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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.

Reduced course portfolio due to COVID-19 pandemic

Due to 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 

- NI-Projekt

The following courses might take place (not decided yet):

- Advanced topics in reinforcement learning

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

Invariant Computations in Local Cortical Networks with Balanced Excitation and Inhibition
Citation key Marino2005
Author Mariño, J. and Schummers, J. and Lyon, D.C. and Schwabe, L. and Beck, O. and Wiesing, P. and Obermayer, K. and Sur, M.
Pages 194 – 201
Year 2005
DOI 10.1038/nn1391
Journal Nature Neuroscience
Volume 8
Abstract Cortical computations critically involve local neuronal circuits. The computations are often invariant across a cortical area, yet are carried out by networks which can vary widely within an area based on its functional architecture. Here, we demonstrate a mechanism by which orientation selectivity is computed invariantly in primary visual cortex across an orientation preference map that provides a wide diversity of local circuits. Visually evoked excitatory and inhibitory synaptic conductances are balanced exquisitely in cortical neurons and thus keep the spike response sharply tuned at all map locations. This functional balance derives from spatially isotropic local connectivity to both excitatory and inhibitory cells. Modelling results demonstrate that such co-variation is a signature of recurrent rather than purely feedforward processing, and that the observed isotropic local circuit is sufficient to generate invariant spike tuning.
Bibtex Type of Publication Selected:main selected:v1 selected:publications
Link to original publication Download Bibtex entry

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

virtual
consultation hours:
Wed 12am-1pm
registration via email

During the 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. Obermayer.

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. Obermayer.

All requets will be handled first-in-first-out. Please stay tuned for the whloe time of the office hour.

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


Consultation hours:
We 9am - 11am