Friedemann Zenke - Google Scholar Citations

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Co-authors

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
Henning SprekelerTechnische Universität BerlinVerified email at tu-berlin.de
Tim P. VogelsWellcome Trust Senior Research Fellow & Associate Professor, Centre for Neural Circuits andVerified email at cncb.ox.ac.uk
Prof. Claudia ClopathBioengineering, Imperial College LondonVerified email at imperial.ac.uk
Everton J. AgnesUniversity of OxfordVerified email at cncb.ox.ac.uk
Guillaume HennequinThe University of Cambridge, UKVerified email at cam.ac.uk
Corette J. WierengaUtrecht UniversityVerified email at uu.nl
Leandro Romero GasquesProfessor de Física, Universidade de São PauloVerified email at if.usp.br
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
Maurits EversThe Australian National UniversityVerified email at anu.edu.au
Robert C. LiuProfessor of Biology, Emory UniversityVerified email at emory.edu
M. DasguptaAustralian National UniversityVerified email at anu.edu.au
Felipe GerhardPostdoctoral Research Associate, Brown Universitz, Providence, RIVerified email at brown.edu
Fritjof HelmchenProfessor of NeuroscienceVerified email at hifo.uzh.ch
Emre NeftciAssistant Professor, Department of Cognitive Sciences and Department of Computer Science, UC IrvineVerified email at uci.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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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	430	2011
Continual learning through synaptic intelligence F Zenke, B Poole, S Ganguli International Conference on Machine Learning, 3987-3995, 2017	319	2017
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	174	2015
Synaptic plasticity in neural networks needs homeostasis with a fast rate detector F Zenke, G Hennequin, W Gerstner PLoS computational biology 9 (11), 2013	109	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	98	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	98	2008
Superspike: Supervised learning in multilayer spiking neural networks F Zenke, S Ganguli Neural computation 30 (6), 1514-1541, 2018	89	2018
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	81	2013
Hebbian plasticity requires compensatory processes on multiple timescales F Zenke, W Gerstner Philosophical Transactions of the Royal Society B 372 (1715), 20160259, 2017	73	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	71	2013
The temporal paradox of Hebbian learning and homeostatic plasticity F Zenke, W Gerstner, S Ganguli Current opinion in neurobiology 43, 166-176, 2017	54	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	49	2019
Limits to high-speed simulations of spiking neural networks using general-purpose computers F Zenke, W Gerstner Frontiers in neuroinformatics 8, 76, 2014	47	2014
A deep learning framework for neuroscience BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... Nature neuroscience 22 (11), 1761-1770, 2019	43	2019
Synaptic consolidation: from synapses to behavioral modeling L Ziegler, F Zenke, DB Kastner, W Gerstner Journal of Neuroscience 35 (3), 1319-1334, 2015	33	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	27	2011
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	20	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	10	2018
Emergent neural computation from the interaction of different forms of plasticity M Gilson, C Savin, F Zenke Frontiers in computational neuroscience 9, 145, 2015	10*	2015
Cooperation across timescales between Hebbian and homeostatic plasticity F Zenke, W Gerstner Philos Trans R Soc London, Ser B Biol Sci, 2016	4	2016

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