Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Stable propagation of synchronous spiking in cortical neural networks
1999696 citationsMarkus Diesmann, Marc-Oliver Gewaltig et al.Natureprofile →
NEST (NEural Simulation Tool)
2007637 citationsMarc-Oliver Gewaltig, Markus Diesmannprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Marc-Oliver Gewaltig
Since
Specialization
Citations
This map shows the geographic impact of Marc-Oliver Gewaltig'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 Marc-Oliver Gewaltig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marc-Oliver Gewaltig more than expected).
Fields of papers citing papers by Marc-Oliver Gewaltig
This network shows the impact of papers produced by Marc-Oliver Gewaltig. 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 Marc-Oliver Gewaltig. The network helps show where Marc-Oliver Gewaltig may publish in the future.
Co-authorship network of co-authors of Marc-Oliver Gewaltig
This figure shows the co-authorship network connecting the top 25 collaborators of Marc-Oliver Gewaltig.
A scholar is included among the top collaborators of Marc-Oliver Gewaltig 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 Marc-Oliver Gewaltig. Marc-Oliver Gewaltig is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Senk, Johanna, Birgit Kriener, Hans Ekkehard Pleßer, et al.. (2019). Connectivity Concepts for Neuronal Networks. JuSER (Forschungszentrum Jülich).1 indexed citations
4.
Erö, Csaba, Marc-Oliver Gewaltig, Daniel Keller, & Henry Markram. (2018). A Cell Atlas for the Mouse Brain. Frontiers in Neuroinformatics. 12. 84–84.161 indexed citations
5.
Welter, Michael, et al.. (2017). HBP Neurorobotics Platform. mediaTUM (Technical University of Munich).4 indexed citations
6.
Röhrbein, Florian, Marc-Oliver Gewaltig, Cecilia Laschi, et al.. (2016). The Neurorobotics Platform of the Human Brain Project.. Cognitive Science.1 indexed citations
7.
Gewaltig, Marc-Oliver, et al.. (2016). The Neurorobotic Platform: A simulation environment for brain-inspired robotics. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–6.2 indexed citations
8.
Müller, Eilif, James A. Bednar, Markus Diesmann, et al.. (2015). Python in neuroscience. Frontiers in Neuroinformatics. 9. 11–11.46 indexed citations
Gewaltig, Marc-Oliver & Robert C. Cannon. (2012). Quality and sustainability of software tools in neuroscience. arXiv (Cornell University).2 indexed citations
Körner, Edgar, Marc-Oliver Gewaltig, Ursula Körner, Andreas Richter, & Tobias Rodemann. (1999). A model of computation in neocortical architecture. Neural Networks. 12(7-8). 989–1005.48 indexed citations
19.
Diesmann, Markus, Marc-Oliver Gewaltig, & Ad Aertsen. (1999). Stable propagation of synchronous spiking in cortical neural networks. Nature. 402(6761). 529–533.696 indexed citations breakdown →
20.
Diesmann, Markus, et al.. (1995). SYNOD: An Environment for Neural Systems Simulations Language Interface and Tutorial.5 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.