| 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 | 112* | 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 | 55 | 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 | 45* | 2019 |
| 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 | 34* | 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 | 24 | 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, ... BioRxiv, 2023.03. 25.534198, 2023 | 14* | 2023 |
| Selected Trends in Artificial Intelligence for Space Applications D Izzo, G Meoni, P Gómez, D Dold, A Zoechbauer arXiv preprint arXiv:2212.06662, 2022 | 11 | 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 | 9 | 2021 |
| Spike: Spike-based embeddings for multi-relational graph data D Dold, JS Garrido 2021 International Joint Conference on Neural Networks (IJCNN), 1-8, 2021 | 8 | 2021 |
| Differentiable graph-structured models for inverse design of lattice materials D Dold, DA van Egmond Cell Reports Physical Science 4 (10), 2023 | 7 | 2023 |
| Neuromorphic Computing and Sensing in Space D Izzo, A Hadjiivanov, D Dold, G Meoni, E Blazquez arXiv preprint arXiv:2212.05236, 2022 | 6 | 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 | 6 | 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 | 5 | 2022 |
| Relational representation learning with spike trains D Dold 2022 International Joint Conference on Neural Networks (IJCNN), 2022 | 4 | 2022 |
| Deep reinforcement learning in a time-continuous model AF Kungl, D Dold, O Riedler, W Senn, MA Petrovici Bernstein Conference, 2019 | 3* | 2019 |
| 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 | 2 | 2022 |
| Harnessing function from form: towards bio-inspired artificial intelligence in neuronal substrates D Dold | 2 | 2020 |
| Advance Concepts Team D Izzo European Space Agency, 2004 | 2 | 2004 |
| Industrial device and method for building and/or processing a knowledge graph JS GARRIDO, D Dold US Patent App. 17/563,480, 2022 | 1 | 2022 |
| Neuromorphic hardware for processing a knowledge graph represented by observed triple statements and method for training a learning component JS GARRIDO, D Dold US Patent App. 17/555,577, 2022 | 1 | 2022 |
