direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Page Content

All Publications


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

Onken, A., Dragoi, V. and Obermayer, K. (2012). A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts. PLoS Computational Biology, 8

Donner, C., Obermayer, K. and Shimazaki, H. (2017). Approximate Inference for Time-varying Interactions and Macroscopic Dynamics of Neural Populations. PLoS Computational Biology, 13

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

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

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.


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

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.,


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

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions