Friedemann Zenke

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Wulfram GERSTNERProfessor of Computer Science, EPFL, Brain Mind Institute, Ecole Polytechnique Federale de LausanneVerified email at epfl.ch
Surya GanguliAssociate Professor, Stanford UniversityVerified email at stanford.edu
Ben PooleResearch Scientist at Google BrainVerified email at stanford.edu
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
Emre NeftciAssistant Professor, Department of Cognitive Sciences and Department of Computer Science, UC IrvineVerified email at uci.edu
Everton J. AgnesBiozentrum, University of BaselVerified email at unibas.ch
Hesham MostafaResearch scientist, Intel CorporationVerified email at intel.com
Guillaume HennequinThe University of Cambridge, UKVerified email at cam.ac.uk
Corette J. WierengaUtrecht UniversityVerified email at uu.nl
Melanie WoodinUniversity of TorontoVerified email at utoronto.ca
Robert FroemkeNYU School of MedicineVerified email at med.nyu.edu
Paul MillerBrandeis UniversityVerified email at brandeis.edu
Julie HaasLehigh UniversityVerified email at lehigh.edu
Robert C. LiuProfessor of Biology, Emory UniversityVerified email at emory.edu
Leandro Romero GasquesProfessor de Física, Universidade de São PauloVerified email at if.usp.br
Maurits EversThe Australian National UniversityVerified email at anu.edu.au
M. DasguptaAustralian National UniversityVerified email at anu.edu.au
Felipe GerhardPostdoctoral Research Associate, Brown Universitz, Providence, RIVerified email at brown.edu

Friedemann Zenke

Friedrich Miescher Institute for Biomedical Research

Verified email at fmi.ch - Homepage

neural networks computational neuroscience machine learning dynamical systems


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Continual learning through synaptic intelligence F Zenke, B Poole, S Ganguli International Conference on Machine Learning, 3987-3995, 2017	703	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	507	2011
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	215	2015
Superspike: Supervised learning in multilayer spiking neural networks F Zenke, S Ganguli Neural computation 30 (6), 1514-1541, 2018	187	2018
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019	184	2019
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	150	2019
Synaptic plasticity in neural networks needs homeostasis with a fast rate detector F Zenke, G Hennequin, W Gerstner PLoS Comput Biol 9 (11), e1003330, 2013	134	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	111	2013
Hebbian plasticity requires compensatory processes on multiple timescales F Zenke, W Gerstner Philosophical Transactions of the Royal Society B 372 (1715), 20160259, 2017	105	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	102	2008
The temporal paradox of Hebbian learning and homeostatic plasticity F Zenke, W Gerstner, S Ganguli Current opinion in neurobiology 43, 166-176, 2017	93	2017
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	89	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	88	2013
Limits to high-speed simulations of spiking neural networks using general-purpose computers F Zenke, W Gerstner Frontiers in neuroinformatics 8, 76, 2014	57	2014
Synaptic consolidation: from synapses to behavioral modeling L Ziegler, F Zenke, DB Kastner, W Gerstner Journal of Neuroscience 35 (3), 1319-1334, 2015	39	2015
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	30	2011
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits A Payeur, J Guerguiev, F Zenke, B Richards, R Naud bioRxiv, 2020	24	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	21	2014
Specific synaptic input strengths determine the computational properties of excitation–inhibition integration in a sound localization circuit E Gjoni, F Zenke, B Bouhours, R Schneggenburger The Journal of physiology 596 (20), 4945-4967, 2018	14	2018
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	13	2020

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