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Journal Publications

2015

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.


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.


Mohr, J., Park, J.-H. and Obermayer, K. (2014). A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception. Neural Networks, 60, 182 - 193.


Tobia, M. J., Guo, R., Schwarze, U., Böhmer, W., Gläscher, J., Finckh, B., Marschner, A., Büchel, C., Obermayer, K. and Sommer, T. (2014). Neural Systems for Choice and Valuation with Counterfactual Learning Signals. NeuroImage, 89, 57-69.


Sheikh, A.-S., Shelton, J. A. and Lücke, J. (2014). A Truncated EM Approach for Spike-and-Slab Sparse Coding. Journal of Machine Learning Research, 15, 2653–2687.


Shen, Y., Tobia, M. J., Sommer, T. and Obermayer, K. (2014). Risk-sensitive Reinforcement Learning. Neural Computation, 26, 1298-1328.


Tobia, M. J., Guo, R., Schwarze, U., Böhmer, W., Gläscher, J., Finckh, B., Marschner, A., Büchel, C., Obermayer, K. and Sommer, T. (2014). Counterfactual Q-learning in a Strategic Sequential Investment Task: Serotonergic and Dopaminergic Involvement in the Medial Prefrontal Cortex and Striatum. Neuroimage


Dai, Z. and Lücke, J. (2014). Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1950–1962.


Henniges, M., Turner, R. E., Sahani, M., Eggert, J. and Lücke, J. (2014). Efficient Occlusive Components Analysis. Journal of Machine Learning Research, 15, 2689–2722.


2013

Pröpper, R. and Obermayer, K. (2013). Spyke Viewer: a flexible and extensible platform for electrophysiological data analysis. Frontiers in Neuroinformatics, 7, 1–10.


Böhmer, W., Grünewälder, S., Shen, Y., Musial, M. and Obermayer, K. (2013). Construction of Approximation Spaces for Reinforcement Learning. Journal of Machine Learning Research, 14, 2067–2118.


Schmidt, S., Scholz, M., Obermayer, K. and Brandt, S. A. (2013). Patterned Brain Stimulation, What a Framework with Rhythmic and Noisy Components Might Tell Us about Recovery Maximization. Frontiers in human neuroscience, 7


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.


Ladenbauer, J., Lehnert, J., Rankoohi, H., Dahms, T., Schöll, E. and Obermayer, K. (2013). Adaptation Controls Synchrony and Cluster States of Coupled Threshold-Model Neurons. Physical Review E, 88, 042713.


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