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Inhalt des Dokuments

All Publications


Jain, B. and Obermayer, K. (2011). Graph Quantization. J. Comput. Vision Image Understanding, 115, 946–961.

Natora, M. and Obermayer, K. (2011). An Unsupervised and Drift-adaptive Spike Detection Algorithm Based on Hybrid Blind Beamforming. EURASIP J. Adv. Signal Processing 2011, 2011

Neubauer, N. and Obermayer, K. (2011). Tripartite community structure in social bookmarking data. New Review of Hypermedia and Multimedia, 17, 267-294.

Srinivasan, D. and Obermayer, K. (2011). Probabilistic prototype models for attributed graphs. ,

Hoch, T., Volgushev, S., Malyshev, A., Obermayer, K. and Volgushev, M. (2011). Modulation of the Amplitude of γ-Band Activity by Stimulus Phase Enhances Signal Encoding. European Journal of Neuroscience, 33, 1223-1239.

Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2011). Regularized Sparse Kernel Slow Feature Analysis. Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, 235–248.,

Grünwälder, S. and Obermayer, K. (2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD and MC. Machine Learning, 83, 289 – 330.


Ladenbauer, J., Augustin, M., Shiau, L. and Obermayer, K. (2012). Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons. PLoS Computational Biology, 8

Onken, A., Dragoi, V. and Obermayer, K. (2012). A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts. PLoS Computational Biology, 8

Seo, S., Mohr, J., Heekeren, H., Heinz, A., Eppinger, B., Li, S. and Obermayer, K. (2012). A voxel selection method for the multivariate analysis of imaging genetics data.. IJCNN. IEEE, 2884-2890.,10.1109/IJCNN.2012.6252766

Adiloglu, K., Annies, R., Wahlen, E., Purwins, H. and Obermayer, K. (2012). A Graphical Representation and Dissimilarity Measure for Basic Everyday Sound Events. IEEE Transactions on Audio, Speech, and Language Processing, 1542 - 1552.

Böhmer, W., Grünewalder, S., Nickisch, H. and Obermayer, K. (2012). Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Machine Learning, 89, 67–86.


Houillon, A., Lorenz, R. C., Boehmer, W., Rapp, M. A., Heinz, A., Gallinat, J. and Obermayer, K. (2013). The effect of novelty on reinforcement learning. Progress in brain research, 202, 415–439.

Ladenbauer, J., Lehnert, J., Rankoohi, H., Dahms, T., Schöll, E. and Obermayer, K. (2013). Adaptation Controls Synchrony and Cluster States of Coupled Threshold-Model Neurons. Physical Review E, 88, 042713.

Shen, Y., Stannat, W. and Obermayer, K. (2013). Risk-sensitive Markov Control Processes. SIAM Journal on Control and Optimization, 51, 3652–3672.

Spanagel, R., Durstewitz, D., Hansson, A., Heinz, A., Kiefer, F., Köhr, G., Matthäus, F., Nöthen, M. M., Noori, H. R., Obermayer, K., Rietschel, M., Schloss, P., Scholz, H., Schumann, G., Smolka, M., Sommer, W., Vengeliene, V., Walter, H., Wurst, W., Zimmermann, U. S., Group, A. G. R., Stringer, S., Smits, Y. and Derks, E. M. (2013). A systems medicine research approach for studying alcohol addiction. Addiction Biology, 18, 883–896.

Augustin, M., Ladenbauer, J. and Obermayer, K. (2013). How Adaptation Shapes Spike Rate Oscillations in Recurrent Neuronal Networks. Front. Comput. Neurosci., 7

Böhmer, W. and Obermayer, K. (2013). Towards Structural Generalization: Factored Approximate Planning. ICRA Workshop on Autonomous Learning

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