Wolfgang Maass
Wolfgang Maass
Professor of Computer Science, Graz University of Technology
Verified email at igi.tugraz.at - Homepage
Cited by
Cited by
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
Networks of spiking neurons: the third generation of neural network models
W Maass
Neural networks 10 (9), 1659-1671, 1997
Pulsed neural networks
W Maass, CM Bishop
MIT press, 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
State-dependent computations: spatiotemporal processing in cortical networks
DV Buonomano, W Maass
Nature Reviews Neuroscience 10 (2), 113-125, 2009
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
Edge of chaos and prediction of computational performance for neural circuit models
R Legenstein, W Maass
Neural networks 20 (3), 323-334, 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
On the computational power of winner-take-all
W Maass
Neural computation 12 (11), 2519-2535, 2000
Lower bounds for the computational power of networks of spiking neurons
W Maass
Neural computation 8 (1), 1-40, 1996
On the computational power of circuits of spiking neurons
W Maass, H Markram
Journal of computer and system sciences 69 (4), 593-616, 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
Fast sigmoidal networks via spiking neurons
W Maass
Neural computation 9 (2), 279-304, 1997
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
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
Towards a theoretical foundation for morphological computation with compliant bodies
H Hauser, AJ Ijspeert, RM Füchslin, R Pfeifer, W Maass
Biological cybernetics 105 (5), 355-370, 2011
What can a neuron learn with spike-timing-dependent plasticity?
R Legenstein, C Naeger, W Maass
Neural computation 17 (11), 2337-2382, 2005
Computational models for generic cortical microcircuits
W Maass, T Natschläger, H Markram
Computational neuroscience: A comprehensive approach 18, 575-605, 2004
Computational aspects of feedback in neural circuits
W Maass, P Joshi, ED Sontag
PLoS computational biology 3 (1), e165, 2007
Noisy spiking neurons with temporal coding have more computational power than sigmoidal neurons
W Maass
Advances in neural information processing systems, 211-217, 1997
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