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Blasdel, G. and Obermayer, K. (1994). Putative Strategies of Scence Segmentation Suggested by Patterns of Orientation Preference in Monkey Visual Cortex [23]. Neural Networks, 7, 865 – 881.

Blasdel, G. and Obermayer, K. (1994). Strategies of Scene Segmentation Suggested by Patterns of Orientation Preference in Monkey Visual Cortex [24]. Neur. Netw., 7, 865 – 881.

Blasdel, G., Obermayer, K. and Kiorpes, L. (1995). Organization of Ocular Dominance and Orientation Columns in the Striate Cortex of Neonatal Macaque Monkeys [25]. Visual Neuroscience, 12, 589 – 603.

Boehmer, W., Guo, R. and Obermayer, K. (2016). Non-deterministic Policy Improvement Stabilizes Approximate Reinforcement Learning [26]. Proceedings of the 13th European Workshop on Reinforcement Learning

Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2011). Regularized Sparse Kernel Slow Feature Analysis [27]. Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, 235–248.,

Böhmer, W., Grünewalder, S., Nickisch, H. and Obermayer, K. (2012). Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis [28]. Machine Learning, 89, 67–86.

Brunner, K., Kussinger, M., Stetter, M. and E.W., L. (1998). A Neural Network Model for the Emergence of Grating Cells [29]. Biological Cybernetics, 78, 389 – 397.

Bucher, D., Scholz, M., Stetter, M., Obermayer, K. and Pflüger, H.-J. (2000). Corrections Methods for Three-dimensional Reconstructions from Confocal Images: I. Tissue Shrinking and Axial Scaling [30]. Journal of Neuroscience Methods, 100, 135 – 143.

Burger, M., Graepel, T. and Obermayer, K. (1997). Phase Transitions in Soft Topographic Vector Quantization [31]. Artificial Neural Networks - ICANN 97. Springer-Verlag, 619 – 624.,

Burger, M. a. G. T. and Obermayer, K. (1998). An Annealed Self-Organizing Map for Source Channel Coding [32]. Advances in Neural Information Processing Systems 10. MIT Press, 430 – 436.,

Böhmer, W., Springenberg, J. T., Boedecker, J., Riedmiller, M. and Obermayer, K. (2015). Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations [33]. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.,10.1007/s13218-015-0356-1

Böhmer, W., Grünewälder, S., Shen, Y., Musial, M. and Obermayer, K. (2013). Construction of Approximation Spaces for Reinforcement Learning [34]. Journal of Machine Learning Research, 14, 2067–2118.

Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions [35]. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.,10.1007/978-3-319-23528-8_8

Böhmer, W. and Obermayer, K. (2013). Towards Structural Generalization: Factored Approximate Planning [36]. ICRA Workshop on Autonomous Learning


Cakan, C., Dimulescu, C., Khakimova, L., Obst, D., Flöel, A. and Obermayer, K. (2022). Spatiotemporal patterns of adaptation-induced slow oscillations in a whole-brain model of slow-wave sleep [37]. Frontiers in Computational Neuroscience, 15, 800101.

Cakan, C. and Obermayer, K. (2020). Biophysically grounded mean-field models of neural populations under electrical stimulation [38]. PLOS Computational Biology, 2020

Cakan, C., Jajcay, N. and Obermayer, K. (2021). neurolib: A Simulation Framework for Wholebrain Neural Mass Modeling [39]. Cognit. Comput., 2021

Chouzouris, T., Roth, N., Cakan, C. and K., O. (2021). Applications of Optimal Nonlinear Control to a Whole-brain Network of FitzHugh-Nagumo Oscillators [40]. Phys. Rev. E, 2021

Cuadros-Vargas, E., Romero, R. and Obermayer, K. (2003). Speeding up Algorithms of the SOM Family for Large and High Dimensional Databases [41]. Proceedings WSOM, 167 – 172.,


Dai, Z. and Lücke, J. (2014). Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts [42]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1950–1962.

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