Robert Legenstein

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Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Johannes BillHarvard Medical SchoolVerified email at hms.harvard.edu
Themis ProdromakisRegius Chair of Engineering, Centre for Electronics Frontiers, University of EdinburghVerified email at ed.ac.uk
Lars BuesingGoogle DeepMindVerified email at google.com
Christos H PAPADIMITRIOUColumbia UniversityVerified email at columbia.edu
Andreas TöschermindfexVerified email at mindfex.com
Dejan PecevskiAI/ML Department, Jumio Corp., Vienna, AustriaVerified email at jumio.com
Giacomo IndiveriInstitute of Neuroinformatics, University of Zurich and ETH ZurichVerified email at ini.uzh.ch
George DeligeorgisPrincipal Researcher, Institute of Electronic Structure and Laser ( IESL - FORTH )Verified email at iesl.forth.gr
Bernabe Linares-BarrancoInstituto de Microelectrónica de Sevilla, IMSE-CNM, Universidad de Sevilla, CSICVerified email at imse-cnm.csic.es
Benjamin SchrauwenProfessor of Machine Learning, Ghent UniversityVerified email at ugent.be
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität BochumVerified email at ini.rub.de
Gregor M. HörzerPhilosopher and former Computational Neuroscientist, University of OsnabrückVerified email at uos.de
Steven M. Chase, Ph.D.Carnegie Mellon UniversityVerified email at cmu.edu
Nils BertschingerFIASVerified email at fias.uni-frankfurt.de
Henry MarkramProfessor of Neuroscience, EPFLVerified email at epfl.ch
Thomas NatschlägerDynatrace, Lead Data ScientistVerified email at dynatrace.com
Arne BlackmanPfizerVerified email at pfizer.com
Per Jesper SjöströmProfessor of Neuroscience, McGill UniversityVerified email at mcgill.ca

Robert Legenstein

Institute for Theoretical Computer Science, Graz University of Technology

Verified email at igi.tugraz.at - Homepage

Computational Neuroscience


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Integration of nanoscale memristor synapses in neuromorphic computing architectures G Indiveri, B Linares-Barranco, R Legenstein, G Deligeorgis, ... Nanotechnology 24 (38), 384010, 2013	515	2013
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	428	2007
Combining predictions for accurate recommender systems M Jahrer, A Töscher, R Legenstein Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	334	2010
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapses A Serb, J Bill, A Khiat, R Berdan, R Legenstein, T Prodromakis Nature communications 7 (1), 1-9, 2016	286	2016
Long short-term memory and learning-to-learn in networks of spiking neurons G Bellec, D Salaj, A Subramoney, R Legenstein, W Maass Advances in neural information processing systems 31, 2018	281	2018
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback R Legenstein, D Pecevski, W Maass PLoS computational biology 4 (10), e1000180, 2008	275	2008
What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005	259	2005
A solution to the learning dilemma for recurrent networks of spiking neurons G Bellec, F Scherr, A Subramoney, E Hajek, D Salaj, R Legenstein, ... Nature communications 11 (1), 1-15, 2020	164	2020
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons L Büsing, B Schrauwen, R Legenstein Neural computation 22 (5), 1272-1311, 2010	161	2010
Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons L Büsing, B Schrauwen, R Legenstein Neural computation 22 (5), 1272-1311, 2010	161	2010
Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017	156	2017
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons R Legenstein, W Maass Journal of Neuroscience 31 (30), 10787-10802, 2011	148	2011
Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning GM Hoerzer, R Legenstein, W Maass Cerebral cortex 24 (3), 677-690, 2014	146	2014
A reward-modulated hebbian learning rule can explain experimentally observed network reorganization in a brain control task R Legenstein, SM Chase, AB Schwartz, W Maass Journal of Neuroscience 30 (25), 8400-8410, 2010	134	2010
What makes a dynamical system computationally powerful R Legenstein, W Maass New directions in statistical signal processing: From systems to brain, 127-154, 2007	134	2007
What makes a dynamical system computationally powerful R Legenstein, W Maass New directions in statistical signal processing: From systems to brain, 127-154, 2007	134	2007
Neuromorphic hardware in the loop: Training a deep spiking network on the brainscales wafer-scale system S Schmitt, J Klähn, G Bellec, A Grübl, M Guettler, A Hartel, S Hartmann, ... 2017 international joint conference on neural networks (IJCNN), 2227-2234, 2017	128	2017
A compound memristive synapse model for statistical learning through STDP in spiking neural networks J Bill, R Legenstein Frontiers in neuroscience 8, 412, 2014	108	2014
Reinforcement learning on slow features of high-dimensional input streams R Legenstein, N Wilbert, L Wiskott PLoS computational biology 6 (8), e1000894, 2010	107	2010
Network plasticity as Bayesian inference D Kappel, S Habenschuss, R Legenstein, W Maass PLoS computational biology 11 (11), e1004485, 2015	103	2015

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