Wolfgang Maass

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	Toate	Din 2020
Referințe bibliografice	32839	12934
h-index	78	49
i10-index	210	116

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Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyAdresă de e-mail confirmată pe igi.tugraz.at
Henry MarkramProfessor of Neuroscience, EPFLAdresă de e-mail confirmată pe epfl.ch
Thomas NatschlägerDynatrace, Lead Data ScientistAdresă de e-mail confirmată pe dynatrace.com
Guillaume BELLECEPFLAdresă de e-mail confirmată pe epfl.ch
Franz ScherrHuawei Technologies - Zurich Research CenterAdresă de e-mail confirmată pe huawei.com
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, AustriaAdresă de e-mail confirmată pe scch.at
Lars BuesingGoogle DeepMindAdresă de e-mail confirmată pe google.com
Eduardo SontagNortheastern University ECE and BioEAdresă de e-mail confirmată pe northeastern.edu
Michael PfeifferBosch Center for Artificial IntelligenceAdresă de e-mail confirmată pe de.bosch.com
Peter AuerMontanuniversität LeobenAdresă de e-mail confirmată pe unileoben.ac.at
Christos H PAPADIMITRIOUColumbia UniversityAdresă de e-mail confirmată pe columbia.edu
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Adresă de e-mail confirmată pe microsoft.com
Dorit S. HOCHBAUMUC BerkeleyAdresă de e-mail confirmată pe ieor.berkeley.edu
Nikos LogothetisDirector of International Center for Primate Brain Research, Shanghai, ChinaAdresă de e-mail confirmată pe stanford.edu
Rodney DouglasProfessor of Computational NeuroscienceAdresă de e-mail confirmată pe ini.phys.ethz.ch
Anthony ZadorProfessor of Biology, CSHLAdresă de e-mail confirmată pe cshl.edu
Auke IjspeertEPFL, Ecole Polytechnique Fédérale de Lausanne, SwitzerlandAdresă de e-mail confirmată pe epfl.ch
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Adresă de e-mail confirmată pe robot-learning.de
Rolf PfeiferDirector, Artificial Intelligence Laboratory, University of ZurichAdresă de e-mail confirmată pe ifi.uzh.ch
Wolf SingerProfessor für Neuroscience, Max Planck InstituteAdresă de e-mail confirmată pe brain.mpg.de

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Wolfgang Maass

Professor of Computer Science, Graz University of Technology

Adresă de e-mail confirmată pe igi.tugraz.at - Pagina de pornire

Computational Neuroscience Machine Learning Computational Complexity Theory


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Real-time computing without stable states: A new framework for neural computation based on perturbations W Maass, T Natschläger, H Markram Neural computation 14 (11), 2531-2560, 2002	4736	2002
Networks of spiking neurons: the third generation of neural network models W Maass Neural networks 10 (9), 1659-1671, 1997	4101	1997
Pulsed neural networks W Maass, CM Bishop MIT press, 2001	1376	2001
State-dependent computations: spatiotemporal processing in cortical networks DV Buonomano, W Maass Nature Reviews Neuroscience 10 (2), 113-125, 2009	1075	2009
Approximation schemes for covering and packing problems in image processing and VLSI DS Hochbaum, W Maass Journal of the ACM (JACM) 32 (1), 130-136, 1985	998	1985
On the computational power of circuits of spiking neurons W Maass, H Markram Journal of computer and system sciences 69 (4), 593-616, 2004	666*	2004
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	614	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	560	2020
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	559	2007
Threshold circuits of bounded depth A Hajnal, W Maass, P Pudlák, M Szegedy, G Turán Journal of Computer and System Sciences 46 (2), 129-154, 1993	526	1993
2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022	517	2022
Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons L Buesing, J Bill, B Nessler, W Maass PLoS computational biology 7 (11), e1002211, 2011	517	2011
On the computational power of winner-take-all W Maass Neural computation 12 (11), 2519-2535, 2000	459	2000
Towards a theoretical foundation for morphological computation with compliant bodies H Hauser, AJ Ijspeert, RM Füchslin, R Pfeifer, W Maass Biological cybernetics 105, 355-370, 2011	394	2011
Lower bounds for the computational power of networks of spiking neurons W Maass Neural computation 8 (1), 1-40, 1996	371	1996
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	349	2008
Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017	341	2017
Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity B Nessler, M Pfeiffer, L Buesing, W Maass PLoS computational biology 9 (4), e1003037, 2013	340	2013
The" liquid computer": A novel strategy for real-time computing on time series T Natschläger, W Maass, H Markram	331	2002
Computational aspects of feedback in neural circuits W Maass, P Joshi, ED Sontag PLoS computational biology 3 (1), e165, 2007	308	2007

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