Thomas Pfeil

1.3k citations
9 papers · 751 · 1 hit paper · h-index 6

Impact in

Papers in

Thomas Pfeil

9 papers receiving 735 citations

Thomas Pfeil's Hit Papers

Deep Learning With Spiking Neurons: Opportunities and Challenges 2018 · 424 citations
4240+2+5Years since publication100200300400

Peers

Thomas Pfeil
Comparison fields: 5 of 70
  • Cognitive Neuroscience 394
  • Cellular and Molecular Neuroscience 222
  • Electrical and Electronic Engineering 651
  • Artificial Intelligence 254
  • Sensory Systems 16
Replace Mihai A. Petrovici with:
Mihai A. Petrovici Switzerland
Peiran Gao United States
Rufin Van Rullen France
Jochen Martin Eppler Germany
Tobias C. Potjans Japan
Kristofor D. Carlson United States
Vittorio Dante Italy
Rudy Guyonneau France
Filip Ponulak Poland
Thomas Pfeil relative to Mihai A. Petrovici Switzerland Mihai A. Petrovici's profile →
Citations per field
00.5×
Mihai A. Petrovici · 1×
Citations per year

Countries citing papers authored by Thomas Pfeil

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Pfeil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 19 scholars most cited alongside Thomas Pfeil, 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 Thomas Pfeil Line = papers co-authored together Thomas Pfeil links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1
Deep Learning With Spiking Neurons: Opportunities and Challenges
Hit paper breakdown →
2018424
2 2013110
3 202073
4 201468
5 201266
6 20207
7 20201
8 20231
9 20131

About Thomas Pfeil

Thomas Pfeil is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Cognitive Neuroscience, Cellular and Molecular Neuroscience and Civil and Structural Engineering, having authored 9 papers that have together received 751 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (7 papers), Neural dynamics and brain function (5 papers), Neural Networks and Reservoir Computing (3 papers), Ferroelectric and Negative Capacitance Devices (3 papers), Machine Learning and ELM (1 paper), Photovoltaic System Optimization Techniques (1 paper), Solar Thermal and Photovoltaic Systems (1 paper) and Neuroscience and Neural Engineering (1 paper). The work is most often cited by research in Cognitive Neuroscience (394 citations), Cellular and Molecular Neuroscience (222 citations), Electrical and Electronic Engineering (651 citations), Artificial Intelligence (254 citations) and Sensory Systems (16 citations). Thomas Pfeil has collaborated with scholars based in Germany, Netherlands and China. Frequent co-authors include Michael Pfeiffer, Michael Schmuker, Martin Paul Nawrot, Karlheinz Meier, Johannes Schemmel, Elisabetta Chicca, Paul Müller, Mihai A. Petrovici, Andreas Grübl and Eric Müller. Their work appears in journals such as Frontiers in Neuroscience, BMC Neuroscience, Proceedings of the National Academy of Sciences and University of Groningen research database (University of Groningen / Centre for Information Technology).

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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