Robert Legenstein - Google Scholar Citations

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	All	Since 2014
Citations	3054	2120
h-index	26	22
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Co-authors

Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Lars BuesingGoogle DeepMindVerified email at google.com
Johannes BillHarvard Medical SchoolVerified email at hms.harvard.edu
Themis ProdromakisProfessor of Nanotechnology, University of SouthamptonVerified email at soton.ac.uk
Andreas Töschercommendo research & consultingVerified email at commendo.at
Benjamin SchrauwenProfessor of Machine Learning, Ghent UniversityVerified email at ugent.be
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
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität BochumVerified email at ini.rub.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
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
Christos H PAPADIMITRIOUColumbia UniversityVerified email at columbia.edu
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


Title	Cited by	Year
Integration of nanoscale memristor synapses in neuromorphic computing architectures G Indiveri, B Linares-Barranco, R Legenstein, G Deligeorgis, ... Nanotechnology 24 (38), 384010, 2013	343	2013
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural Networks 20 (3), 323-334, 2007	306	2007
Combining predictions for accurate recommender systems M Jahrer, A Töscher, R Legenstein Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	265	2010
What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005	228	2005
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	212	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, 12611, 2016	142	2016
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	120	2010
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, 2012	108	2012
Branch-specific plasticity enables self-organization of nonlinear computation in single neurons R Legenstein, W Maass Journal of Neuroscience 31 (30), 10787-10802, 2011	105	2011
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	101	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	99	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	99	2007
Reinforcement learning on slow features of high-dimensional input streams R Legenstein, N Wilbert, L Wiskott PLoS computational biology 6 (8), e1000894, 2010	90	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	74	2014
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	68	2005
Network plasticity as Bayesian inference D Kappel, S Habenschuss, R Legenstein, W Maass PLoS computational biology 11 (11), e1004485, 2015	63	2015
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	62	2002
Improved neighborhood-based algorithms for large-scale recommender systems A Töscher, M Jahrer, R Legenstein Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and …, 2008	60	2008
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	57	2018
On computational power and the order-chaos phase transition in reservoir computing B Schrauwen, L Büsing, RA Legenstein Advances in Neural Information Processing Systems, 1425-1432, 2009	48	2009

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