Christian K. Machens

6.5k citations
50 papers · 3.5k indexed · 1 hit paper · h-index 27
Topics
Neural dynamics and brain function (45 papers)Visual perception and processing mechanisms (11 papers)Advanced Memory and Neural Computing (11 papers)

In The Last Decade

Christian K. Machens

48 papers receiving 3.4k citations

Hit Papers

Demixed principal component analysis of neural population...20162026201920222016100200300

Peers

Christian K. Machens
Comparison fields: 5 of 113
  • Cognitive Neuroscience 3.0k
  • Cellular and Molecular Neuroscience 1.3k
  • Electrical and Electronic Engineering 607
  • Artificial Intelligence 486
  • Statistical and Nonlinear Physics 322
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Citations per field
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Citations per year

Countries citing papers authored by Christian K. Machens

Since Specialization
Citations

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

Fields of papers citing papers by Christian K. Machens

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christian K. Machens. 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 Christian K. Machens. The network helps show where Christian K. Machens may publish in the future.

Co-authorship network of co-authors of Christian K. Machens

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 3
2 3
3 14
4 26
5
Understanding spiking networks through convex optimization
7
6
Demixed principal component analysis of neural population databreakdown →
311
7 273
8
Unsupervised learning of an efficient short-term memory network
9
9
Extracting Latent Structure From Multiple Interacting Neural Populations
18
10 15
11 148
12
Firing rate predictions in optimal balanced networks
10
13
Learning optimal spike-based representations
29
14 55
15
Demixed Principal Component Analysis
32
16 175
17 17
18 93
19 219
20 21

About Christian K. Machens

Christian K. Machens is a scholar working on Cognitive Neuroscience, Cellular and Molecular Neuroscience and Developmental Biology, having authored 50 papers that have together received 3.5k indexed citations. Recurring topics across this work include Neural dynamics and brain function (45 papers), Visual perception and processing mechanisms (11 papers) and Advanced Memory and Neural Computing (11 papers). The work is most often cited by research in Cognitive Neuroscience (3.0k citations), Developmental Biology (154 citations) and Cellular and Molecular Neuroscience (1.3k citations). Christian K. Machens has collaborated with scholars based in Portugal, United States and Germany. Frequent co-authors include Ranulfo Romo, Sophie Denève, Carlos D. Brody, Andreas V. M. Herz, Tim Gollisch, Dieter Jaeger, Michael Wehr, Anthony M. Zador, Alfonso Renart and João D. Semedo. Their work appears in journals such as Nature, Science and Cell.

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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2026