David Kappel

971 total citations
21 papers, 433 citations indexed

About

David Kappel is a scholar working on Cognitive Neuroscience, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, David Kappel has authored 21 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Cognitive Neuroscience, 13 papers in Electrical and Electronic Engineering and 8 papers in Artificial Intelligence. Recurrent topics in David Kappel's work include Neural dynamics and brain function (13 papers), Advanced Memory and Neural Computing (12 papers) and Neural Networks and Applications (6 papers). David Kappel is often cited by papers focused on Neural dynamics and brain function (13 papers), Advanced Memory and Neural Computing (12 papers) and Neural Networks and Applications (6 papers). David Kappel collaborates with scholars based in Germany, Austria and United Kingdom. David Kappel's co-authors include Wolfgang Maass, Robert Legenstein, Stefan Habenschuss, Bernhard Nessler, Erika Covi, Hadi Heidari, Xiangpeng Liang, Wei Wang, Elisa Donati and Melika Payvand and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

David Kappel

19 papers receiving 423 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Kappel Germany	8	274	264	141	130	32	21	433
Aaron R. Voelker Canada	9	478 1.7×	324 1.2×	252 1.8×	140 1.1×	42 1.3×	18	642
De Ma China	14	405 1.5×	210 0.8×	197 1.4×	94 0.7×	36 1.1×	44	592
Massimiliano Versace United States	10	192 0.7×	255 1.0×	121 0.9×	127 1.0×	10 0.3×	24	502
Corey Lammie Australia	12	362 1.3×	142 0.5×	138 1.0×	113 0.9×	34 1.1×	31	511
Filip Ponulak Poland	4	316 1.2×	234 0.9×	172 1.2×	93 0.7×	16 0.5×	7	414
Charlotte Frenkel Belgium	10	375 1.4×	143 0.5×	133 0.9×	91 0.7×	75 2.3×	32	489
Morteza Hosseini United States	10	446 1.6×	170 0.6×	223 1.6×	144 1.1×	31 1.0×	19	607
Dejan Pecevski Austria	8	264 1.0×	307 1.2×	149 1.1×	128 1.0×	10 0.3×	9	419
Sumit Bam Shrestha Singapore	11	206 0.8×	160 0.6×	124 0.9×	56 0.4×	40 1.3×	16	325
Morteza Alamgir Germany	7	99 0.4×	342 1.3×	80 0.6×	122 0.9×	21 0.7×	10	481

Countries citing papers authored by David Kappel

Since Specialization

Citations

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

Fields of papers citing papers by David Kappel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Kappel

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

All Works

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20 of 20 papers shown

Subramoney, Anand, et al.. (2025). Early Prediction of Dynamic Sparsity in Large Language Models. 651–656.

Kappel, David & Sen Cheng. (2025). Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model. Frontiers in Computational Neuroscience. 18. 1462110–1462110. 1 indexed citations

Kappel, David & Christian Tetzlaff. (2024). Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics. PLoS Computational Biology. 20(11). e1012531–e1012531. 1 indexed citations

Mayr, Christian, et al.. (2024). Scalable Event-by-Event Processing of Neuromorphic Sensory Signals with Deep State-Space Models. 124–131. 6 indexed citations

Vogginger, Bernhard, et al.. (2024). Language Modeling on a SpiNNaker2 Neuromorphic Chip. 492–496. 4 indexed citations

Mikolajick, Thomas, et al.. (2024). Coincidence Detection with an Analog Spiking Neuron Exploiting Ferroelectric Polarization. University of Groningen research database (University of Groningen / Centre for Information Technology). 1–5.

Ricci, Saverio, David Kappel, Christian Tetzlaff, Daniele Ielmini, & Erika Covi. (2023). Tunable synaptic working memory with volatile memristive devices. SHILAP Revista de lepidopterología. 3(4). 44004–44004. 5 indexed citations

Kappel, David, et al.. (2023). CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning. Frontiers in Neuroinformatics. 17. 1134405–1134405. 5 indexed citations

Kappel, David, et al.. (2022). Differential Hebbian learning with time-continuous signals for active noise reduction. PLoS ONE. 17(5). e0266679–e0266679. 1 indexed citations

10.

Covi, Erika, Elisa Donati, Xiangpeng Liang, et al.. (2021). Adaptive Extreme Edge Computing for Wearable Devices. Frontiers in Neuroscience. 15. 611300–611300. 106 indexed citations

11.

Liu, Chen, Johannes Partzsch, David Kappel, et al.. (2020). Event-based Neural Network for ECG Classification with Delta Encoding and Early Stopping. GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen). 1–4. 6 indexed citations

12.

Kappel, David, et al.. (2018). A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning. eNeuro. 5(2). ENEURO.0301–17.2018. 37 indexed citations

13.

Liu, Chen, Guillaume Bellec, Bernhard Vogginger, et al.. (2018). Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype. Frontiers in Neuroscience. 12(4). 70–71. 32 indexed citations

14.

Bellec, Guillaume, David Kappel, Wolfgang Maass, & Robert Legenstein. (2017). Deep Rewiring: Training very sparse deep networks. arXiv (Cornell University). 17 indexed citations

15.

Kappel, David, et al.. (2017). Reward-based stochastic self-configuration of neural circuits.. arXiv (Cornell University). 7 indexed citations

16.

Rueckert, Elmar, et al.. (2016). Recurrent Spiking Networks Solve Planning Tasks. Scientific Reports. 6(1). 21142–21142. 42 indexed citations

17.

Kappel, David, Stefan Habenschuss, Robert Legenstein, & Wolfgang Maass. (2015). Synaptic sampling: a Bayesian approach to neural network plasticity and rewiring. neural information processing systems. 28. 370–378. 6 indexed citations

18.

Kappel, David, Stefan Habenschuss, Robert Legenstein, & Wolfgang Maass. (2015). Network Plasticity as Bayesian Inference. PLoS Computational Biology. 11(11). e1004485–e1004485. 73 indexed citations

19.

Kappel, David, Bernhard Nessler, & Wolfgang Maass. (2014). STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning. PLoS Computational Biology. 10(3). e1003511–e1003511. 70 indexed citations

20.

Pecevski, Dejan, et al.. (2014). NEVESIM: event-driven neural simulation framework with a Python interface. Frontiers in Neuroinformatics. 8. 70–70. 13 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.

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