| Demonstrating advantages of neuromorphic computation: a pilot study T Wunderlich, AF Kungl, E Müller, A Hartel, Y Stradmann, SA Aamir, ... Frontiers in neuroscience 13, 260, 2019 | 141 | 2019 |
| 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 | 105 | 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 |
| Fast and deep neuromorphic learning with first-spike coding J Göltz, A Baumbach, S Billaudelle, AF Kungl, O Breitwieser, K Meier, ... Proceedings of the 2020 Annual Neuro-Inspired Computational Elements …, 2020 | 39 | 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 | 38 | 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 | 30 | 2019 |
| Fast and deep: Energy-efficient neuromorphic learning with first-spike times J Göltz, L Kriener, A Baumbach, S Billaudelle, O Breitwieser, B Cramer, ... arXiv preprint arXiv:1912.11443, 2019 | 13 | 2019 |
| 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 | 7 | 2023 |
| Lagrangian dynamics of dendritic microcircuits enables real-time backpropagation of errors D Dold, AF Kungl, J Sacramento, MA Petrovici, K Schindler, J Binas, ... Computational and Systems Neuroscience (Cosyne), 2019 | 7 | 2019 |
| Robust learning algorithms for spiking and rate-based neural networks AF Kungl Kirchhoff-Institute for Physics, Heidelberg Universität, 2020 | 6 | 2020 |
| Accelerated physical emulation of bayesian inference in spiking neural networks. Front Neurosci 13: 1201 AF Kungl, S Schmitt, J Klähn, P Müller, A Baumbach, D Dold, A Kugele, ... | 5 | 2019 |
| Demonstrating advantages of neuromorphic computation: a pilot study. Front Neurosci 13: 260 T Wunderlich, AF Kungl, E Müller, A Hartel, Y Stradmann, SA Aamir, ... | 5 | 2019 |
| Sampling with leaky integrate-and-fire neurons on the HICANNv4 neuromorphic chip AF Kungl Kirchhoff-Institute for Physics, Heidelberg University, 2016 | 5 | 2016 |
| Generative models on accelerated neuromorphic hardware AF Kungl, S Schmitt, J Klähn, P Müller, A Baumbach, D Dold, A Kugele, ... arXiv preprint arXiv:1807.02389, 2018 | 4 | 2018 |
| Magnetic phenomena in spiking neural networks A Baumbach, AF Kungl, MA Petrovici, J Schemmel, K Meier Spin 200, 100, 2016 | 4 | 2016 |
| Brain-inspired hardware for artificial intelligence: accelerated learning in a physical-model spiking neural network T Wunderlich, AF Kungl, E Müller, J Schemmel, M Petrovici Artificial Neural Networks and Machine Learning–ICANN 2019: Theoretical …, 2019 | 3 | 2019 |
| An oblate spheroidal model for multi-frequency acoustic back-scattering of frazil ice AF Kungl, D Schumayer, EK Frazer, PJ Langhorne, GH Leonard Cold Regions Science and Technology 177, 103122, 2020 | 2 | 2020 |
| Deep reinforcement learning in a time-continuous model AF Kungl, D Dold, O Riedler, W Senn, MA Petrovici Bernstein Conference, 2019 | 2 | 2019 |
| Deep reinforcement learning for time-continuous substrates AF Kungl, D Dold, O Riedler, W Senn, MA Petrovici training 101 (102), 103, 2020 | 1 | 2020 |
| Self-sustained probabilistic computing on spike-based neuromorphic systems AF Kungl, D Dold, Baumbach, O Andreas, Breitwieser, I Bytschok, A Grübl, ... Bernstein Conference, 2020 | | 2020 |
Mihai A. PetroviciGroup Leader, NeuroTMA Lab, University of Bern在 unibe.ch 的电子邮件经过验证
