Henning Sprekeler

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	Toate	Din 2020
Referințe bibliografice	3595	1947
h-index	28	23
i10-index	42	34

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Wulfram GerstnerEPFLAdresă de e-mail confirmată pe epfl.ch
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität BochumAdresă de e-mail confirmată pe ini.rub.de
Tim P. VogelsInstitute of Science and Technology, AustriaAdresă de e-mail confirmată pe ist.ac.at
Friedemann ZenkeFriedrich Miescher Institute (FMI), University of BaselAdresă de e-mail confirmată pe fmi.ch
Prof. Claudia ClopathBioengineering, Imperial College LondonAdresă de e-mail confirmată pe imperial.ac.uk
Joram KeijserUniversity College LondonAdresă de e-mail confirmată pe ucl.ac.uk
Nicolas FrémauxPhD student, Brain Mind Institiute, Ecole Polytechnique de LausanneAdresă de e-mail confirmată pe epfl.ch
Laura Bella NaumannInstitute of Science and Technology, AustriaAdresă de e-mail confirmată pe ist.ac.at
Robert Tjarko LangeSakana AI, TU BerlinAdresă de e-mail confirmată pe tu-berlin.de
Loreen HertägTU BerlinAdresă de e-mail confirmată pe tu-berlin.de
Richard NaudDepartment of Cellular and Molecular Medicine, Department of Physics, University of OttawaAdresă de e-mail confirmată pe uottawa.ca
Robert FroemkeNYU School of MedicineAdresă de e-mail confirmată pe med.nyu.edu
M. Belén PardiInserm, Institute of Psychiatry and Neuroscience of ParisAdresă de e-mail confirmată pe inserm.fr
Tamas DalmayProfessor of RNA Biology, University of East AngliaAdresă de e-mail confirmată pe uea.ac.uk
Richard KempterInstitute for Theoretical BiologyAdresă de e-mail confirmată pe biologie.hu-berlin.de
Michael HerzogEPFL, SwitzerlandAdresă de e-mail confirmată pe epfl.ch
Mathias FranziusHonda Research Institute EuropeAdresă de e-mail confirmată pe honda-ri.de
Simon N WeberTechnische Universität BerlinAdresă de e-mail confirmată pe tu-berlin.de
Jean-Pascal PfisterProfessor of Theoretical Neuroscience, Department of Physiology, University of BernAdresă de e-mail confirmată pe unibe.ch
Simone Carlo SuracePostdoctoral fellow, Department of Physiology, University of Bern and Institute for NeuroinformaticsAdresă de e-mail confirmată pe unibe.ch

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Henning Sprekeler

Technische Universität Berlin

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Computational Neuroscience


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
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	836	2011
Slowness and sparseness lead to place, head-direction, and spatial-view cells M Franzius, H Sprekeler, L Wiskott PLoS Computational Biology 3 (8), e166, 2007	277	2007
Theory and Simulation in Neuroscience W Gerstner, H Sprekeler, G Deco Science 338 (6103), 60-65, 2012	228	2012
Reinforcement learning using a continuous time actor-critic framework with spiking neurons N Frémaux, H Sprekeler, W Gerstner PLoS computational biology 9 (4), e1003024, 2013	226	2013
Functional requirements for reward-modulated spike-timing-dependent plasticity N Frémaux, H Sprekeler, W Gerstner The Journal of Neuroscience 30 (40), 13326-13337, 2010	184	2010
Inhibitory synaptic plasticity: spike timing-dependence and putative network function TP Vogels, RC Froemke, N Doyon, M Gilson, JS Haas, R Liu, A Maffei, ... Frontiers in neural circuits 7, 119, 2013	169	2013
Sparse bursts optimize information transmission in a multiplexed neural code R Naud, H Sprekeler Proceedings of the National Academy of Sciences, 201720995, 2018	142	2018
Slowness: An objective for spike timing-dependent plasticity? H Sprekeler, C Michaelis, L Wiskott PLoS Computational Biology 3 (6), e112, 2007	103	2007
Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond H Sprekeler Current opinion in neurobiology 43, 198-203, 2017	94	2017
On the relation of slow feature analysis and laplacian eigenmaps H Sprekeler Neural computation 23, 303-335, 2011	90	2011
An extension of slow feature analysis for nonlinear blind source separation H Sprekeler, T Zito, L Wiskott The Journal of Machine Learning Research 15 (1), 921-947, 2014	89*	2014
A thalamocortical top-down circuit for associative memory MB Pardi, J Vogenstahl, T Dalmay, T Spanò, DL Pu, LB Naumann, ... Science 370 (6518), 844-848, 2020	84	2020
Predictive coding and the slowness principle: An information-theoretic approach F Creutzig, H Sprekeler Neural Computation 20 (4), 1026-1041, 2008	75	2008
Memory replay in balanced recurrent networks N Chenkov, H Sprekeler, R Kempter PLoS computational biology 13 (1), e1005359, 2017	72	2017
Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity SN Weber, H Sprekeler Elife 7, e34560, 2018	62	2018
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception A Kutschireiter, SC Surace, H Sprekeler, JP Pfister Scientific reports 7 (1), 8722, 2017	62	2017
Slow feature analysis L Wiskott Encyclopedia of Computational Neuroscience, 3142-3143, 2022	61	2022
Amplifying the redistribution of somato-dendritic inhibition by the interplay of three interneuron types L Hertäg, H Sprekeler PLoS computational biology 15 (5), e1006999, 2019	57	2019
Learning prediction error neurons in a canonical interneuron circuit L Hertäg, H Sprekeler eLife 9, e57541, 2020	55	2020
Inhibition as a binary switch for excitatory plasticity in pyramidal neurons KA Wilmes, H Sprekeler, S Schreiber PLoS computational biology 12 (3), e1004768, 2016	52	2016

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