×	81 0.4×	39 0.3×	40	1.6k

Countries citing papers authored by Anna Levina

Since Specialization

Citations

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

Fields of papers citing papers by Anna Levina

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anna Levina

This figure shows the co-authorship network connecting the top 25 collaborators of Anna Levina. A scholar is included among the top collaborators of Anna Levina 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 Anna Levina. Anna Levina is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Safavi, Shervin, Matthew Chalk, Nikos K. Logothetis, & Anna Levina. (2024). Signatures of criticality in efficient coding networks. Proceedings of the National Academy of Sciences. 121(41). e2302730121–e2302730121. 9 indexed citations

Levina, Anna, et al.. (2024). Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning. PLoS Computational Biology. 20(10). e1012510–e1012510.

Hefferon, Kathleen, et al.. (2024). Versatile video assignment improves undergraduates’ learning and confidence. Active Learning in Higher Education. 27(1). 123–141. 1 indexed citations

Yamamoto, Hideaki, F. Paul Spitzner, Tomohiro Konno, et al.. (2023). Modular architecture facilitates noise-driven control of synchrony in neuronal networks. Science Advances. 9(34). eade1755–eade1755. 20 indexed citations

Martius, Georg, et al.. (2023). Locally adaptive cellular automata for goal-oriented self-organization.

Denker, Manfred, et al.. (2023). Estimation by Stable Motions and its Applications. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 43(1).

Shi, Yan-Liang, Nicholas A. Steinmetz, Marc Alwin Gieselmann, et al.. (2023). Intrinsic timescales in the visual cortex change with selective attention and reflect spatial connectivity. Nature Communications. 14(1). 1858–1858. 40 indexed citations

Shi, Yan-Liang, et al.. (2023). Spatial and temporal correlations in neural networks with structured connectivity. Physical Review Research. 5(1). 12 indexed citations

Cheung, C. C., T. J. Johnson, P. Jean, et al.. (2022). Fermi LAT Gamma-ray Detection of the Recurrent Nova RS Ophiuchi during its 2021 Outburst. The Astrophysical Journal. 935(1). 44–44. 22 indexed citations

10.

Engel, Tatiana A., et al.. (2022). A flexible Bayesian framework for unbiased estimation of timescales. Nature Computational Science. 2(3). 193–204. 15 indexed citations

11.

Bao, Jianxin, et al.. (2022). Detecting Cochlear Synaptopathy Through Curvature Quantification of the Auditory Brainstem Response. Frontiers in Cellular Neuroscience. 16. 851500–851500. 7 indexed citations

12.

Levina, Anna. (2021). Portfolio Risk Management of Project Network Interdependency (on the Example of Agroholding). 10(1). 33–43. 1 indexed citations

13.

Priesemann, Viola, et al.. (2021). Self-Organization Toward Criticality by Synaptic Plasticity. Frontiers in Physics. 9. 33 indexed citations

14.

Levina, Anna, et al.. (2020). Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions. PLoS Computational Biology. 16(12). e1008503–e1008503. 10 indexed citations

15.

Zierenberg, Johannes, Jens Wilting, Viola Priesemann, & Anna Levina. (2020). Description of spreading dynamics by microscopic network models and macroscopic branching processes can differ due to coalescence. Physical review. E. 101(2). 22301–22301. 14 indexed citations

16.

Levina, Anna, et al.. (2019). Refined parcellation of the nervous system by algorithmic detection of hidden features within communities. Physical review. E. 100(1). 12301–12301. 2 indexed citations

17.

Levina, Anna & Viola Priesemann. (2017). Subsampling scaling. Nature Communications. 8(1). 15140–15140. 62 indexed citations

18.

Jost, Jürgen, et al.. (2015). Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity. PLoS Computational Biology. 11(9). e1004420–e1004420. 41 indexed citations

19.

Levina, Anna, J. Michael Herrmann, & T. Geisel. (2009). Phase Transitions towards Criticality in a Neural System with Adaptive Interactions. Physical Review Letters. 102(11). 118110–118110. 99 indexed citations

20.

Levina, Anna, et al.. (2005). Dynamical Synapses Give Rise to a Power-Law Distribution of Neuronal Avalanches. Edinburgh Research Explorer (University of Edinburgh). 18. 771–778. 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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