direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

TU Berlin

Inhalt des Dokuments

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 effect of novelty on reinforcement learning
Citation key Houillon2013
Author Houillon, A. and Lorenz, R. C. and Boehmer, W. and Rapp, M. A. and Heinz, A. and Gallinat, J. and Obermayer, K.
Pages 415–439
Year 2013
DOI 10.1016/B978-0-444-62604-2.00021-6
Journal Progress in brain research
Volume 202
Publisher Elsevier
Abstract Recent research suggests that novelty has an influence on reward-related learning. Here, we showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate learning of the most rewarding category, depending on the condition. The extent of this influence depended on the individual trait of novelty seeking. Different reinforcement learning models were developed to quantify subjects' choices. We introduced a bias parameter to model explorative behavior toward novel stimuli and characterize individual variation in novelty response. The theoretical framework allowed us to test different assumptions, concerning the motivational value of novelty. The best fitting-model combined all novelty components and had a significant positive correlation with both the experimentally measured novelty bias and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by itself enhances behavioral responses underlying reward processing, but also that novelty has a direct influence on reward-dependent learning processes, consistently with computational predictions.
Bibtex Type of Publication Selected:main selected:decision selected:publications
Link to original publication Download Bibtex entry

To top