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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:

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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:

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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:

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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

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


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


Houillon, A., Lorenz, R., Boehmer, W., Rapp, M., Heinz, A., Gallinat, J. and Obermayer, K. (2013). The effect of novelty on reinforcement learning.. Progress in brain research, 202, 415–439.


Augustin, M., Ladenbauer, J. and Obermayer, K. (2013). How Adaptation Shapes Spike Rate Oscillations in Recurrent Neuronal Networks. Front. Comput. Neurosci., 7


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.


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. Phys. Rev. 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.


Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2012). Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Machine Learning, 89, 67–86.


Ladenbauer, J., Augustin, M., Shiau, L. and Obermayer, K. (2012). Impact of Adaptation Currents on Synchronization of Coupled Exponential Integrate-and-Fire Neurons. PLoS Comput. Biol., 8, e1002478.


Onken, A., Dragoi, V. and Obermayer, K. (2012). A Maximum Entropy Test for Evaluating Higher-Order Correlations in Spike Counts. PLoS Comput. Biol., 8, e1002539.


Grünewälder, 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. J. Comput. Neurosci., 127 – 148.


Jain, B. J. and Obermayer, K. (2009). Structure Spaces. J. Mach. Learn. Res., 10, 2667 – 2714.


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


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


Young, J. M., Waleszczyk, W. J., 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.


Mariño, J., Schummers, J., Lyon, D., Schwabe, L., Beck, O., Wiesing, P., Obermayer, K. and Sur, M. (2005). Invariant Computations in Local Cortical Networks with Balanced Excitation and Inhibition. Nat. Neurosci., 8, 194 – 201.


Schmitt, S., Evers, J.-F., Duch, C., Scholz, M. and Obermayer, K. (2004). New Methods for the Computer-Assisted 3D Reconstruction of Neurons from Confocal Image Stacks. Neuroimage, 23, 1283 – 1298.


Wenning, G. and Obermayer, K. (2003). Activity Driven Adaptive Stochastic Resonance. Phys. Rev. Lett., 90, 120602.


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Head
Prof. Dr. rer. nat. Klaus Obermayer
Room MAR 5043


Consultation hours:
Fri 12am-1pm

Administrative Office
Bruns, Camilla
Room MAR 5042
Fon: +49 30 314 73442
Fax: +49 30 314 73121


Consultation hours:
Mon-Fri 8:45am-12:30am