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.
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, adaptationand 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.
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.
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
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.
Ladenbauer, J., Augustin, M., Shiau, L. and
(2012). Impact of Adaptation Currents on Synchronization of Coupled
Exponential Integrate-and-Fire Neurons .
PLoS Comput. Biol., 8, e1002478.
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.
Grünewälder, S. and Obermayer, K.
(2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD
and MC .
Machine Learning, 83, 289 – 330.
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
Cereb. Cortex, 19, 2166 – 2180.
Jain, B. J. and Obermayer, K.
(2009). Structure Spaces .
J. Mach. Learn. Res., 10, 2667 – 2714.
Franke, F., Natora, M., Boucsein, C., Munk, M.
and Obermayer, K.
(2009). An Online Spike Detection and Spike Classification Algorithm
Capable of Instantaneous Resolution of Overlapping Spikes .
J. Comput. Neurosci., 127 – 148.
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.
Prof. Dr. rer. nat. Klaus Obermayer
Room MAR 5043
e-mail query