Robert Legenstein

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	All	Since 2015
Citations	3744	2573
h-index	27	24
i10-index	43	36

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Co-authors

Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Johannes BillHarvard Medical SchoolVerified email at hms.harvard.edu
Lars BuesingGoogle DeepMindVerified email at google.com
Themis ProdromakisProfessor of Nanotechnology, University of SouthamptonVerified email at soton.ac.uk
Andreas Töschercommendo research & consultingVerified email at mindfex.com
Benjamin SchrauwenProfessor of Machine Learning, Ghent UniversityVerified email at ugent.be
Christos H PAPADIMITRIOUColumbia UniversityVerified email at columbia.edu
Dejan PecevskiInstitute for Theoretical Computer Science, Graz University of Technology, AustriaVerified email at igi.tugraz.at
Gregor M. HörzerPhilosopher and Computational Neuroscientist, University of OsnabrückVerified email at uos.de
Giacomo IndiveriInstitute of Neuroinformatics, University of Zurich and ETH ZurichVerified email at ini.uzh.ch
George DeligeorgisResearcher at FORTH - IESL, Microelectronics Research Group, GreeceVerified email at physics.uoc.gr
Bernabe Linares-BarrancoInstituto de Microelectrónica de Sevilla, IMSE-CNM, Universidad de Sevilla, CSICVerified email at imse-cnm.csic.es
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität BochumVerified email at ini.rub.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 BlackmanGovernment Science and EngineeringVerified email at go-science.gsi.gov.uk
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	413	2013
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	350	2007
Combining predictions for accurate recommender systems M Jahrer, A Töscher, R Legenstein Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	306	2010
What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005	244	2005
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback R Legenstein, D Pecevski, W Maass PLoS Comput Biol 4 (10), e1000180, 2008	237	2008
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	210	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, 787-797, 2018	132	2018
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	132	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	132	2010
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons R Legenstein, W Maass Journal of Neuroscience 31 (30), 10787-10802, 2011	122	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	121	2014
What makes a dynamical system computationally powerful R Legenstein, W Maass New directions in statistical signal processing: From systems to brain, 127-154, 2007	118	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	118	2007
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	115	2010
Reinforcement learning on slow features of high-dimensional input streams R Legenstein, N Wilbert, L Wiskott PLoS Comput Biol 6 (8), e1000894, 2010	97	2010
A compound memristive synapse model for statistical learning through STDP in spiking neural networks J Bill, R Legenstein Frontiers in neuroscience 8, 412, 2014	83	2014
Network plasticity as Bayesian inference D Kappel, S Habenschuss, R Legenstein, W Maass PLoS Comput Biol 11 (11), e1004485, 2015	79	2015
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	74	2017
At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks N Bertschinger, T Natschläger, RA Legenstein Advances in neural information processing systems, 145-152, 2005	74	2005
A new approach towards vision suggested by biologically realistic neural microcircuit models W Maass, R Legenstein, H Markram International Workshop on Biologically Motivated Computer Vision, 282-293, 2002	65	2002

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