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TU Berlin

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Journal Publications

2017

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


2016

Guo, R., Böhmer, W., Hebart, M., Chien, S., Sommer, T., Obermayer, K. and Gläscher, J. (2016). Interaction of Instrumental and Goal-directed Learning Modulates Prediction Error Representations in the Ventral Striatum. Journal of Neuroscience, 36, 12650-12660.


Mohr, J., Seyfarth, J., Lueschow, A., Weber, J. E., Wichman, F. A. and Obermayer, K. (2016). BOiS - Berlin Object in Scene Database: Controlled Photographic Images for Visual Search Experiments with Quantified Contextual Priors. Frontiers in Psychology, 7


Xie, S., Liu, C., Obermayer, K., Zhu, F., Wang, L., Xie, X. and Wang, W. (2016). Stimulator Selection in SSVEP Based Spatial Selective Attention Study. Comput. Intelligence Neurosci., 2016


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


Meyer, R. and Obermayer, K. (2016). pypet: A Python Toolkit for Data Management of Parameter Explorations. Frontiers Neuroinformatics, 10


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.


Kanev, J., Koutsou, A., Christodoulou, C. and Obermayer, K. (2016). Integrator or Coincidence Detector - a Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode. Neural Computation, 28, 1-38.


Tobia, M. J., Guo, R., Gläscher, J., Schwarze, U., Brassen, S., Büchel, C., Obermayer, K. and Sommer, T. (2016). Altered behavioral and neural responsiveness to counterfactual gains in the elderly. Cognitive, Affective, & Behavioral Neuroscience, 457-472.


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.


Huys, Q., Deserno, L., Obermayer, K., Schlagenhauf, F. and Heinz, A. (2016). Model-free temporal-difference learning and dopamine in alcohol dependence: examining concepts from theory and animals in human imaging. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 1, 401 - 410.


Bielivtsov, D., Ladenbauer, J. and Obermayer, K. (2016). Controlling Statistical Moments of Stochastic Dynamical Networks. Physical Review E, 94, 012306.


2015

Seo, S., Mohr, J., Beck, A., Wüstenberg, T., Heinz, A. and Obermayer, K. (2015). Predicting the future relapse of alcohol-dependent patients from structural and functional brain images. Addiction Biology, 20, 1042-1055.


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.


Guggenmos, M., Rothkirch, M., Obermayer, K., Haynes, J. D. and Sterzer, P. (2015). A Hippocampal Signature of Perceptual Learning in Object Recognition. Journal of Cognitive Neuroscience, 27, 787–797.


Hutter, F., Lücke, J. and Schmidt-Thieme, L. (2015). Beyond Manual Tuning of Hyperparameters. KI - Künstliche Intelligenz, 29, 329-337.


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


Franke, F., Pröpper, R., Alle, H., Meier, P., Geiger, J. R. P., Obermayer, K. and Munk, M. H. J. (2015). Spike sorting of synchronous spikes from local neuron ensembles. Journal of Neurophysiology, 114, 2535–2549.


Franke, F., Quiroga, R. Q., Hierlemann, A. and Obermayer, K. (2015). Bayes optimal template matching for spike sorting – combining fisher discriminant analysis with optimal filtering. Journal of Computational Neuroscience, 38, 439-459.


2014

Svensson, C.-M., Krusekopf, S., Lücke, J. and Figge, M. T. (2014). Automated Detection of Circulating Tumour Cells With Naive Bayesian Classifiers. Cytometry Part A, 85, 501–511.


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