| Informed Machine Learning--A Taxonomy and Survey of Integrating Knowledge into Learning Systems L von Rueden, S Mayer, K Beckh, B Georgiev, S Giesselbach, R Heese, ... arXiv preprint arXiv:1903.12394, 2019 | 612* | 2019 |
| Optimal sequence memory in driven random networks J Schuecker, S Goedeke, M Helias arXiv preprint arXiv:1603.01880, 2016 | 101 | 2016 |
| Informed machine learning–towards a taxonomy of explicit integration of knowledge into machine learning L Von Rueden, S Mayer, J Garcke, C Bauckhage, J Schuecker learning 18, 19-20, 2019 | 70 | 2019 |
| Modulated escape from a metastable state driven by colored noise J Schuecker, M Diesmann, M Helias Physical Review E 92 (5), 052119, 2015 | 41 | 2015 |
| Nest 2.12. 0 S Kunkel, R Deepu, HE Plesser, B Golosio, ME Lepperød, JM Eppler, ... Jülich Supercomputing Center, 2017 | 40 | 2017 |
| Fundamental activity constraints lead to specific interpretations of the connectome J Schuecker, M Schmidt, SJ van Albada, M Diesmann, M Helias PLoS computational biology 13 (2), e1005179, 2017 | 37 | 2017 |
| Integration of continuous-time dynamics in a spiking neural network simulator J Hahne, D Dahmen, J Schuecker, A Frommer, M Bolten, M Helias, ... Frontiers in neuroinformatics 11, 34, 2017 | 31 | 2017 |
| NEST 2.14. 0 A Peyser, R Deepu, J Mitchell, S Appukuttan, T Schumann, JM Eppler, ... Jülich Supercomputing Center, 2017 | 26 | 2017 |
| Functional methods for disordered neural networks J Schücker, S Goedeke, D Dahmen, M Helias arXiv preprint arXiv:1605.06758, 2016 | 24 | 2016 |
| Leveraging domain knowledge for reinforcement learning using mmc architectures R Ramamurthy, C Bauckhage, R Sifa, J Schücker, S Wrobel International Conference on Artificial Neural Networks, 595-607, 2019 | 14 | 2019 |
| Noise dynamically suppresses chaos in neural networks S Goedeke, J Schuecker, M Helias arXiv preprint arXiv.1603.01880, 2016 | 10 | 2016 |
| Informed Machine Learning Through Functional Composition. C Bauckhage, C Ojeda, J Schücker, R Sifa, S Wrobel LWDA, 33-37, 2018 | 9 | 2018 |
| NEST 2.8. 0 JM Eppler, R Deepu, C Bachmann, T Zito, A Peyser, J Jordan, R Pauli, ... JARA-HPC, 2015 | 9 | 2015 |
| Conditions for traveling waves in spiking neural networks J Senk, K Korvasová, J Schuecker, E Hagen, T Tetzlaff, M Diesmann, ... arXiv preprint arXiv:1801.06046, 2018 | 8 | 2018 |
| Noise dynamically suppresses chaos in random neural networks S Goedeke, J Schuecker, M Helias arXiv preprint arXiv:1603.01880, 2016 | 5 | 2016 |
| Reduction of colored noise in excitable systems to white noise and dynamic boundary conditions J Schuecker, M Diesmann, M Helias arXiv preprint arXiv:1410.8799, 2014 | 3 | 2014 |
| Switching Dynamical Systems with Deep Neural Networks C Ojeda, B Georgiev, K Cvejoski, J Schucker, C Bauckhage, RJ Sánchez 2020 25th International Conference on Pattern Recognition (ICPR), 6305-6312, 2021 | 2 | 2021 |
| Adiabatic Quantum argmax Computation C Bauckhage, K Cvejoski, C Ojeda, J Schücker, R Sifa researchgate, Tech. Rep, 2018 | 1 | 2018 |
| The transfer function of the LIF model: A reduction from colored to white noise J Schücker, M Diesmann, M Helias Eleventh Goettingen Meeting of German Neuroscience Community, 2015 | 1 | 2015 |
| Spectral properties of excitable systems subject to colored noise J Schuecker, M Diesmann, M Helias arXiv preprint arXiv:1411.0432, 2014 | 1 | 2014 |
