| 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 | 2824 | 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 (3), 349-398, 2007 | 805 | 2007 |
| Real-time computation at the edge of chaos in recurrent neural networks N Bertschinger, T Natschläger Neural computation 16 (7), 1413-1436, 2004 | 591 | 2004 |
| Computational models for generic cortical microcircuits W Maass, T Natschläger, H Markram Computational neuroscience: A comprehensive approach 18, 575-605, 2004 | 228 | 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 | 227 | 2002 |
| On the computational power of circuits of spiking neurons W Maass, H Markram Journal of computer and system sciences 69 (4), 593-616, 2004 | 223 | 2004 |
| Spatial and temporal pattern analysis via spiking neurons T Natschläger, B Ruf Network: Computation in Neural Systems 9 (3), 319-332, 1998 | 223 | 1998 |
| 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 | 134 | 2017 |
| A model for real-time computation in generic neural microcircuits W Maass, T Natschläger, H Markram Advances in neural information processing systems, 229-236, 2003 | 111 | 2003 |
| PCSIM: a parallel simulation environment for neural circuits fully integrated with Python D Pecevski, T Natschläger, K Schuch Frontiers in neuroinformatics 3, 11, 2009 | 106 | 2009 |
| 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 | 95 | 1997 |
| Computer models and analysis tools for neural microcircuits T Natschläger, H Markram, W Maass Neuroscience databases, 123-138, 2003 | 90 | 2003 |
| 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 | 79 | 2004 |
| At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks N Bertschinger, T Natschläger, RA Legenstein Advances in neural information processing systems, 145-152, 2005 | 72 | 2005 |
| Spiking neurons and the induction of finite state machines T Natschläger, W Maass Theoretical Computer Science 287 (1), 251-265, 2002 | 59 | 2002 |
| A model for fast analog computation based on unreliable synapses W Maass, T Natschläger Neural Computation 12 (7), 1679-1704, 2000 | 36 | 2000 |
| On stability of distance measures for event sequences induced by level-crossing sampling BA Moser, T Natschlaeger IEEE Transactions on Signal Processing 62 (8), 1987-1999, 2014 | 25 | 2014 |
| Sensitivity analysis and validation of an EnergyPlus model of a house in Upper Austria W Pereira, A Bögl, T Natschläger Energy Procedia 62 (Supplement C), 472-481, 2014 | 23 | 2014 |
| A fresh look at real-time computation in generic recurrent neural circuits W Maass, T Natschläger, H Markram submitted for publication, 2002 | 23 | 2002 |
| Computing the optimally fitted spike train for a synapse T Natschläger, W Maass Neural computation 13 (11), 2477-2494, 2001 | 22 | 2001 |
Wolfgang MaassProfessor of Computer Science, Graz University of TechnologyEmail verificata su igi.tugraz.at
