C. Bohm

8.4k citations
13 papers · 31 · h-index 4

Impact in

    • Neural dynamics and brain function
    • Functional Brain Connectivity Studies
    • Memory and Neural Mechanisms
    • Evolutionary Algorithms and Applications
    • Reinforcement Learning in Robotics

Papers in

    • Evolutionary Algorithms and Applications 4
    • Reinforcement Learning in Robotics 2
    • Neural Networks and Applications 1
    • Metaheuristic Optimization Algorithms Research 1
    • Modular Robots and Swarm Intelligence 2

C. Bohm

10 papers receiving 31 citations

Peers

C. Bohm
Comparison fields: 5 of 19
  • Cognitive Neuroscience 12
  • Artificial Intelligence 12
  • Genetics 7
  • Cultural Studies 2
  • Sociology and Political Science 9
Replace Milan Petrović with:
Milan Petrović Croatia
Jessica Leech United Kingdom
Iskra Herak Belgium
Martina Martinović Italy
Yanti Yanti Indonesia
Volker Struckmeier Germany
Utku Norman Switzerland
Elena Titov United Kingdom
Nathan Chi United States
Cole Nussbaumer Knaflic United States
C. Bohm relative to Milan Petrović Croatia Milan Petrović's profile →
Citations per field
00.5×
Milan Petrović · 1×
Citations per year

Countries citing papers authored by C. Bohm

Since Specialization
Citations

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

Fields of papers citing papers by C. Bohm

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. Bohm. 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 C. Bohm. The network helps show where C. Bohm may publish in the future.

Co-authors

The 9 scholars most cited alongside C. Bohm, 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 C. Bohm Line = papers co-authored together C. Bohm links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 201710
2 20224
3 20224
4 20224
5 20193
6 20212
7 20231
8 20211
9 20221
10 20191
11 20220
12 20190
13 20250

About C. Bohm

C. Bohm is a scholar working on Artificial Intelligence, Mechanical Engineering, Cellular and Molecular Neuroscience, Social Psychology and Sociology and Political Science, having authored 13 papers that have together received 31 indexed citations. Recurring topics across this work include Evolutionary Algorithms and Applications (4 papers), Reinforcement Learning in Robotics (2 papers), Modular Robots and Swarm Intelligence (2 papers), Neuroscience and Neural Engineering (1 paper), Wildlife Ecology and Conservation (1 paper), Neural dynamics and brain function (1 paper), Neural Networks and Applications (1 paper) and Metaheuristic Optimization Algorithms Research (1 paper). The work is most often cited by research in Cognitive Neuroscience (12 citations), Artificial Intelligence (12 citations), Genetics (7 citations), Cultural Studies (2 citations) and Sociology and Political Science (9 citations). C. Bohm has collaborated with scholars based in United States, United Kingdom and Sweden. Frequent co-authors include Arend Hintze, Douglas Kirkpatrick, Charles Ofria, Payam Aminpour, Christoph Adami, Alexander Lalejini, Kyle D. Perry, Emily Dolson and David Parsons. Their work appears in journals such as Artificial Life, Neural Computation, PLoS ONE, Entropy and HAL (Le Centre pour la Communication Scientifique Directe).

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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