Thomas Natschläger
Thomas Natschläger
Dynatrace, Lead Data Scientist
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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
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
Real-time computation at the edge of chaos in recurrent neural networks
N Bertschinger, T Natschläger
Neural computation 16 (7), 1413-1436, 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
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
Computational models for generic cortical microcircuits
W Maass, T Natschläger, H Markram
Computational neuroscience: A comprehensive approach 18, 575-605, 2004
Spatial and temporal pattern analysis via spiking neurons
T Natschläger, B Ruf
Network: Computation in Neural Systems 9 (3), 319, 1998
A model for real-time computation in generic neural microcircuits
W Maass, T Natschlager, H Markram
Advances in neural information processing systems, 229-236, 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
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
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
Computer models and analysis tools for neural microcircuits
T Natschläger, H Markram, W Maass
Neuroscience databases, 123-138, 2003
At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks
T Natschläger, N Bertschinger, R Legenstein
Advances in neural information processing systems 17, 145-152, 2005
Spiking neurons and the induction of finite state machines
T Natschläger, W Maass
Theoretical computer science 287 (1), 251-265, 2002
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
Standard-free calibration transfer-An evaluation of different techniques
B Malli, A Birlutiu, T Natschläger
Chemometrics and Intelligent Laboratory Systems 161, 49-60, 2017
A model for fast analog computation based on unreliable synapses
W Maass, T Natschläger
Neural Computation 12 (7), 1679-1704, 2000
Generalized online transfer learning for climate control in residential buildings
T Grubinger, GC Chasparis, T Natschläger
Energy and Buildings 139, 63-71, 2017
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
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