| Continual learning through synaptic intelligence F Zenke, B Poole, S Ganguli International Conference on Machine Learning, 3987-3995, 2017 | 2629 | 2017 |
| Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks EO Neftci, H Mostafa, F Zenke IEEE Signal Processing Magazine 36 (6), 51-63, 2019 | 1143 | 2019 |
| A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019 | 832 | 2019 |
| Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks TP Vogels, H Sprekeler, F Zenke, C Clopath, W Gerstner Science 334 (6062), 1569-1573, 2011 | 753 | 2011 |
| Superspike: Supervised learning in multilayer spiking neural networks F Zenke, S Ganguli Neural computation 30 (6), 1514-1541, 2018 | 591 | 2018 |
| Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks F Zenke, EJ Agnes, W Gerstner Nature communications 6 (1), 6922, 2015 | 356 | 2015 |
| Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits A Payeur, J Guerguiev, F Zenke, BA Richards, R Naud Nature neuroscience 24 (7), 1010-1019, 2021 | 234 | 2021 |
| The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks F Zenke, TP Vogels Neural computation 33 (4), 899-925, 2021 | 229 | 2021 |
| The temporal paradox of Hebbian learning and homeostatic plasticity F Zenke, W Gerstner, S Ganguli Current opinion in neurobiology 43, 166-176, 2017 | 199 | 2017 |
| Hebbian plasticity requires compensatory processes on multiple timescales F Zenke, W Gerstner Philosophical Transactions of the Royal Society B 372 (1715), 20160259, 2017 | 192 | 2017 |
| The heidelberg spiking data sets for the systematic evaluation of spiking neural networks B Cramer, Y Stradmann, J Schemmel, F Zenke IEEE Transactions on Neural Networks and Learning Systems 33 (7), 2744-2757, 2020 | 188 | 2020 |
| Synaptic plasticity in neural networks needs homeostasis with a fast rate detector F Zenke, G Hennequin, W Gerstner PLoS computational biology 9 (11), e1003330, 2013 | 175 | 2013 |
| Inhibitory Synaptic Plasticity-Spike timing dependence and putative network function. H Sprekeler, TP Vogels, RC Froemke, N Doyon, M Gilson, JS Haas, R Liu, ... Frontiers in Neural Circuits 7, 2013 | 158 | 2013 |
| Entrance channel dependence of quasifission in reactions forming RG Thomas, DJ Hinde, D Duniec, F Zenke, M Dasgupta, ML Brown, ... Physical Review C 77 (3), 034610, 2008 | 123 | 2008 |
| Inference of neuronal network spike dynamics and topology from calcium imaging data H Lütcke, F Gerhard, F Zenke, W Gerstner, F Helmchen Frontiers in neural circuits 7, 201, 2013 | 110 | 2013 |
| Surrogate gradients for analog neuromorphic computing B Cramer, S Billaudelle, S Kanya, A Leibfried, A Grübl, V Karasenko, ... Proceedings of the National Academy of Sciences 119 (4), e2109194119, 2022 | 109* | 2022 |
| Determination of the η′-nucleus optical potential M Nanova, V Metag, EY Paryev, D Bayadilov, B Bantes, R Beck, ... Physics Letters B 727 (4-5), 417-423, 2013 | 98 | 2013 |
| Limits to high-speed simulations of spiking neural networks using general-purpose computers F Zenke, W Gerstner Frontiers in neuroinformatics 8, 76, 2014 | 95 | 2014 |
| Brain-inspired learning on neuromorphic substrates F Zenke, EO Neftci Proceedings of the IEEE 109 (5), 935-950, 2021 | 89 | 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 | 72 | 2021 |
Wulfram GerstnerEPFL在 epfl.ch 的电子邮件经过验证
