| Fast and energy-efficient neuromorphic deep learning with first-spike times J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, ... Nature machine intelligence 3 (9), 823-835, 2021 | 200* | 2021 |
| Versatile emulation of spiking neural networks on an accelerated neuromorphic substrate S Billaudelle, Y Stradmann, K Schreiber, B Cramer, A Baumbach, D Dold, ... 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 1-5, 2020 | 67 | 2020 |
| Accelerated physical emulation of bayesian inference in spiking neural networks AF Kungl, S Schmitt, J Klähn, P Müller, A Baumbach, D Dold, A Kugele, ... Frontiers in neuroscience 13, 1201, 2019 | 51* | 2019 |
| Machine learning on knowledge graphs for context-aware security monitoring JS Garrido, D Dold, J Frank 2021 IEEE International Conference on Cyber Security and Resilience (CSR), 55-60, 2021 | 42 | 2021 |
| A neuronal least-action principle for real-time learning in cortical circuits W Senn, D Dold, AF Kungl, B Ellenberger, J Jordan, Y Bengio, ... ELife 12, RP89674, 2024 | 41* | 2024 |
| Stochasticity from function—why the bayesian brain may need no noise D Dold, I Bytschok, AF Kungl, A Baumbach, O Breitwieser, W Senn, ... Neural networks 119, 200-213, 2019 | 38* | 2019 |
| Differentiable graph-structured models for inverse design of lattice materials D Dold, DA van Egmond Cell Reports Physical Science 4 (10), 2023 | 30 | 2023 |
| Neuromorphic Computing and Sensing in Space D Izzo, A Hadjiivanov, D Dold, G Meoni, E Blazquez arXiv preprint arXiv:2212.05236, 2022 | 25 | 2022 |
| Selected Trends in Artificial Intelligence for Space Applications D Izzo, G Meoni, P Gómez, D Dold, A Zoechbauer arXiv preprint arXiv:2212.06662, 2022 | 24 | 2022 |
| Learning through structure: towards deep neuromorphic knowledge graph embeddings VC Chian, M Hildebrandt, T Runkler, D Dold 2021 International Conference on Neuromorphic Computing (ICNC), 61-70, 2021 | 17 | 2021 |
| Detection, explanation and filtering of cyber attacks combining symbolic and sub-symbolic methods A Himmelhuber, D Dold, S Grimm, S Zillner, T Runkler 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 381-388, 2022 | 14 | 2022 |
| An energy-based model for neuro-symbolic reasoning on knowledge graphs D Dold, JS Garrido 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | 13 | 2021 |
| Neuro-symbolic computing with spiking neural networks D Dold, J Soler Garrido, V Caceres Chian, M Hildebrandt, T Runkler Proceedings of the International Conference on Neuromorphic Systems 2022, 1-4, 2022 | 12 | 2022 |
| Spike: Spike-based embeddings for multi-relational graph data D Dold, JS Garrido 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 12 | 2021 |
| Artificial Intelligence for Space: AI4SPACE: Trends, Applications, and Perspectives M Madi, O Sokolova CRC Press, 2023 | 6 | 2023 |
| Evaluating the feasibility of interpretable machine learning for globular cluster detection D Dold, K Fahrion Astronomy & Astrophysics (A&A) 663, A81, 2022 | 6 | 2022 |
| Stable learning using spiking neural networks equipped with affine encoders and decoders AM Neuman, D Dold, PC Petersen Journal of Machine Learning Research 26 (246), 1-49, 2025 | 5 | 2025 |
| Relational representation learning with spike trains D Dold 2022 International Joint Conference on Neural Networks (IJCNN), 2022 | 5 | 2022 |
| Investigation of low-energy spiking neural networks based on temporal coding for scene classification P Lunghi, S Silvestrini, G Meoni, D Dold, A Hadjiivanov, D Izzo 75th International Astronautical Congress (IAC 2024), 1-13, 2024 | 4 | 2024 |
| Causal pieces: analysing and improving spiking neural networks piece by piece D Dold, PC Petersen arXiv preprint arXiv:2504.14015, 2025 | 3 | 2025 |
