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Inhalt des Dokuments

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Schwabe, L. and Obermayer, K. (2005). Learning Top-Down Gain Control in a Recurrent Network Model of a Visual Cortical Area. Vision Research, 45, 3202 – 3209.


Schwabe, L. and Obermayer, K. (2005). Adaptivity of Tuning Functions in a Generic Recurrent Network Model of a Cortical Hypercolumn. Journal of Neuroscience, 25, 3323 – 3332.


Schwabe, L. and Obermayer, K. (2003). Modelling the Adaptive Visual System: A Survey of Principled Approaches. Neur. Netw., 16, 1353 – 1371.


Schwabe, L. and Obermayer, K. (2002). Rapid Adaptation and Efficient Coding. Biosystems, 67, 239 – 244.


Schwabe, L., Obermayer, K., Angelucci, A. and Bressloff, P. (2006). The Role of Feedback in Shaping the Extra-Classical Receptive Field of Cortical Neurons: A Recurrent Network Model. Journal of Neuroscience, 26, 9117 – 9129.


Schwetz, I., Gruhler, G. and Obermayer, K. (2006). A Cross-Spectrum Weighting Algorithm for Speech Enhancement and Array Processing: Combining Locating and Long-Term Statistics. J. Acoust. Soc. Amer., 119, 952 – 964.


Schwetz, I., Gruhler, G. and Obermayer, K. (2004). Stationarity of Speech Radiation: Consequences for Linear Multichannel Filtering. IEEE Transactions on Speech and Audio Processing ., 12, 460 – 467.


Schöner, H., Stetter, M., Schiessl, I., Mayhew, J., Lund, J., McLoughlin, N. and Obermayer, K. (1999). Blind Separation of Noisy Mixtures by Iterative Decorrelation.. Proceedings. The Learning Workshop, Snowbird, USA, (1 – 2).,


Schöner, H., Stetter, M., Schießl, I., Mayhew, J., Lund, J., McLoughlin, N. and Obermayer, K. (2000). Application of Blind Separation of Sources to Optical Recording of Brain Activity. Advances in Neural Information Processing Systems 12. MIT Press, 949 – 955.,


Seidel, R., Jahn, N., Seo, S., Goerttler, T. and Obermayer, K. (2022). NAPC: A Neural Algorithm for Automated Passenger Counting in Public Transport on a Privacy-Friendly Dataset. IEEE Open Journal of Intelligent Transportation Systems, 3, 33-44.


Seo, S., Beck, A., Matthis, C., Genauck, A., Banaschewski, T., Bokde, A., Bromberg, U., Büchel, C., Quinlan, E., Flor, H., Frouin, V., Garavan, H., Gowland, P., Ittermann, B., Martinot, J., Martinot, M., Nees, F., Orfanos, D., Poustka, L., Hohmann, S., Froehner, J., Smolka, M., Walter, H., Whelan, R., Desrivieres, S., Heinz, A., Schumann, G. and Obermayer, K. (2019). Risk Profiles for Heavy Drinking in Adolescence: Differential Effects of Gender. Addiction Biology, 24, 787-801.


Seo, S., Mohr, J., Beck, A., Wüstenberg, T., Heinz, A. and Obermayer, K. (2015). Predicting the future relapse of alcohol-dependent patients from structural and functional brain images. Addiction Biology, 20, 1042-1055.


Seo, S., Bode, M. and Obermayer, K. (2003). Soft Nearest Prototype Classification. IEEE Transactions on Neural Networks, 14, 390 – 398.


Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.,10.1109/IJCNN.2015.7280637


Seo, S., Mohr, J. and Obermayer, K. (2009). A New Incremental Pairwise Clustering Algorithm. Proceedings of the ICMLA -09: The Eighth International Conference on Machine Learning and Applications. IEEE, 223 – 228.,10.1109/ICMLA.2009.42


Seo, S. and Obermayer, K. (2006). Dynamic Hyperparameter Scaling Method for LVQ Algorithms. IJCNN 2006 Conference Proceedings. IEEE, 3196 – 3203.,10.1109/IJCNN.2006.247304


Seo, S. and Obermayer, K. (2004). Self-Organizing Maps and Clustering Methods for Matrix Data. Neural Networks Special Issue, 17, 1211 – 1229.


Seo, S. and Obermayer, K. (2003). Soft Learning Vector Quantization. Neural Computation, 15, 1589 – 1604.


Seo, S., Wallat, M., Graepel, T. and Obermayer, K. (2000). Gaussian Process Regression: Active Data Selection and Test Point Rejection. Neural Networks - IJCNN 2000. IEEE, 241 – 246.,10.1109/IJCNN.2000.861310


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


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