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Neural Information ProcessingNeural Information Processing

Neuronale Informationsverarbeitung

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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

Risk Profiles for Heavy Drinking in Adolescence: Differential Effects of Gender
Citation key Seo2019
Author Seo, S. and Beck, A. and Matthis, C. and Genauck, A. and Banaschewski, T. and Bokde, A. and Bromberg, U. and Büchel, C. and Quinlan, E. and Flor, H. and Frouin, V. and Garavan, H. and Gowland, P. and Ittermann, B. and Martinot, J. and Martinot, M. and Nees, F. and Orfanos, D. and Poustka, L. and Hohmann, S. and Froehner, J. and Smolka, M. and Walter, H. and Whelan, R. and Desrivieres, S. and Heinz, A. and Schumann, G. and Obermayer, K.
Pages 787-801
Year 2019
Journal Addiction Biology
Volume 24
Number 4
Bibtex Type of Publication Selected:publications
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