the ongoing COVID-19 pandemic the Neural Information Processing group
might not offer all courses for the SoSe 2021. Further information of
the execution can be found on ISIS.
The following courses will be offered:
- Praktisches Programmieren und Rechneraufbau
- Machine intelligence II
- Einführung in die Informatik . Vertiefung
The following courses might take place (not decided yet):
- Advanced topics in reinforcement
Information Processing Group
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
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.
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
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
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Cakan, C. and Obermayer, K.
(2020). Biophysically grounded mean-field models of neural
populations under electrical stimulation .
PLOS Computational Biology, 2020
Seo, S., Beck, A., Matthis, C., Genauck, A.,
Banaschewski, T., Bokde, A., Bromberg, U., Büchel, C., Quinlan,
E., Flor, H., Frouin, V., Garavan, H., Gowland, P., Ittermann,
B., Martinot, J., Martinot, M., Nees, F., Orfanos, D., Poustka,
L., Hohmann, S., Froehner, J., Smolka, M., Walter, H., Whelan,
R., Desrivieres, S., Heinz, A., Schumann, G. and Obermayer, K.
(2019). Risk Profiles for Heavy Drinking in Adolescence: Differential
Effects of Gender .
Addiction Biology, 24, 787-801.
Ladenbauer, J. and Obermayer, K.
(2019). Weak Electric Fields Promote Resonance in Neuronal Spiking
Activity: Analytical Results from Two-compartment Cell and Network
PLoS Computational Biology, 15
Mergenthaler, K., Oschmann, F. and Obermayer,
(2019). Glutamate Uptake by Astrocytic Transporters .
Computational Glioscience, 329-361.
Trowitzsch I., Schymura C., Kolossa D. and K.,
(2019). Joining Sound Event Detection and Localization Through
Spatial Segregation .
IEEE Trans. Audio Speech Language Proc.
Aspart, F., Remme, M. and Obermayer, K.
(2018). Differential Polarization of Cortical Pyramidal Neuron
Dendrites through Weak Extracellular Fields .
Computational Biology, 15
Meyer, R., Ladenbauer, J. and Obermayer, K.
(2017). Influence of Mexican Hat Recurrent Connectivity on Noise
Correlations and Stimulus Encoding .
Frontiers in Computational Neuroscience
Trowitzsch, I., Mohr, J., Kashef, Y. and
(2017). Robust Detection of Environmental Sounds in Binaural Auditory
IEEE Transactions on Audio Speech and Language Processing, 25,
Oschmann, F., Mergenthaler, K., Jungnickel, E.
and Obermayer, K.
(2017). Spatial Separation of Two Different Pathways Accounting for
the Generation of Calcium Signals in Astrocytes .
PLoS Computational Biology, 13
Augustin, M., Ladenbauer, J., Baumann, F. and
(2017). Low-dimensional spike rate models derived from networks of
adaptive integrate-and-fire neurons: Comparison and implementation
PLoS Computational Biology, 13
Guo, R., Böhmer, W., Hebart, M., Chien, S.,
Sommer, T., Obermayer, K. and Gläscher, J.
(2016). Interaction of Instrumental and Goal-directed Learning
Modulates Prediction Error Representations in the Ventral Striatum
Journal of Neuroscience, 36, 12650-12660.
Aspart, F., Ladenbauer, J. and Obermayer, K.
(2016). Extending Integrate-and-fire Model Neurons to Account for the
Effects of Weak Electric Fields and Input Filtering Mediated by the
PLOS Computional Biology, 12, e1005206.
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.
Bielivtsov, D., Ladenbauer, J. and Obermayer,
(2016). Controlling Statistical Moments of Stochastic Dynamical
Physical Review E, 94, 012306.
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,
(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
(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
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.
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Prof. Dr. rer. nat. Klaus Obermayer
Room MAR 5043
e-mail query 
registration via email
restricted acces to TU buildings in reacion to the Covid-19 pandemic,
it is nescessary to register per email for the office hour of Prof.
Please send an email with some days in advance to explain your
concern. If it is not possible to solve it by email, you will receive
an email at the time of the office hour (Wed, 12-1 pm) including a
link which will allow to participate in a video conference with Prof.
All requets will be handled first-in-first-out. Please stay tuned
for the whloe time of the office hour.