Friedemann Zenke

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Wulfram GerstnerEPFLVerified email at epfl.ch
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
Ben PooleResearch Scientist at Google DeepMindVerified email at stanford.edu
Emre NeftciInstitute Director, Forschungszentrum Jülich; Professor, RWTH AachenVerified email at fz-juelich.de
Tim P. VogelsInstitute of Science and Technology, AustriaVerified email at ist.ac.at
Henning SprekelerTechnische Universität BerlinVerified email at tu-berlin.de
Prof. Claudia ClopathBioengineering, Imperial College LondonVerified email at imperial.ac.uk
Johannes SchemmelHeidelberg UniversityVerified email at kip.uni-heidelberg.de
Everton J. AgnesBiozentrum, University of BaselVerified email at unibas.ch
Yannik StradmannKirchhoff-Institute for Physics, Heidelberg UniversityVerified email at kip.uni-heidelberg.de
Richard NaudDepartment of Cellular and Molecular Medicine, Department of Physics, University of OttawaVerified email at uottawa.ca
Hesham MostafaResearch scientist, Intel CorporationVerified email at intel.com
Axel LaborieuxPostdoctoral researcher, Friedrich Miescher Institute for Biomedical ResearchVerified email at fmi.ch
Alexandre PayeurResearch associate at Université de MontréalVerified email at umontreal.ca
Jordan GuerguievUniversity of Toronto ScarboroughVerified email at mail.utoronto.ca
Blake RichardsMila + McGill UniversityVerified email at mila.quebec
Manu Srinath HalvagalFriedrich Miescher Institute for Biomedical ResearchVerified email at fmi.ch
Korbinian SchreiberHeidelberg UniversityVerified email at kip.uni-heidelberg.de
Johannes WeisScientific staff, M.Sc. Physics, Heidelberg UniversityVerified email at kip.uni-heidelberg.de
Sebastian BillaudelleVerified email at kip.uni-heidelberg.de

Friedemann Zenke

Friedrich Miescher Institute (FMI), University of Basel

Verified email at fmi.ch - Homepage

computational neuroscience learning neuroAI spiking neural networks neuromorphic computing


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Continual learning through synaptic intelligence F Zenke, B Poole, S Ganguli International Conference on Machine Learning, 3987-3995, 2017	2817	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	1297	2019
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019	905	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	792	2011
Superspike: Supervised learning in multilayer spiking neural networks F Zenke, S Ganguli Neural computation 30 (6), 1514-1541, 2018	644	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	374	2015
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	264	2021
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	259	2021
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	219	2020
The temporal paradox of Hebbian learning and homeostatic plasticity F Zenke, W Gerstner, S Ganguli Current opinion in neurobiology 43, 166-176, 2017	212	2017
Hebbian plasticity requires compensatory processes on multiple timescales F Zenke, W Gerstner Philosophical Transactions of the Royal Society B 372 (1715), 20160259, 2017	203	2017
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	182	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	167	2013
Entrance channel dependence of quasifission in reactions forming RG Thomas, DJ Hinde, D Duniec, F Zenke, M Dasgupta, ML Brown, ... Physical Review C—Nuclear Physics 77 (3), 034610, 2008	134	2008
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	122*	2022
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	113	2013
Brain-inspired learning on neuromorphic substrates F Zenke, EO Neftci Proceedings of the IEEE 109 (5), 935-950, 2021	97	2021
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	97	2013
Limits to high-speed simulations of spiking neural networks using general-purpose computers F Zenke, W Gerstner Frontiers in neuroinformatics 8, 76, 2014	94	2014
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	81	2021

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