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.
Principles of connectivity among morphologically defined cell types in adult neocortex
2015552 citationsXiaolong Jiang, Shan Shen et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Fabian H. Sinz
Since
Specialization
Citations
This map shows the geographic impact of Fabian H. Sinz'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 Fabian H. Sinz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabian H. Sinz more than expected).
This network shows the impact of papers produced by Fabian H. Sinz. 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 Fabian H. Sinz. The network helps show where Fabian H. Sinz may publish in the future.
Co-authorship network of co-authors of Fabian H. Sinz
This figure shows the co-authorship network connecting the top 25 collaborators of Fabian H. Sinz.
A scholar is included among the top collaborators of Fabian H. Sinz 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 Fabian H. Sinz. Fabian H. Sinz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sinz, Fabian H., Xaq Pitkow, Jacob Reimer, Matthias Bethge, & Andreas S. Tolias. (2019). Engineering a Less Artificial Intelligence. Neuron. 103(6). 967–979.103 indexed citations
9.
Sinz, Fabian H., Alexander S. Ecker, Paul G. Fahey, et al.. (2018). Stimulus domain transfer in recurrent models for large scale cortical population prediction on video. Neural Information Processing Systems. 31. 7199–7210.2 indexed citations
Bethge, Matthias, Eero P. Simoncelli, & Fabian H. Sinz. (2009). Hierarchical Modeling of Local Image Features through L_p-Nested Symmetric Distributions. Neural Information Processing Systems. 22. 1696–1704.17 indexed citations
15.
Sinz, Fabian H. & Matthias Bethge. (2008). The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction. Max Planck Institute for Plasma Physics. 21. 1521–1528.10 indexed citations
Chapelle, Olivier, Alekh Agarwal, Fabian H. Sinz, & Bernhard Schölkopf. (2007). An Analysis of Inference with the Universum. Neural Information Processing Systems. 20. 1369–1376.66 indexed citations
18.
Collobert, Ronan, Fabian H. Sinz, Jason Weston, & Léon Bottou. (2006). Large Scale Transductive SVMs. Journal of Machine Learning Research. 7(62). 1687–1712.355 indexed citations
19.
Collobert, Ronan, Fabian H. Sinz, Jason Weston, & Léon Bottou. (2006). Trading convexity for scalability. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 201–208.254 indexed citations
20.
Weston, Jason, Ronan Collobert, Fabian H. Sinz, Léon Bottou, & Vladimir Vapnik. (2006). Inference with the Universum. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 1009–1016.141 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.