Thomas Natschläger's Publications


This list is also available as BiBTeX file. Also visit Thomas Natschläger's Homepage.

[19]
T. Natschläger, N. Bertschinger, and R. Legenstein. At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks. In Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, Advances in Neural Information Processing Systems 17, pages 145-152. MIT Press, Cambridge, MA, 2005. (PDF, 706 KB).

[18]
T. Natschläger and W. Maass. Dynamics of information and emergent computation in generic neural microcircuit models. Neural Networks, 18(10):1301-1308, 2005. (PDF, 410 KB).

[17]
N. Bertschinger and T. Natschläger. Real-time computation at the edge of chaos in recurrent neural networks. Neural Computation, 16(7):1413-1436, 2004. (PDF, 476 KB).

[16]
T. Natschläger and W. Maass. Information dynamics and emergent computation in recurrent circuits of spiking neurons. In S. Thrun, L. Saul, and B. Schölkopf, editors, Proc. of NIPS 2003, Advances in Neural Information Processing Systems, volume 16, pages 1255-1262, Cambridge, 2004. MIT Press. (PDF, 180 KB).

[15]
W. Maass, T. Natschläger, and H. Markram. Computational models for generic cortical microcircuits. In J. Feng, editor, Computational Neuroscience: A Comprehensive Approach, chapter 18, pages 575-605. Chapman & Hall/CRC, Boca Raton, 2004. (PDF, 863 KB).

[14]
W. Maass, T. Natschläger, and H. Markram. A model for real-time computation in generic neural microcircuits. In S. Becker, S. Thrun, and K. Obermayer, editors, Proc. of NIPS 2002, Advances in Neural Information Processing Systems, volume 15, pages 229-236. MIT Press, 2003. (PDF, 254 KB).

[13]
T. Natschläger, H. Markram, and W. Maass. Computer models and analysis tools for neural microcircuits. In R. Kötter, editor, Neuroscience Databases. A Practical Guide, chapter 9, pages 123-138. Kluwer Academic Publishers (Boston), 2003. (PDF, 230 KB).

[12a]
W. Maass, T. Natschläger, and H. Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11):2531-2560, 2002. (PDF, 886 KB).

[12b]
T. Natschläger, W. Maass, and H. Markram. The "liquid computer": A novel strategy for real-time computing on time series. Special Issue on Foundations of Information Processing of TELEMATIK, 8(1):39-43, 2002. (PDF, 277 KB).

[12c]
W. Maass, T. Natschläger, and H. Markram. Fading memory and kernel properties of generic cortical microcircuit models. Journal of Physiology -- Paris, 98(4-6):315-330, 2004. (PDF, 576 KB).

[11]
T. Natschläger and W. Maass. Spiking neurons and the induction of finite state machines. Theoretical Computer Science: Special Issue on Natural Computing, 287:251-265, 2002. (PDF, 311 KB).

[10]
W. Maass, A. Pinz, R. Braunstingl, G. Wiesspeiner, T. Natschläger, O. Friedl, and H. Burgsteiner. Konstruktion von Lernfähigen Robotern im Studentenwettbewerb ``Robotik 2000'' an der Technischen Universität Graz. in: Telematik, pages 20-24, 2000. (Gzipped PostScript, 8 p., 83 KB). (PDF, 178 KB).

[9a]
T. Natschläger and W. Maass. Computing the optimally fitted spike train for a synapse. Neural Computation, 13(11):2477-2494, 2001. (Gzipped PostScript, 15 p., 203 KB). (PDF, 288 KB).

[9b]
T. Natschläger and W. Maass. Finding the key to a synapse. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Advances in Neural Information Processing Systems (NIPS '2000), volume 13, pages 138-144, Cambridge, 2001. MIT Press. (Gzipped PostScript, 7 p., 66 KB). (PDF, 124 KB). The poster presented at NIPS is available as Acrobat PDF file.

[8a]
T. Natschläger, W. Maass, and A. Zador. Efficient temporal processing with biologically realistic dynamic synapses. Network: Computation in Neural Systems, 12:75-87, 2001. (Gzipped PostScript, 14 p., 109 KB). (PDF, 185 KB).

