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.
Integration of nanoscale memristor synapses in neuromorphic computing architectures
2013464 citationsRobert Legenstein et al.profile →
A solution to the learning dilemma for recurrent networks of spiking neurons
2020269 citationsGuillaume Bellec, Franz Scherr et al.Nature Communicationsprofile →
Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models
2023187 citationsOzan Özdenizci, Robert Legensteinprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Robert Legenstein
Since
Specialization
Citations
This map shows the geographic impact of Robert Legenstein'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 Robert Legenstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Legenstein more than expected).
Fields of papers citing papers by Robert Legenstein
This network shows the impact of papers produced by Robert Legenstein. 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 Robert Legenstein. The network helps show where Robert Legenstein may publish in the future.
Co-authorship network of co-authors of Robert Legenstein
This figure shows the co-authorship network connecting the top 25 collaborators of Robert Legenstein.
A scholar is included among the top collaborators of Robert Legenstein 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 Robert Legenstein. Robert Legenstein is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Özdenizci, Ozan & Robert Legenstein. (2021). Training Adversarially Robust Sparse Networks via Bayesian Connectivity Sampling. International Conference on Machine Learning. 8314–8324.7 indexed citations
Bellec, Guillaume, Franz Scherr, Anand Subramoney, et al.. (2020). A solution to the learning dilemma for recurrent networks of spiking neurons. Nature Communications. 11(1). 3625–3625.269 indexed citations breakdown →
Bellec, Guillaume, David Kappel, Wolfgang Maass, & Robert Legenstein. (2017). Deep Rewiring: Training very sparse deep networks. arXiv (Cornell University).17 indexed citations
Kappel, David, et al.. (2017). Reward-based stochastic self-configuration of neural circuits.. arXiv (Cornell University).7 indexed citations
12.
Legenstein, Robert, et al.. (2016). Assembly projections support the assignment of thematic roles to concepts in networks of spiking neurons. arXiv (Cornell University).2 indexed citations
13.
Kappel, David, Stefan Habenschuss, Robert Legenstein, & Wolfgang Maass. (2015). Synaptic sampling: a Bayesian approach to neural network plasticity and rewiring. neural information processing systems. 28. 370–378.6 indexed citations
Schrauwen, Benjamin, Lars Buesing, & Robert Legenstein. (2008). On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing. Ghent University Academic Bibliography (Ghent University). 21. 1425–1432.27 indexed citations
17.
Legenstein, Robert & Wolfgang Maass. (2005). A Criterion for the Convergence of Learning with Spike Timing Dependent Plasticity. Neural Information Processing Systems. 18. 763–770.2 indexed citations
18.
Maass, Wolfgang, Robert Legenstein, & Nils Bertschinger. (2004). Methods for Estimating the Computational Power and Generalization Capability of Neural Microcircuits. Neural Information Processing Systems. 17. 865–872.22 indexed citations
19.
Legenstein, Robert & Wolfgang Maass. (2001). Optimizing the Layout of a Balanced Tree. Electronic colloquium on computational complexity. 8.1 indexed citations
20.
Legenstein, Robert & Wolfgang Maass. (2000). Foundations for a Circuit Complexity Theory of Sensory Processing. Neural Information Processing Systems. 13. 259–265.4 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.