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Thomas Natschläger
Thomas Natschläger
Dynatrace, Lead Data Scientist
Verified email at dynatrace.com
Title
Cited by
Cited by
Year
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
45332002
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
10522007
Real-time computation at the edge of chaos in recurrent neural networks
N Bertschinger, T Natschläger
Neural computation 16 (7), 1413-1436, 2004
9532004
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
6962017
On the computational power of circuits of spiking neurons
W Maass, H Markram
Journal of computer and system sciences 69 (4), 593-616, 2004
5262004
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
3292002
Spatial and temporal pattern analysis via spiking neurons
T Natschläger, B Ruf
Network: Computation in Neural Systems 9 (3), 319, 1998
2741998
Computational models for generic cortical microcircuits
W Maass, T Natschläger, H Markram
Computational neuroscience: A comprehensive approach 18, 575-605, 2004
2712004
PCSIM: a parallel simulation environment for neural circuits fully integrated with Python
D Pecevski, T Natschläger, K Schuch
Frontiers in neuroinformatics 3, 356, 2009
1452009
A model for real-time computation in generic neural microcircuits
W Maass, T Natschläger, H Markram
Advances in neural information processing systems 15, 2002
1382002
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
1152004
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
1092004
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
1051997
Computer models and analysis tools for neural microcircuits
T Natschläger, H Markram, W Maass
Neuroscience databases: a practical guide, 123-138, 2003
1002003
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
982019
Efficient temporal processing with biologically realistic dynamic synapses
T Natschlger, W Maass, A Zador
Network: Computation in Neural Systems 12 (1), 75-87, 2001
922001
Spiking neurons and the induction of finite state machines
T Natschläger, W Maass
Theoretical computer science 287 (1), 251-265, 2002
702002
Standard-free calibration transfer-An evaluation of different techniques
B Malli, A Birlutiu, T Natschläger
Chemometrics and Intelligent Laboratory Systems 161, 49-60, 2017
692017
Generalized online transfer learning for climate control in residential buildings
T Grubinger, GC Chasparis, T Natschläger
Energy and Buildings 139, 63-71, 2017
632017
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
502014
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Articles 1–20