Wolfgang Maass 个人学术档案

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	总计	2019 年至今
引用	30906	13042
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i10 指数	208	121

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Robert LegensteinInstitute for Theoretical Computer Science, Graz University of Technology在 igi.tugraz.at 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
Guillaume BELLECEPFL在 epfl.ch 的电子邮件经过验证
Franz ScherrHuawei Technologies - Zurich Research Center在 huawei.com 的电子邮件经过验证
Bernhard NesslerSCCH GmbH & Johannes Kepler University Linz, Austria在 scch.at 的电子邮件经过验证
Eduardo SontagNortheastern University ECE and BioE在 northeastern.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
Michael PfeifferBosch Center for Artificial Intelligence在 de.bosch.com 的电子邮件经过验证
Peter AuerMontanuniversität Leoben在 unileoben.ac.at 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
Christopher M. BishopTechnical Fellow, Microsoft Research, Cambridge, U.K.在 microsoft.com 的电子邮件经过验证
Dorit S. HOCHBAUMUC Berkeley在 ieor.berkeley.edu 的电子邮件经过验证
Nikos LogothetisVisiting Professor of Neuroscience, Stanford University在 stanford.edu 的电子邮件经过验证
Anthony ZadorProfessor of Biology, CSHL在 cshl.edu 的电子邮件经过验证
Auke IjspeertEPFL, Ecole Polytechnique Fédérale de Lausanne, Switzerland在 epfl.ch 的电子邮件经过验证
Wolf SingerProfessor für Neuroscience, Max Planck Institute在 brain.mpg.de 的电子邮件经过验证
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)在 robot-learning.de 的电子邮件经过验证
Rolf PfeiferDirector, Artificial Intelligence Laboratory, University of Zurich在 ifi.uzh.ch 的电子邮件经过验证
Rodney DouglasProfessor of Computational Neuroscience在 ini.phys.ethz.ch 的电子邮件经过验证

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Wolfgang Maass

Professor of Computer Science, Graz University of Technology

在 igi.tugraz.at 的电子邮件经过验证 - 首页

Computational Neuroscience Machine Learning Computational Complexity Theory


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
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	4471	2002
Networks of spiking neurons: the third generation of neural network models W Maass Neural networks 10 (9), 1659-1671, 1997	3691	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	1036	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	971	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	653*	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	549	2018
Edge of chaos and prediction of computational performance for neural circuit models R Legenstein, W Maass Neural networks 20 (3), 323-334, 2007	539	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	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	496	2011
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	485	2020
On the computational power of winner-take-all W Maass Neural computation 12 (11), 2519-2535, 2000	436	2000
2022 roadmap on neuromorphic computing and engineering DV Christensen, R Dittmann, B Linares-Barranco, A Sebastian, ... Neuromorphic Computing and Engineering 2 (2), 022501, 2022	407	2022
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	368	2011
Lower bounds for the computational power of networks of spiking neurons W Maass Neural computation 8 (1), 1-40, 1996	362	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	336	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	330	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	328	2002
Deep rewiring: Training very sparse deep networks G Bellec, D Kappel, W Maass, R Legenstein arXiv preprint arXiv:1711.05136, 2017	311	2017
Computational aspects of feedback in neural circuits W Maass, P Joshi, ED Sontag PLoS computational biology 3 (1), e165, 2007	293	2007

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