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 | 92 | 2021 |
Visualizing a joint future of neuroscience and neuromorphic engineering F Zenke, SM Bohté, C Clopath, IM Comşa, J Göltz, W Maass, ... Neuron 109 (4), 571-575, 2021 | 65 | 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 | 51 | 2020 |
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 | 50* | 2019 |
A scalable approach to modeling on accelerated neuromorphic hardware E Müller, E Arnold, O Breitwieser, M Czierlinski, A Emmel, J Kaiser, ... Frontiers in neuroscience 16, 690, 2022 | 18 | 2022 |
The yin-yang dataset L Kriener, J Göltz, MA Petrovici Proceedings of the 2022 Annual Neuro-Inspired Computational Elements …, 2022 | 17 | 2022 |
Training deep networks with time-to-first-spike coding on the brainscales wafer-scale system J Göltz Masterarbeit, Universität Heidelberg, April, 2019 | 5 | 2019 |
Fast and Energy-efficient deep Neuromorphic Learning J Göltz, L Kriener, V Sabado, MA Petrovici ERCIM News, 17, 2021 | 1 | 2021 |
Gradient-based methods for spiking physical systems J Göltz, S Billaudelle, L Kriener, L Blessing, C Pehle, E Müller, ... arXiv preprint arXiv:2309.10823, 2023 | | 2023 |