Folgen
Wolfgang Maass
Wolfgang Maass
Professor of Computer Science, Graz University of Technology
Bestätigte E-Mail-Adresse bei igi.tugraz.at - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
39502002
Networks of spiking neurons: the third generation of neural network models
W Maass
Neural networks 10 (9), 1659-1671, 1997
29391997
Pulsed neural networks
W Maass, CM Bishop
MIT press, 2001
12832001
State-dependent computations: spatiotemporal processing in cortical networks
DV Buonomano, W Maass
Nature Reviews Neuroscience 10 (2), 113-125, 2009
9382009
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
9191985
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
4921993
On the computational power of circuits of spiking neurons
W Maass, H Markram
Journal of computer and system sciences 69 (4), 593-616, 2004
4732004
Edge of chaos and prediction of computational performance for neural circuit models
R Legenstein, W Maass
Neural networks 20 (3), 323-334, 2007
4722007
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
4432011
On the computational power of winner-take-all
W Maass
Neural computation 12 (11), 2519-2535, 2000
3992000
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
3892018
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
3322011
Lower bounds for the computational power of networks of spiking neurons
W Maass
Neural computation 8 (1), 1-40, 1996
3291996
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
3092008
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
3072002
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
3032013
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
2862020
Fast sigmoidal networks via spiking neurons
W Maass
Neural computation 9 (2), 279-304, 1997
2761997
Computational aspects of feedback in neural circuits
W Maass, P Joshi, ED Sontag
PLoS computational biology 3 (1), e165, 2007
2752007
What can a neuron learn with spike-timing-dependent plasticity?
R Legenstein, C Naeger, W Maass
Neural computation 17 (11), 2337-2382, 2005
2692005
Das System kann den Vorgang jetzt nicht ausführen. Versuchen Sie es später erneut.
Artikel 1–20