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Akos F. Kungl
Akos F. Kungl
Carl Zeiss SMT
Verified email at zeiss.com
Title
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Cited by
Year
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
1362019
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
942021
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 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
392020
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
362019
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
282019
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
122019
Lagrangian dynamics of dendritic microcircuits enables real-time backpropagation of errors
D Dold, AF Kungl, J Sacramento, MA Petrovici, K Schindler, J Binas, ...
target 100 (1), 2, 2019
72019
Sampling with leaky integrate-and-fire neurons on the HICANNv4 neuromorphic chip
AF Kungl
Kirchhoff-Institute for Physics, Heidelberg University, 2016
62016
Robust learning algorithms for spiking and rate-based neural networks
AF Kungl
Kirchhoff-Institute for Physics, Heidelberg Universität, 2020
52020
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
52019
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
42018
Magnetic phenomena in spiking neural networks
A Baumbach, AF Kungl, MA Petrovici, J Schemmel, K Meier
Spin 200, 100, 2016
42016
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
32023
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
22020
Deep reinforcement learning in a time-continuous model
AF Kungl, D Dold, O Riedler, W Senn, MA Petrovici
Bernstein Conference, 2019
22019
Deep reinforcement learning for time-continuous substrates
AF Kungl, D Dold, O Riedler, W Senn, MA Petrovici
training 101 (102), 103, 2020
12020
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
Demonstrating advantages of neuromorphic computation
T Wunderlich, Á Kungl, E Müller, A Hartel, Y Stradmann, SA Aamir, ...
2019
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Articles 1–19