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Closing of TU buildings

Due to the complete closing of all TU buildings from 20.03.2020 (3 pm) we can not answer any letters sent by postal services. Please send a copy via email to 

(Camilla Groiss, secretary),

(Prof. Dr. Klaus Obermayer) and

(Dr. Michael Scholz).

Reduced course portfolio due to COVID-19 pandemic

Due to the ongoing COVID-19 pandemic, presence teaching is actual not allowed. Wherever feasible, the Neural Information Processing group will offer its courses for the SoSe 2020 in an appropriate online format. Unfortunately this is not possible for all our courses. Please stay alert for further updates on this website regarding the cancelled courses.

The following courses will be offered:

- Praktisches Programmieren und Rechneraufbau (ISIS-page)

- Machine intelligence II (ISIS-page)

- Einführung in die Informatik . Vertiefung (Prof. Hellwich)

The following courses will be cancelled:

- Modelle zur Informationsverarbeitung im Gehirn

- Advanced topics in reinforcement learning

- NI-Projekt

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

pypet: A Python Toolkit for Data Management of Parameter Explorations
Citation key Meyer2016b
Author Meyer, R. and Obermayer, K.
Year 2016
DOI 10.3389/fninf.2016.00038
Journal Frontiers Neuroinformatics
Volume 10
Number 38
Abstract pypet (Python parameter exploration toolkit) is a new multi-platform Python toolkit for managing numerical simulations. Sampling the space of model parameters is a key aspect of simulations and numerical experiments. pypet is designed to allow easy and arbitrary sampling of trajectories through a parameter space beyond simple grid searches. pypet collects and stores both simulation parameters and results in a single HDF5 file. This collective storage allows fast and convenient loading of data for further analyses. pypet provides various additional features such as multiprocessing and parallelization of simulations, dynamic loading of data, integration of git version control, and supervision of experiments via the electronic lab notebook Sumatra. pypet supports a rich set of data formats, including native Python types, Numpy and Scipy data, Pandas DataFrames, and BRIAN(2) quantities. Besides these formats, users can easily extend the toolkit to allow customized data types. pypet is a flexible tool suited for both short Python scripts and large scale projects. pypet's various features, especially the tight link between parameters and results, promote reproducible research in computational neuroscience and simulation-based disciplines.
Bibtex Type of Publication Selected:main selected:structured
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:
Mo, We, Fr 9am - 11am