Peer Reviewed Papers
2004
Körding, KP. and Wolpert, D. (2004) Bayesian Integration in Sensorimotor Learning, Nature 427:244-247 [pdf]
Körding, KP, Kayser, C., Einhäuser, W. and König,P., How are complex cell properties adapted to the statistics of natural scenes? Journal of Neurophysiology 91(1):206-212[pdf]
Körding, KP. and Wolpert, D. (2003) Bayesian Integration with Multimodal priors, NIPS [pdf]
Betsch, B, Einhäuser, W., Körding, KP and König, P. (2003) Biological Cybernetics, in press
2003
Hafner, V. V., Fend, M., Lungarella, M., Pfeifer, R., König, P., Körding, K. P. (2003), Optimal coding for naturally occurring whisker deflections, Proceedings of the Joint International Conference on Artificial Neural Networks and Neural Information Processing (ICANN/ICONIP), Springer Lecture Notes in Computer Science, pp. 805-812, ISSN 0302-9743, ISBN 3-540-40409-2, Istanbul
Einhäuser, W., Kayser, C., Körding, K.P. and König,P. (2003) Learning distinct and complementary feature-selectivities from natural colour videos. Reviews in the Neurosciences 14, p. 43-52, 2003. [pdf]
Klein, D.J., König, P. and Körding, K.P. (2003) Sparse spectrotemporal coding of sounds. EURASIP Journal of Applied Signal Processing, [pdf]
Körding, K.P., Kayser, C. and König, P. (2003) On the choice of a sparse prior, Reviews in the Neurosciences 14, p. 53-62, 2003
Kayser C, Körding KP, König P. (2003). Learning the nonlinearity of neurons from natural visual stimuli. Neural Computation. 15(8) 1751-1759. [pdf]
2002
Einhäuser,W. Kayser,C. Körding K.P. and König,P. Learning Multiple Feature Representations from Natural Image Sequences Artificial Neural Networks - ICANN 2002, Springer Verlag Berlin Heidelberg New York. [pdf]
Einhäuser, W., Kayser, C. , König, P. and Körding, K.P., Learning the invariance properties of complex cells from natural stimuli. (Eur J Neurosci 2002 Feb;15(3):475-86) [pdf]
Konrad P. Körding, Peter König, and David J. Klein (2002) Learning of sparse auditory receptive fields (IJCNN) [pdf]
2001
Körding, K.P., Kayser C., Betsch, B. and König, P., (2001) Non contact eye-tracking on cats. (J Neurosci Methods. Sep 30;110:103-111) [pdf]
Körding, K.P. and König, P., (2001) A spike based learning rule for the generation of invariant representations. ,Journal of Physiology Paris 94:539-548[pdf]
Körding, K.P. and König,P., (2001) Neurons with two sites of synaptic integration learn invariant representations. (Neural Computation 13:2823-2849) [pdf]
Kayser, C., Einhäuser, W., Dümmer, O., König, P. and Körding, K.P., (2001) Extracting slow subspaces from natural videos leads to complex cells. (International conference on artificial neural networks) [pdf]
Körding, K.P. and König ,P. , (2001) Supervised and unsupervised learning with two sites of synaptic integration. (Journal of Computational Neuroscience11:207-215) [pdf]
2000
Körding, K.P. and König, P., (2000) Two sites of synaptic integration: Relevant for learning (International Joint Conference on Neural Networks ) [pdf]
Körding, K.P. and König,P.,(2000) A learning rule for local decorrelation and dynamic recruitement (Neural Networks 13:1-9) [pdf]
Siegel,M. Körding,K.P. and König, P. (2000) Integrating top-down and bottom-up sensory processing by somato-dendritic interatctions (J. Comp. Neurosci 8:161-173)[pdf]
Konrad P. Körding and Peter König, (2000) Learning with two sites of synaptic integration (Network: Computation in Neural Systems 11:25-39) [pdf]
Under Revision
Learning a hierarchical model of cortical function from natural stimuli
How does sparse coding lead to localized receptive fields?
Miscellaneous
Radioplay 45 min in German Language. "Reparatur". Christine Abbt, Doris Agotai, Jeanette Behringer, Silvia Berger, Konrad Paul Körding, Celina Ramjoue, Kaspar Schatzmann, Jair Stern. Played at the "end-of-term" celebration of the Collegium Helveticum. Part of the year-book. Sent on radio LORA Munich on June 20th 2003 along with a live interview.Invited Talks
FIL, London, 2004, the utility function of human sensorimotor processing
NIC, Netherlands, 2004, Bayesian processing in the human sensorimotor system
MIT, 2004, Optimality criteria of the human sensorimotor system.
Cold Spring Harbor,2004, Towards a Bayesian Nose
Combloux, 2004, The human sensorimotor system in a Bayesian framework
GATSBY, London, 2003, The sensorimotor system uses the Bayes rule
Cambridge, 2003, Bayesian integration in the sensorimotor system
Plymouth, 2003,Learning from the real world - one algorithm for visual and auditory stimuli.
Oldenburg, 2003,Sparse Coding of speech data predicts properties of the auditory system
GATSBY, London, 2003, Learning hierarchical representations from videos of natural scenes
Center for Neuroscience, Davis CA, 2002, Complex Cells, A question of time
Asilomar, 2002, Complex Cells, optimality to natural scenes
Wolpert Lab 2002, Optimality of Complex Cells
Thesis Defense, Zuerich, 2001, Optimality and Learning, From Microscopic cell properties to natural scenes
Seung Lab MIT, 2000, Learning Invariant Representations
Brown University, 2000, Invariant Representations and the Binding Problem
Cold Spring Harbor Lab , 2000, Complex Cells emerge from natural Videos
Banbury Meeting : Statistics of Natural Scenes , 2000, What cats see and what networks can learn from this
Heidelberg Max Planck Institute for Medical Research, 2000, The significance of two sites of synaptic integration
GATSBY and University College London, 2000, Physiologically realistic mechanisms for learning
ITB Berlin,1999, Significance of two sites of synaptic integration
