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
Bernhard NesslerJohannes Kepler University Linz, AustriaVerified email at nessler.com
Guillaume BELLECEPFLVerified email at epfl.ch
Peter AuerMontanuniversität LeobenVerified email at unileoben.ac.at
Eduardo SontagNortheastern University ECE and BioEVerified email at northeastern.edu
Christopher M. BishopDistinguished Scientist, Microsoft Research, Cambridge, U.K.Verified email at microsoft.com
Lars BuesingGoogle DeepMindVerified email at google.com
Michael PfeifferBosch Center for Artificial IntelligenceVerified email at de.bosch.com
Franz ScherrTU GrazVerified email at tugraz.at
Anthony ZadorProfessor of Biology, CSHLVerified email at cshl.edu
Dorit S. HOCHBAUMUC BerkeleyVerified email at ieor.berkeley.edu
Nikos LogothetisVisiting Professor of Neuroscience, Stanford UniversityVerified email at stanford.edu
Rodney DouglasProfessor of Computational NeuroscienceVerified email at ini.phys.ethz.ch
Auke IjspeertEPFL, Ecole Polytechnique Fédérale de Lausanne, SwitzerlandVerified email at epfl.ch
Christos H PAPADIMITRIOUColumbia UniversityVerified email at columbia.edu
Wolf SingerProfessor für Neuroscience, Max Planck InstituteVerified email at brain.mpg.de
Rolf PfeiferDirector, Artificial Intelligence Laboratory, University of ZurichVerified email at ifi.uzh.ch
Pekka OrponenProfessor of Computer Science, Aalto UniversityVerified email at aalto.fi

Wolfgang Maass

Professor of Computer Science, Graz University of Technology

Verified email at igi.tugraz.at - Homepage

Computational Neuroscience Machine Learning Computational Complexity Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	3103	2002
Networks of spiking neurons: the third generation of neural network models W Maass Neural networks 10 (9), 1659-1671, 1997	1990	1997
Pulsed neural networks W Maass, CM Bishop MIT press, 2001	1219	2001
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	841	1985
State-dependent computations: spatiotemporal processing in cortical networks DV Buonomano, W Maass Nature Reviews Neuroscience 10 (2), 113-125, 2009	788	2009
Introduction: Bilingual conversation revisited P Auer Code-switching in conversation: Language, interaction and identity 1, 24, 1998	594	1998
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	448	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 Comput Biol 7 (11), e1002211, 2011	375	2011
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	374	2007
Universal multi-task kernels A Caponnetto, CA Micchelli, M Pontil, Y Ying The Journal of Machine Learning Research 9, 1615-1646, 2008	344*	2008
On the computational power of winner-take-all W Maass Neural computation 12 (11), 2519-2535, 2000	336	2000
Lower bounds for the computational power of networks of spiking neurons W Maass Neural computation 8 (1), 1-40, 1996	303	1996
Fast sigmoidal networks via spiking neurons W Maass Neural Computation 9 (2), 279-304, 1997	265	1997
On the computational power of circuits of spiking neurons W Maass, H Markram Journal of computer and system sciences 69 (4), 593-616, 2004	260	2004
The" liquid computer": A novel strategy for real-time computing on time series T Natschläger, W Maass, H Markram Special issue on Foundations of Information Processing of TELEMATIK 8 …, 2002	254	2002
What can a neuron learn with spike-timing-dependent plasticity? R Legenstein, C Naeger, W Maass Neural computation 17 (11), 2337-2382, 2005	250	2005
Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity B Nessler, M Pfeiffer, L Buesing, W Maass PLoS Comput Biol 9 (4), e1003037, 2013	248	2013
A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback R Legenstein, D Pecevski, W Maass PLoS Comput Biol 4 (10), e1000180, 2008	247	2008
Computational models for generic cortical microcircuits W Maass, T Natschläger, H Markram Computational neuroscience: A comprehensive approach 18, 575-605, 2003	242	2003
Computational neuroscience: a comprehensive approach J Feng CRC press, 2003	236	2003

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