Robert Legenstein 个人学术档案

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Wolfgang MaassProfessor of Computer Science, Graz University of Technology在 igi.tugraz.at 的电子邮件经过验证
Themis ProdromakisRegius Chair of Engineering, Centre for Electronics Frontiers, University of Edinburgh在 ed.ac.uk 的电子邮件经过验证
Johannes BillHarvard Medical School在 hms.harvard.edu 的电子邮件经过验证
Lars BuesingGoogle DeepMind在 google.com 的电子邮件经过验证
Christos H PAPADIMITRIOUColumbia University在 columbia.edu 的电子邮件经过验证
Dejan PecevskiAI/ML Department, Jumio Corp., Vienna, Austria在 jumio.com 的电子邮件经过验证
Andreas Töschermindfex在 mindfex.com 的电子邮件经过验证
Giacomo IndiveriInstitute of Neuroinformatics, University of Zurich and ETH Zurich在 ini.uzh.ch 的电子邮件经过验证
George DeligeorgisPrincipal Researcher, Institute of Electronic Structure and Laser ( IESL - FORTH )在 iesl.forth.gr 的电子邮件经过验证
Bernabe Linares-BarrancoInstituto de Microelectrónica de Sevilla, IMSE-CNM, Universidad de Sevilla, CSIC在 imse-cnm.csic.es 的电子邮件经过验证
Benjamin SchrauwenProfessor of Machine Learning, Ghent University在 ugent.be 的电子邮件经过验证
Laurenz WiskottProfessor of Theory of Neural Systems, Institut für Neuroinformatik, Ruhr-Universität Bochum在 ini.rub.de 的电子邮件经过验证
Gregor M. HörzerPhilosopher and former Computational Neuroscientist, University of Osnabrück在 uos.de 的电子邮件经过验证
Steven M. Chase, Ph.D.Carnegie Mellon University在 cmu.edu 的电子邮件经过验证
Nils BertschingerFIAS在 fias.uni-frankfurt.de 的电子邮件经过验证
Henry MarkramProfessor of Neuroscience, EPFL在 epfl.ch 的电子邮件经过验证
Thomas NatschlägerDynatrace, Lead Data Scientist在 dynatrace.com 的电子邮件经过验证
Arne BlackmanPfizer在 pfizer.com 的电子邮件经过验证
Per Jesper SjöströmProfessor of Neuroscience, McGill University在 mcgill.ca 的电子邮件经过验证

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Robert Legenstein

Institute for Theoretical Computer Science, Graz University of Technology

在 igi.tugraz.at 的电子邮件经过验证 - 首页

Computational Neuroscience


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Integration of nanoscale memristor synapses in neuromorphic computing architectures G Indiveri, B Linares-Barranco, R Legenstein, G Deligeorgis, ... Nanotechnology 24 (38), 384010, 2013	668	2013
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	526	2007
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	523	2018
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), 3625, 2020	446	2020
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), 12611, 2016	362	2016
Combining predictions for accurate recommender systems M Jahrer, A Töscher, R Legenstein Proceedings of the 16th ACM SIGKDD international conference on Knowledge …, 2010	355	2010
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	325	2008
Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017	297	2017
What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005	280	2005
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	194	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	186	2011
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	184	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, 2014	175	2014
What makes a dynamical system computationally powerful? R Legenstein, W Maass	160	2006
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	157	2010
Network plasticity as Bayesian inference D Kappel, S Habenschuss, R Legenstein, W Maass PLoS computational biology 11 (11), e1004485, 2015	136	2015
A compound memristive synapse model for statistical learning through STDP in spiking neural networks J Bill, R Legenstein Frontiers in neuroscience 8, 120754, 2014	126	2014
Reinforcement learning on slow features of high-dimensional input streams R Legenstein, N Wilbert, L Wiskott PLoS computational biology 6 (8), e1000894, 2010	114	2010
Restoring vision in adverse weather conditions with patch-based denoising diffusion models O Özdenizci, R Legenstein IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023	111	2023
Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets G Bellec, F Scherr, E Hajek, D Salaj, R Legenstein, W Maass arXiv preprint arXiv:1901.09049, 2019	105	2019

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