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B


Blasdel, G. and Obermayer, K. (1994). Strategies of Scene Segmentation Suggested by Patterns of Orientation Preference in Monkey Visual Cortex. 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. Visual Neuroscience, 12, 589 – 603.


Boehmer, W., Guo, R. and Obermayer, K. (2016). Non-deterministic Policy Improvement Stabilizes Approximate Reinforcement Learning. 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. 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. 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. 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. Journal of Neuroscience Methods, 100, 135 – 143.


Burger, M., Graepel, T. and Obermayer, K. (1997). Phase Transitions in Soft Topographic Vector Quantization. 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. Advances in Neural Information Processing Systems 10. MIT Press, 430 – 436.,10.1.1.26.9359


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. 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. Journal of Machine Learning Research, 14, 2067–2118.


Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions. 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. ICRA Workshop on Autonomous Learning


C

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. Frontiers in Computational Neuroscience, 15, 800101.


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


Cakan, C., Jajcay, N. and Obermayer, K. (2021). neurolib: A Simulation Framework for Wholebrain Neural Mass Modeling. 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. 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. Proceedings WSOM, 167 – 172.,


D

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


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