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Journal Publications

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


Oschmann, F., Mergenthaler, K., Jungnickel, E. and Obermayer, K. (2017). Spatial Separation of Two Different Pathways Accounting for the Generation of Calcium Signals in Astrocytes. PLoS Computational Biology, 13


Wimmer, K., Hildebrandt, K., Henning, R. and Obermayer, K. (2008). Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2. PLoS Computational Biology, 4


Augustin, M., Ladenbauer, J., Baumann, F. and Obermayer, K. (2017). Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation. PLoS Computational Biology, 13


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.


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.


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


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


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.


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.


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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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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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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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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.


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.


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