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Inhalt des Dokuments



Henniges, M., Turner, R. E., Sahani, M., Eggert, J. and Lücke, J. (2014). Efficient Occlusive Components Analysis. Journal of Machine Learning Research, 15, 2689–2722.


Pröpper, R. and Obermayer, K. (2013). Spyke Viewer: a flexible and extensible platform for electrophysiological data analysis. Frontiers in Neuroinformatics, 7, 1–10.

Böhmer, W., Grünewälder, S., Shen, Y., Musial, M. and Obermayer, K. (2013). Construction of Approximation Spaces for Reinforcement Learning. Journal of Machine Learning Research, 14, 2067–2118.

Schmidt, S., Scholz, M., Obermayer, K. and Brandt, S. A. (2013). Patterned Brain Stimulation, What a Framework with Rhythmic and Noisy Components Might Tell Us about Recovery Maximization. Frontiers in human neuroscience, 7

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

Proepper, R., Munk, M. and Obermayer, K. (2013). Memory load modulates spiking activity in prefrontal cortex. ,10.12751/nncn.bc2013.0180

Xie, S., Zhu, F., Obermayer, K., Ritter, P. and Wang, L. (2013). An Efficient Spatial Selective Visual Attention Pattern Recognition Method Based on Joint Short SSVEP. ,10.1109/IJCNN.2013.6706872


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.


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.

Jain, B. and Obermayer, K. (2011). Maximum Likelihood for Gaussians on Graphs. Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 62-71.,10.1007/978-3-642-20844-7_7

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