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TU Berlin

Inhalt des Dokuments

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Grünewälder, S. and Obermayer, K. (2005). Attention Driven Memory. Proceedings of the 27th Annual Conf. of the Cognitive Science Society, 845 – 850.,


Guggenmos, M., Rothkirch, M., Obermayer, K., Haynes, J. D. and Sterzer, P. (2015). A Hippocampal Signature of Perceptual Learning in Object Recognition. Journal of Cognitive Neuroscience, 27, 787–797.


Guo, R., Böhmer, W., Hebart, M., Chien, S., Sommer, T., Obermayer, K. and Gläscher, J. (2016). Interaction of Instrumental and Goal-directed Learning Modulates Prediction Error Representations in the Ventral Striatum. Journal of Neuroscience, 36, 12650-12660.


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Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Data Selection for Fuzzy Topographic Mapping of Proximities. Fuzzy-Neuro Systems 1999 - Computational Intelligence, 93–104.,


Hasenjäger, M., Ritter, H. and Obermayer, K. (2000). Active Data Selection for Topographic Pairwise Clustering. Classification, Automation, and New Media. Program of the 24th Annual Conference of the German Classification Society (GfKl), 80.,


Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Topographic Mapping of Proximities. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 952 – 957.,10.1049/cp:19991235


Hasenjäger, M., Ritter, H. and Obermayer, K. (1999). Active Learning in Self-Organizing Maps. Kohonen Maps. Elsevier, 57–70.,


Heinz, A., Beck, A., Wrase, J., Mohr, J., Obermayer, K., Gallinat, J. and Puls, I. (2009). Neurotransmitter Systems in Alcohol Dependence. Pharmacopsychiatry, 42, 95 – 101.


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.


Henrich, F. and Obermayer, K. (2008). Active Learning by Spherical Subdivision. Journal of Machine Learning Research, 9, 105 – 130.


Herbrich, R., Graepel, T., Bollmann-Sdorra, P. and Obermayer, K. (1998). Learning a Preference Relation for Information Retrieval. Working Notes of the AAAI Workshop on Information Retrieval 98, 83 – 86.,


Herbrich, R., Graepel, T. and Obermayer, K. (2000). Large Margin Rank Boundaries for Ordinal Regression. Advances in Large Margin Classifiers. MIT Press, 115 – 132.,


Herbrich, R., Graepel, T. and Obermayer, K. (1999). Support Vector Learning for Ordinal Regression. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 97 – 102.,10.1049/cp:19991091


Herbrich, R., Keilbach, M., Graepel, T., Bollmann-Sdorra, P. and Obermayer, K. (1999). Neural Networks in Economics: Background, Applications and New Developments. Advances in Computational Economics: Computational Techniques for Modelling Learning in Economics. Kluwer Academics, 169 – 196.,


Hiesinger, P., Scholz, M., Meinertzhagen, I., Fischbach, K.-F. and Obermayer, K. (2001). Visualization of Synaptic Markers in the Optic Neuropils of Drosophila Using a New Constrained Deconvolution Method. Journal of Comparative Neurology, 429, 277 – 288.


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.


Hoch, T., Wenning, G. and Obermayer, K. (2005). The Effect of Correlations in the Background Activity on the Information Transmission Properties of Neural Populations. Journal of Neurocomputing, 65-66, 365 – 370.


Hoch, T., Wenning, G. and Obermayer, K. (2003). Adaptation using Local Information for Maximizing the Global Cost. Neurocomputing, 52 – 54, 467-472.


Hoch, T., Wenning, G. and Obermayer, K. (2003). Optimal Noise-Aided Signal Transmission through Populations of Neurons. Physical Review E, 68, 11911.


Hochreiter, J. and Obermayer, K. (2006). Support Vector Machines for Dyadic Data. Neural Comput., 18, 1472 – 1510.


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