Friedemann Zenke
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Continual learning through synaptic intelligence
F Zenke, B Poole, S Ganguli
International Conference on Machine Learning, 3987-3995, 2017
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
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
F Zenke, EJ Agnes, W Gerstner
Nature communications 6 (1), 1-13, 2015
Superspike: Supervised learning in multilayer spiking neural networks
F Zenke, S Ganguli
Neural computation 30 (6), 1514-1541, 2018
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
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
Hebbian plasticity requires compensatory processes on multiple timescales
F Zenke, W Gerstner
Philosophical Transactions of the Royal Society B 372 (1715), 20160259, 2017
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
The temporal paradox of Hebbian learning and homeostatic plasticity
F Zenke, W Gerstner, S Ganguli
Current opinion in neurobiology 43, 166-176, 2017
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
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
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
Limits to high-speed simulations of spiking neural networks using general-purpose computers
F Zenke, W Gerstner
Frontiers in neuroinformatics 8, 76, 2014
Synaptic consolidation: from synapses to behavioral modeling
L Ziegler, F Zenke, DB Kastner, W Gerstner
Journal of Neuroscience 35 (3), 1319-1334, 2015
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits
A Payeur, J Guerguiev, F Zenke, BA Richards, R Naud
Nature neuroscience, 1-10, 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
The PANDA GEM-based TPC prototype
L Fabbietti, H Angerer, R Arora, R Beck, M Berger, P Bühler, M Cargnelli, ...
Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2011
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, 2020
Experimental constraints on the ω–nucleus real potential
S Friedrich, K Makonyi, V Metag, D Bayadilov, B Bantes, R Beck, ...
Physics Letters B 736, 26-32, 2014
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