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TU Berlin

Inhalt des Dokuments

All Publications

2016

Xie, S., Wang, L., Obermayer, K. and Zhu, F. (2016). Design of a Visual Stimulation System with LED in the Study of Spatial Selective Attention. Advances in Cognitive Neurodynamics. Springer, 461-468.,10.1007/978-981-10-0207-6_63


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.


Trowitzsch, I., Taghia, J., Kashef, Y. and Obermayer, K. (2016). NIGENS Anechoic Ear Signals. Zenodo.,10.5281/zenodo.168042


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.


Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.,10.1109/IJCNN.2015.7280637


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


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


Schmidt, S., Scholz, M., Haberbosch, L., Mante, A., Obermayer, K. and Brandt, S. A. (2015). P273: Fast induction of alpha entrainment with bandwidth confined electric and photic stimulation.. Clinical Neurophysiology, 125, S122.


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


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

Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory. Neural Networks (IJCNN), 2014 International Joint Conference on, 487-493.,10.1109/IJCNN.2014.6889547


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.


Ladenbauer, J., Augustin, M. and Obermayer, K. (2014). How Adaptation Currents Change Threshold, Gain and Variability of Neuronal Spiking. Journal of Neurophysiology, 111, 939–953.


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