J. Michael Herrmann

1.9k citations
68 papers · 1.2k · h-index 15

Impact in

Papers in

    • Neural dynamics and brain function 32
    • Neural Networks and Applications 14
    • Evolutionary Algorithms and Applications 8
    • Reinforcement Learning in Robotics 6
    • Metaheuristic Optimization Algorithms Research 5

J. Michael Herrmann

66 papers receiving 1.2k citations

Peers

J. Michael Herrmann
Comparison fields: 5 of 117
  • Cognitive Neuroscience 735
  • Statistical and Nonlinear Physics 328
  • Cellular and Molecular Neuroscience 149
  • Artificial Intelligence 243
  • Computer Networks and Communications 121
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Citations per field
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Citations per year

Countries citing papers authored by J. Michael Herrmann

Since Specialization
Citations

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

Fields of papers citing papers by J. Michael Herrmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Michael Herrmann. 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 J. Michael Herrmann. The network helps show where J. Michael Herrmann may publish in the future.

Co-authors

The 25 scholars most cited alongside J. Michael Herrmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with J. Michael Herrmann Line = papers co-authored together J. Michael Herrmann links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007372
2 2009102
3 201883
4 200279
5 201775
6 200247
7 200828
8 201025
9 201220
10 201316
11 201716
12 200616
13 200515
14 201114
15 201214
16 201013
17 201713
18 200013
19 200713
20 200012

About J. Michael Herrmann

J. Michael Herrmann is a scholar working on Cognitive Neuroscience, Artificial Intelligence, Statistical and Nonlinear Physics, Electrical and Electronic Engineering and Computer Networks and Communications, having authored 68 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural dynamics and brain function (32 papers), Neural Networks and Applications (14 papers), Evolutionary Algorithms and Applications (8 papers), stochastic dynamics and bifurcation (7 papers), Advanced Memory and Neural Computing (6 papers), Reinforcement Learning in Robotics (6 papers), Nonlinear Dynamics and Pattern Formation (5 papers) and Metaheuristic Optimization Algorithms Research (5 papers). The work is most often cited by research in Cognitive Neuroscience (735 citations), Statistical and Nonlinear Physics (328 citations), Cellular and Molecular Neuroscience (149 citations), Artificial Intelligence (243 citations) and Computer Networks and Communications (121 citations). J. Michael Herrmann has collaborated with scholars based in United Kingdom, Germany and China. Frequent co-authors include T. Geisel, Anna Levina, T. A. Joyce, Udo Ernst, Gbenga Ibikunle, Silke Dodel, Christian W. Eurich, Joachim Haß, Jörg Behrendt and Marcus Hasselhorn. Their work appears in journals such as Neurocomputing, BMC Neuroscience, PLoS ONE, Journal of Computational Neuroscience and Neural Computation.

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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