Wolfgang Maass

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Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyEmail verificata su igi.tugraz.at
Henry MarkramProfessor of Neuroscience, EPFLEmail verificata su epfl.ch
Thomas NatschlägerDynatrace, Lead Data ScientistEmail verificata su dynatrace.com
Guillaume BELLECEPFLEmail verificata su epfl.ch
Franz ScherrHuawei Technologies - Zurich Research CenterEmail verificata su huawei.com
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, AustriaEmail verificata su scch.at
Eduardo SontagNortheastern University ECE and BioEEmail verificata su northeastern.edu
Lars BuesingGoogle DeepMindEmail verificata su google.com
Michael PfeifferBosch Center for Artificial IntelligenceEmail verificata su de.bosch.com
Peter AuerMontanuniversität LeobenEmail verificata su unileoben.ac.at
Christos H PAPADIMITRIOUColumbia UniversityEmail verificata su columbia.edu
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Email verificata su microsoft.com
Dorit S. HOCHBAUMUC BerkeleyEmail verificata su ieor.berkeley.edu
Nikos LogothetisDirector of International Center for Primate Brain Research, Shanghai, ChinaEmail verificata su stanford.edu
Anthony ZadorProfessor of Biology, CSHLEmail verificata su cshl.edu
Auke IjspeertEPFL, Ecole Polytechnique Fédérale de Lausanne, SwitzerlandEmail verificata su epfl.ch
Wolf SingerProfessor für Neuroscience, Max Planck InstituteEmail verificata su brain.mpg.de
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Email verificata su robot-learning.de
Rolf PfeiferDirector, Artificial Intelligence Laboratory, University of ZurichEmail verificata su ifi.uzh.ch
Rodney DouglasProfessor of Computational NeuroscienceEmail verificata su ini.phys.ethz.ch

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Wolfgang Maass

Professor of Computer Science, Graz University of Technology

Email verificata su igi.tugraz.at - Home page

Computational Neuroscience Machine Learning Computational Complexity Theory


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	4588	2002
Networks of spiking neurons: the third generation of neural network models W Maass Neural networks 10 (9), 1659-1671, 1997	3885	1997
Pulsed neural networks W Maass, CM Bishop MIT press, 2001	1379	2001
State-dependent computations: spatiotemporal processing in cortical networks DV Buonomano, W Maass Nature Reviews Neuroscience 10 (2), 113-125, 2009	1049	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	982	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	665*	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	569	2018
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	552	2007
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	521	2020
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	517	1993
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	509	2011
2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022	451	2022
On the computational power of winner-take-all W Maass Neural computation 12 (11), 2519-2535, 2000	447	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	384	2011
Lower bounds for the computational power of networks of spiking neurons W Maass Neural computation 8 (1), 1-40, 1996	365	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	342	2008
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	334	2013
The" liquid computer": A novel strategy for real-time computing on time series T Natschläger, W Maass, H Markram Telematik 8 (1), 39-43, 2002	330	2002
Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017	326	2017
Computational aspects of feedback in neural circuits W Maass, P Joshi, ED Sontag PLoS computational biology 3 (1), e165, 2007	302	2007

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