Stefan Klampfl online

Publications


[10]		S. Klampfl, W. Maass. Emergence of assembly coding in generic cortical microcircuit models through STDP. In preparation.

[9]		S. Klampfl, S. V. David, P. Yin, S. A. Shamma, W. Maass. A quantitative analysis of information about past and present stimuli encoded by spikes of A1 neurons. Submitted for publication, 2011.

[8]		S. Klampfl. Computation and Learning in Biological Networks of Neurons: Theoretical Analysis, Computer Simulations, and Analysis of Experimental Data. PhD thesis, Institute for Theoretical Computer Science, Graz University of Technology, 2011.

[7]		S. Klampfl, W. Maass. A theoretical basis for emergent pattern discrimination in neural systems through slow feature extraction. Neural Computation, 22(12):2979-3035, 2010 (Link, PDF, 2.812 KB).

[6]		S. Klampfl, W. Maass. Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks. In Proc. of NIPS 2009, Advances in Neural Information Processing Systems, volume 22, pp. 988-996. MIT Press, 2010 (PDF, 1.656 KB).

[5]		S. Klampfl, S. V. David, P. Yin, S. A. Shamma, W. Maass. Integration of stimulus history in information conveyed by neurons in primary auditory cortex in response to tone sequences. Program No. 163.8. 2009 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience, 2009. Online. (Link).

[4]		S. Klampfl, R. Legenstein, and W. Maass. Spiking neurons can learn to solve information bottleneck problems and to extract independent components. Neural Computation, 21(4):911-959, 2009 (Link, PDF, 1.071 KB).

[3]		S. Klampfl, R. Legenstein, and W. Maass. Information bottleneck optimization and independent component extraction with spiking neurons. In Proc. of NIPS 2006, Advances in Neural Information Processing Systems, volume 19, pp. 713-720. MIT Press, 2007 (PDF, 599 KB).

[2]		S. Klampfl. Extracting statistically independent components with a generalized BCM rule for spiking neurons. Diploma thesis, Institute for Theoretical Computer Science, Graz University of Technology, 2006.

[1]		W. Hribernik, S. Klampfl. A C++ Signal Processing Class Library for Power System Transients. In Proc. IASTED International Conference on Power and Energy Systems, Tampa, FL, Nov. 2001, pp. 213-218.

Conference & Invited Talks*

Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks, Dec 7, 2009, NIPS 2009 Spotlight presentation, Vancouver, BC, Canada
Computing with transient network dynamics, Dec 11, 2008, EPSRC Symposium Workshop on Computational Neuroscience, Mathematics Research Centre, University of Warwick, United Kingdom
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons, Feb 12, 2007, Computational Neuroscience Seminar, WS 2006/07, Institute for Theoretical Biology, Humboldt-University Berlin, Germany

Posters*

Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks, Dec 7, 2009, NIPS 2009, Vancouver, BC, Canada
Integration of stimulus history in information conveyed by neurons in primary auditory cortex in response to tone sequences, Oct 18, 2009, SfN 2009, Chicago, IL, United States
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons, Dec 5, 2006, NIPS 2006, Vancouver, BC, Canada
Extracting Independent Components with Spiking Neurons, Oct 23, 2006, CoVi Hearing, FWF Vienna, Austria

Conferences

NIPS 2009, Dec 7 - Dec 12, 2009, Vancouver, BC, Canada
SfN 2009, Oct 17 - Oct 21, 2009, Chicago, IL, United States
EPSRC Symposium Workshop on Computational Neuroscience, Dec 8 - Dec 11, 2008, University of Warwick, Coventry, United Kingdom
NIPS 2006, Dec 4 - Dec 9, 2006, Vancouver, BC, Canada

* Access to PDFs may be restricted