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Julian Göltz
Julian Göltz
PhD Student, Heidelberg University and University of Bern
Verified email at kip.uni-heidelberg.de - Homepage
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Cited by
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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
952021
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
672021
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
532020
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
192022
The yin-yang dataset
L Kriener, J Göltz, MA Petrovici
Proceedings of the 2022 Annual Neuro-Inspired Computational Elements …, 2022
172022
Training deep networks with time-to-first-spike coding on the brainscales wafer-scale system
J Göltz
Masterarbeit, Universität Heidelberg, April, 2019
52019
Fast and Energy-efficient deep Neuromorphic Learning
J Göltz, L Kriener, V Sabado, MA Petrovici
ERCIM News, 17, 2021
12021
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
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