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
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:
- © NI, TUB
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:
- © NI, TUB
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:
- © NI, TUB
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.
|Author||Meyer, R., and Ladenbauer, J., and Obermayer, K.|
|Journal||Frontiers in Computational Neuroscience|
|Abstract||Noise correlations are a common feature of neural responses and have been observed in many cortical areas across different species. These correlations can influence information processing by enhancing or diminishing the quality of the neural code, but the origin of these correlations is still a matter of controversy. In this computational study we explore the hypothesis that noise correlations are the result of local recurrent excitatory and inhibitory connections. We simulated two-dimensional networks of adaptive spiking neurons with local connection patterns following Gaussian kernels. Noise correlations decay with distance between neurons but are only observed if the range of excitatory connections is smaller than the range of inhibitory connections ("Mexican hat'' connectivity) and if the connection strengths are sufficiently strong. These correlations arise from a moving blob-like structure of evoked activity, which is absent if inhibitory interactions have a smaller range ("inverse Mexican hat'' connectivity). Spatially structured external inputs fixate these blobs to certain locations and thus effectively reduce noise correlations. We further investigated the influence of these network configurations on stimulus encoding. On the one hand, the observed correlations diminish information about a stimulus encoded by a network. On the other hand, correlated activity allows for more precise encoding of stimulus information if the decoder has only access to a limited amount of neurons.|
|Bibtex Type of Publication||Selected:structured selected:publications|
Prof. Dr. rer. nat. Klaus Obermayer
Room MAR 5043
e-mail query 
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.
Room MAR 5042
Fon: +49 30 314 73442
Fax: +49 30 314 73121
e-mail query 
We 9am - 11am
Departmental reseach labs
- Cognitive Systems 
- Data Analytics & Cloud 
- Future Internet & Media Technology 
- Cyber-Physical Systems 
Collaborative projects in research and education
- Bernstein Center for Computational Neuroscience 
- Research Training Group "Sensory Computation in Neural Systems" 
- Graduate School Mind and Brain 
- International Master-Program in Computational Neuroscience 
- Einstein Center Neuroscience 
- Collaborative Research Center "Control of Self-Organizing Nonlinear Systems" 
- SysMedAlcoholism: Alcohol Addiction: A Systems-Oriented Approach 
- Science of Intelligence (DFG Research Cluster) 
- Collaborative Research Center "Mechanisms and Disturbances in Memory Consolidation" 
- SMARTSTART - training program in computational neuroscience