[8b]
T. Natschläger, W. Maass, E. D. Sontag, and A. Zador. Processing of time series by neural circuits with biologically realistic synaptic dynamics. In Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors, Advances in Neural Information Processing Systems 2000 (NIPS '2000), volume 13, pages 145-151, Cambridge, 2001. MIT Press. (Gzipped PostScript, 7 p., 60 KB). (PDF, 133 KB). The poster presented at NIPS is available as Acrobat PDF file.

[7]
T. Natschläger. Efficient Computation in Networks of Spiking Neurons -- Simulations and Theory. PhD thesis, Graz University of Technology, 1999. (Gzipped PostScript, 116 p., 1289 KB).

[6a]
W. Maass and T. Natschläger. A model for fast analog computation based on unreliable synapses. Neural Computation, 12(7):1679-1704, 2000. (Gzipped PostScript, 26 p., 211 KB). (PDF, 407 KB).

[6b]
T. Natschläger and W. Maass. Fast analog computation in networks of spiking neurons using unreliable synapses. In ESANN'99 Proceedings of the European Symposium on Artificial Neural Networks, pages 417-422, Bruges, Belgium, 1999. (Gzipped PostScript, 6 p., 79 KB). (PDF, 180 KB).

[5a]
T. Natschläger and B. Ruf. Spatial and temporal pattern analysis via spiking neurons. Network: Computation in Neural Systems, 9(3):319-332, 1998. (Gzipped PostScript, 15 p., 179 KB).

[5b]
T. Natschläger and B. Ruf. Online clustering with spiking neurons using temporal coding. In L. S. Smith and A. Hamilton, editors, Neuromorphic Systems: Engineering Silicon from Neurobiology, pages 33-42. World Scientific, 1998. (Gzipped PostScript, 10 p., 92 KB).

[5c]
T. Natschläger and B. Ruf. Pattern analysis with spiking neurons using delay coding. Neurocomputing, 26-27(1-3):463-469, 1999. (Gzipped PostScript, 7 p., 87 KB).

[5d]
T. Natschläger, B. Ruf, and M. Schmitt. Unsupervised learning and self-organization in networks of spiking neurons. In U. Seiffert and L. C. Jain, editors, Self-Organizing Neural Networks. Recent Advances and Applications, volume 78 of Springer Series on Studies in Fuzziness and Soft Computing. Springer-Verlag, Heidelberg, 2001. in press.

[4a]
W. Maass and T. Natschläger. Networks of spiking neurons can emulate arbitrary Hopfield nets in temporal coding. Network: Computation in Neural Systems, 8(4):355-372, 1997. (Gzipped PostScript, 19 p., 188 KB). (PDF, 364 KB).

[4b]
W. Maass and T. Natschläger. Emulation of Hopfield networks with spiking neurons in temporal coding. In J. M. Bower, editor, Computational Neuroscience: Trends in Research, pages 221-226. Plenum Press, 1998. (Gzipped PostScript, 7 p., 82 KB). (PDF, 187 KB).

[4c]
W. Maass and T. Natschläger. Associative memory with networks of spiking neurons in temporal coding. In L. S. Smith and A. Hamilton, editors, Neuromorphic Systems: Engineering Silicon from Neurobiology, pages 21-32. World Scientific, 1998. (Gzipped PostScript, 13 p., 103 KB). (PDF, 253 KB).

[3a]
T. Natschläger. Netzwerke von Spiking Neuronen: Die dritte Generation von Modellen für neuronale Netzwerke. In Jenseits von Kunst. Passagen Verlag, 1996. (Gzipped PostScript, 10 p., 194 KB). Online version

[3b]
T. Natschläger. Networks of spiking neurons: A new generation of neural network models. In Jenseits von Kunst. Passagen Verlag, 1998. (Gzipped PostScript, 211 KB). Online version

[2]
T. Natschläger. Raum- zeitliche Strukturen von Berechnungen in biologisch realistischen neuronalen Netzwerken. Master's thesis, Technische Universität Graz, February 1996. (Gzipped PostScript, 129 p., 675 KB).

[1]
T. Natschläger and M. Schmitt. Exact VC-Dimension of boolean monomials. Information Processing Letters, 59:19-20, 1996. (Gzipped PostScript, 4 p., 41 KB).