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Müller, L., Ploner, M., Goerttler, T. and Obermayer, K. (2021). An Interactive Introduction to Model-Agnostic Meta-Learning. Workshop on Visualization for AI Explainability at IEEE VIS


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Adorjan, P., Levitt, J., Lund, J. and Obermayer, K. (1999). A Model for the Intracortical Origin of Orientation Preference and Tuning in Macaque Striate Cortex. Visual Neuroscience, 16, 303 – 318.


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.


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.


Bauer, U., Scholz, M., Levitt, J., Obermayer, K. and Lund, J. (1999). A Model for the Depth-Dependence of Receptive Field Size and Contrast Sensitivity of Cells in Layer 4C of Macaque Striate Cortex. Vision Research, 39, 613 – 629.


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Kneer, F., Obermayer, K. and Dahlem, M. A. (2015). Analyzing critical propagation in a reaction-diffusion-advection model using unstable slow waves. The European Physical Journal E, 38


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Shen, Y., Stannat, W. and Obermayer, K. (2013). Risk-sensitive Markov Control Processes. SIAM Journal on Control and Optimization, 51, 3652–3672.


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Adorjan, P. and Obermayer, K. (1999). Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability. Advances in Neural Information Processing Systems 11. MIT Press.,


Adorjan, P., Piepenbrock, C. and Obermayer, K. (1999). Contrast Adaptation and Infomax in Visual Cortical Neurons. Review Neuroscience, 10, 181 – 200.


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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.


Obermayer, K., Ritter, H. and Schulten, K. (1990). A Principle for the Formation of the Spatial Structure of Cortical Feature Maps. Proceedings of the National Academy of Sciences of the United States of America, 8345 – 8349.


Normann, I., Purwins, H. and Obermayer, K. (2001). Octave Ambigous Tones. Proceedings of the International Computer Music Conference 2001


Liu, C., Xie, S., Xie, X., Duan, X., Wang, W. and Obermayer, K. (2018). Design of a Video Feedback SSVEP-BCI System for Car Control Based on the Improved MUSIC Method. Proceedings of the IEEE 6th International Winter Conference on Brain-Computer Interfaces


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


Koren, V., Andrei, A., Hu, M., Dragoi, V. and Obermayer, K. (2019). Reading-out task variables as a low-dimensional reconstruction of parallel spike trains in single trials. PLoS ONE, 14(10), 24.


Shelton, J. A., Sheikh, A.-S., Bornschein, J., Sterne, P. and Lücke, J. (2015). Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding. PLoS ONE, 10, e0124088.


Aspart, F., Ladenbauer, J. and Obermayer, K. (2016). Extending Integrate-and-fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite. PLOS Computional Biology, 12, e1005206.


Donner, C., Obermayer, K. and Shimazaki, H. (2016). Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations. PLoS Computional Biology, 13, 1 -27.


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



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