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Courses in the winter semester 2021/22

Please note that due to the IT attack on the TU Berlin, it is currently not possible to edit this website. So unfortunately not all information is kept up to date.

You can therefore find our course offer in the winter semester 2021/22 here.

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

Müller, L., Ploner, M., Goerttler, T. and Obermayer, K. (2021). An Interactive Introduction to Model-Agnostic Meta-Learning. Workshop on Visualization for AI Explainability at IEEE VIS


Chouzouris, T., Roth, N., Cakan, C. and K., O. (2021). Applications of Optimal Nonlinear Control to a Whole-brain Network of FitzHugh-Nagumo Oscillators. Phys. Rev. E, 2021


Cakan, C., Jajcay, N. and K, O. (2021). neurolib: A Simulation Framework for Wholebrain Neural Mass Modeling. Cognit. Comput., 2021


Cakan, C. and Obermayer, K. (2020). Biophysically grounded mean-field models of neural populations under electrical stimulation. PLOS Computational Biology, 2020


Koren, V., Andrei, A., Hu, M., Dragol, V. and Obermayer, K. (2020). Pair-wise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex. Cell Reports, 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.



Mergenthaler, K., Oschmann, F. and Obermayer, K. (2019). Glutamate Uptake by Astrocytic Transporters. Computational Glioscience, 329-361.


Trowitzsch I., Schymura C., Kolossa D. and K., O. (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 Obermayer, K. (2017). Robust Detection of Environmental Sounds in Binaural Auditory Scenes. IEEE Transactions on Audio Speech and Language Processing, 25, 1344-1356.


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 Obermayer, K. (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 Dendrite. 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, K. (2016). Controlling Statistical Moments of Stochastic Dynamical Networks. 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, K. (2014). How Adaptation Currents Change Threshold, Gain and Variability of Neuronal Spiking. Journal of Neurophysiology, 111, 939–953.


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