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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

The Operating Regime of Local Computations in Primary Visual Cortex
Citation key Stimberg2009
Author Stimberg, M. and Wimmer, K. and Martin, R. and Schwabe, L. and Marino, J. and Schummers, J. and Lyon, D. and Sur, M. and Obermayer, K.
Pages 2166 – 2180
Year 2009
DOI 10.1093/cercor/bhn240
Journal Cerebral Cortex
Volume 19
Number 9
Publisher Oxford Journals
Abstract In V1, local circuitry depends on the position in the orientation map: close to pinwheel centers recurrent inputs show variable orientation preferences; within iso-orientation domains inputs are relatively uniformly tuned. Physiological properties such as cell's membrane potentials, spike outputs, and temporal characteristics change systematically with map location. We investigate in a firing rate and a Hodgkin-Huxley network model what constraints these tuning characteristics of V1 neurons impose on the cortical operating regime. Systematically varying the strength of both recurrent excitation and inhibition, we test a wide range of model classes and find the likely models to account for the experimental observations. We show that recent intracellular and extracellular recordings from cat V1 provide the strongest evidence for a regime where excitatory and inhibitory recurrent inputs are balanced and dominate the feed-forward input. Our results are robust against changes in model assumptions such as spatial extent and strength of lateral inhibition. Intriguingly, the most likely recurrent regime is in a region of parameter space where small changes have large effects on the network dynamics, and it is close to a regime of ''runaway excitation``, where the network shows strong self-sustained activity. This could make the cortical response particularly sensitive to modulation.
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


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