| 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 | 4591 | 2002 |
| Simulation of networks of spiking neurons: a review of tools and strategies R Brette, M Rudolph, T Carnevale, M Hines, D Beeman, JM Bower, ... Journal of computational neuroscience 23, 349-398, 2007 | 1057 | 2007 |
| Real-time computation at the edge of chaos in recurrent neural networks N Bertschinger, T Natschläger Neural computation 16 (7), 1413-1436, 2004 | 965 | 2004 |
| Central moment discrepancy (cmd) for domain-invariant representation learning W Zellinger, T Grubinger, E Lughofer, T Natschläger, S Saminger-Platz arXiv preprint arXiv:1702.08811, 2017 | 705 | 2017 |
| On the computational power of circuits of spiking neurons W Maass, H Markram Journal of computer and system sciences 69 (4), 593-616, 2004 | 532 | 2004 |
| 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 | 330 | 2002 |
| Spatial and temporal pattern analysis via spiking neurons T Natschläger, B Ruf Network: Computation in Neural Systems 9 (3), 319, 1998 | 275 | 1998 |
| Computational models for generic cortical microcircuits W Maass, T Natschläger, H Markram Computational neuroscience: A comprehensive approach 18, 575-605, 2004 | 271 | 2004 |
| PCSIM: a parallel simulation environment for neural circuits fully integrated with Python D Pecevski, T Natschläger, K Schuch Frontiers in neuroinformatics 3, 356, 2009 | 148 | 2009 |
| A model for real-time computation in generic neural microcircuits W Maass, T Natschläger, H Markram Advances in neural information processing systems 15, 2002 | 139 | 2002 |
| Fading memory and kernel properties of generic cortical microcircuit models W Maass, T Natschläger, H Markram Journal of Physiology-Paris 98 (4-6), 315-330, 2004 | 116 | 2004 |
| At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks N Bertschinger, T Natschläger, R Legenstein Advances in neural information processing systems 17, 2004 | 110 | 2004 |
| Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding W Maass, T Natschläger Network: Computation in Neural Systems 8 (4), 355-371, 1997 | 106 | 1997 |
| Robust unsupervised domain adaptation for neural networks via moment alignment W Zellinger, BA Moser, T Grubinger, E Lughofer, T Natschläger, ... Information Sciences 483, 174-191, 2019 | 101 | 2019 |
| Computer models and analysis tools for neural microcircuits T Natschläger, H Markram, W Maass Neuroscience databases: a practical guide, 123-138, 2003 | 100 | 2003 |
| Efficient temporal processing with biologically realistic dynamic synapses T Natschlger, W Maass, A Zador Network: Computation in Neural Systems 12 (1), 75-87, 2001 | 92 | 2001 |
| Standard-free calibration transfer-An evaluation of different techniques B Malli, A Birlutiu, T Natschläger Chemometrics and Intelligent Laboratory Systems 161, 49-60, 2017 | 70 | 2017 |
| Spiking neurons and the induction of finite state machines T Natschläger, W Maass Theoretical computer science 287 (1), 251-265, 2002 | 70 | 2002 |
| Generalized online transfer learning for climate control in residential buildings T Grubinger, GC Chasparis, T Natschläger Energy and Buildings 139, 63-71, 2017 | 63 | 2017 |
| Sensitivity analysis and validation of an EnergyPlus model of a house in Upper Austria W Pereira, A Bögl, T Natschläger Energy Procedia 62, 472-481, 2014 | 50 | 2014 |
Wolfgang MaassProfessor of Computer Science, Graz University of Technologyigi.tugraz.at의 이메일 확인됨