Wolfgang Maass

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Robert LegensteinInstitute for Theoretical Computer Science, Graz University of TechnologyVerified email at igi.tugraz.at
Henry MarkramProfessor of Neuroscience, EPFLVerified email at epfl.ch
Thomas NatschlägerDynatrace, Lead Data ScientistVerified email at dynatrace.com
Guillaume BELLECEPFLVerified email at epfl.ch
Franz ScherrHuawei Technologies - Zurich Research CenterVerified email at huawei.com
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, AustriaVerified email at scch.at
Eduardo SontagNortheastern University ECE and BioEVerified email at northeastern.edu
Lars BuesingGoogle DeepMindVerified email at google.com
Michael PfeifferBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Peter AuerMontanuniversität LeobenVerified email at unileoben.ac.at
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.Verified email at microsoft.com
Dorit S. HOCHBAUMUC BerkeleyVerified email at ieor.berkeley.edu
Christos H PAPADIMITRIOUColumbia UniversityVerified email at columbia.edu
Nikos LogothetisVisiting Professor of Neuroscience, Stanford UniversityVerified email at stanford.edu
Auke IjspeertEPFL, Ecole Polytechnique Fédérale de Lausanne, SwitzerlandVerified email at epfl.ch
Rodney DouglasProfessor of Computational NeuroscienceVerified email at ini.phys.ethz.ch
Anthony ZadorProfessor of Biology, CSHLVerified email at cshl.edu
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Verified email at robot-learning.de
Rolf PfeiferDirector, Artificial Intelligence Laboratory, University of ZurichVerified email at ifi.uzh.ch
Wolf SingerProfessor für Neuroscience, Max Planck InstituteVerified email at brain.mpg.de

Wolfgang Maass

Professor of Computer Science, Graz University of Technology

Verified email at igi.tugraz.at - Homepage

Computational Neuroscience Machine Learning Computational Complexity Theory


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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	3935	2002
Networks of spiking neurons: the third generation of neural network models W Maass Neural networks 10 (9), 1659-1671, 1997	2926	1997
Pulsed neural networks W Maass, CM Bishop MIT press, 2001	1282	2001
State-dependent computations: spatiotemporal processing in cortical networks DV Buonomano, W Maass Nature Reviews Neuroscience 10 (2), 113-125, 2009	938	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	919	1985
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	491	1993
On the computational power of circuits of spiking neurons W Maass, H Markram Journal of computer and system sciences 69 (4), 593-616, 2004	473	2004
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	472	2007
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	443	2011
On the computational power of winner-take-all W Maass Neural computation 12 (11), 2519-2535, 2000	397	2000
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	387	2018
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	329	2011
Lower bounds for the computational power of networks of spiking neurons W Maass Neural computation 8 (1), 1-40, 1996	329	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	309	2008
The" liquid computer": A novel strategy for real-time computing on time series T Natschläger, W Maass, H Markram Telematik 8 (ARTICLE), 39-43, 2002	307	2002
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	303	2013
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	280	2020
Fast sigmoidal networks via spiking neurons W Maass Neural computation 9 (2), 279-304, 1997	276	1997
Computational aspects of feedback in neural circuits W Maass, P Joshi, ED Sontag PLoS computational biology 3 (1), e165, 2007	274	2007
What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005	269	2005

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