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.
Painfree and accurate Bayesian estimation of psychometric functions for (potentially) overdispersed data
Countries citing papers authored by Jakob H. Macke
Since
Specialization
Citations
This map shows the geographic impact of Jakob H. Macke'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 Jakob H. Macke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakob H. Macke more than expected).
This network shows the impact of papers produced by Jakob H. Macke. 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 Jakob H. Macke. The network helps show where Jakob H. Macke may publish in the future.
Co-authorship network of co-authors of Jakob H. Macke
This figure shows the co-authorship network connecting the top 25 collaborators of Jakob H. Macke.
A scholar is included among the top collaborators of Jakob H. Macke 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 Jakob H. Macke. Jakob H. Macke is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Macke, Jakob H., et al.. (2020). Long timescale dynamics in freely behaving rats. Bulletin of the American Physical Society.1 indexed citations
8.
Ansuini, Alessio, Alessandro Laio, Jakob H. Macke, & Davide Zoccolan. (2019). Intrinsic dimension of data representations in deep neural networks. arXiv (Cornell University). 32. 6109–6119.9 indexed citations
9.
Schneider, Steffen, Alexander S. Ecker, Jakob H. Macke, & Matthias Bethge. (2018). Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains. Neural Information Processing Systems.5 indexed citations
10.
Lueckmann, Jan-Matthis, Pedro J. Gonçalves, Giacomo Bassetto, et al.. (2017). Flexible statistical inference for mechanistic models of neural dynamics. Lirias (KU Leuven). 30. 1289–1299.29 indexed citations
Nonnenmacher, Marcel, et al.. (2017). Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations. Max Planck Digital Library. 30. 5702–5712.4 indexed citations
13.
Speiser, Artur, Jinyao Yan, Evan Archer, et al.. (2017). Fast amortized inference of neural activity from calcium imaging data with variational autoencoders. MPG.PuRe (Max Planck Society). 30. 4024–4034.6 indexed citations
14.
Park, Mijung, Gergő Bohner, & Jakob H. Macke. (2015). Unlocking neural population non-stationarity using a hierarchical dynamics model. UCL Discovery (University College London). 145–153.1 indexed citations
15.
Bassetto, Giacomo, et al.. (2014). A Bayesian model for identifying hierarchically organised states in neural population activity. Max Planck Digital Library. 27. 3095–3103.3 indexed citations
16.
Buesing, Lars, et al.. (2013). Inferring neural population dynamics from multiple partial recordings of the same neural circuit. Max Planck Digital Library. 26. 539–547.15 indexed citations
17.
Macke, Jakob H., Lars Buesing, John P. Cunningham, et al.. (2011). Empirical models of spiking in neural populations. MPG.PuRe (Max Planck Society). 24. 1350–1358.90 indexed citations
Zeck, Günther, Matthias Bethge, & Jakob H. Macke. (2007). Receptive Fields without Spike-Triggering. Neural Information Processing Systems. 20. 969–976.6 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.