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TU Berlin

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Neural Information Processing Group

We are concerned with the principles underlying information processing in biological systems. On the one hand we want to understand how the brain computes, on the other hand we want to utilize the strategies employed by biological systems for machine learning applications. Our research interests cover three thematic areas.

Models of Neuronal Systems:

Lupe

In collaboration with neurobiologists and clinicians we study how the visual system processes visual information. Research topics include: cortical dynamics, the representation of visual information, adaptation and plasticity, and the role of feedback. More recently we became interested in how perception is linked to cognitive function, and we began to study computational models of decision making in uncertain environments, and how those processes interact with perception and memory.

Machine Learning and Neural Networks:

Lupe

Here we investigate how machines can learn from examples in order to predict and (more recently) act. Research topics include the learning of proper representations, active and semisupervised learning schemes, and prototype-based methods. Motivated by the model-based analysis of decision making in humans we also became interested in reinforcement learning schemes and how these methods can be extended to cope with multi-objective cost functions. In collaboration with colleagues from the application domains, machine learning methods are applied to different problems ranging from computer vision, information retrieval, to chemoinformatics.

Analysis of Neural Data:

Lupe

Here we are interested to apply machine learning and statistical methods to the analysis of multivariate biomedical data, in particular to data which form the basis of our computational studies of neural systems. Research topics vary and currently include spike-sorting and the analysis of multi-tetrode recordings, confocal microscopy and 3D-reconstruction techniques, and the analysis of imaging data. Recently we became interested in the analysis of multimodal data, for example, correlating anatomical, imaging, and genetic data.

Selected Publications

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


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


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


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.


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.


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


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.


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.


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


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


Grünwälder, S. and Obermayer, K. (2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD and MC. Machine Learning, 83, 289 – 330.


Franke, F., Natora, M., Boucsein, C., Munk, M. and Obermayer, K. (2010). An Online Spike Detection and Spike Classification Algorithm Capable of Instantaneous Resolution of Overlapping Spikes. Journal of Computional Neuroscience, 127 – 148.


Jain, B. and Obermayer, K. (2009). Structure Spaces. Journal of Machine Learning Research, 10, 2667 – 2714.


Stimberg, M., Wimmer, K., Martin, R., Schwabe, L., Marino, J., Schummers, J., Lyon, D., Sur, M. and Obermayer, K. (2009). The Operating Regime of Local Computations in Primary Visual Cortex. Cerebral Cortex, 19, 2166 – 2180.


Henrich, F. and Obermayer, K. (2008). Active Learning by Spherical Subdivision. Journal of Machine Learning Research, 9, 105 – 130.


Young, J., Waleszczyk, W., Wang, C., Calford, M., Dreher, B. and Obermayer, K. (2007). Cortical Reorganization Consistent with Spike Timing- but not Correlation-Dependent Plasticity. Nat. Neurosci., 10, 887 – 889.


Hochreiter, J. and Obermayer, K. (2006). Support Vector Machines for Dyadic Data. Neural Comput., 18, 1472 – 1510.


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