Autonomous Robotics

Our research in autonomous robotics is aimed at demonstrating that neural dynamic architectures of embodied cognition can generate object-oriented actions and simple forms of cognition. We organize the work around a scenario in which a partially autonomous robot system interacts with human operators with whom they share a natural environment.

Computational Neurology

In the Computational Neurology group, we answer clinical relevant questions in the field of neuropsychiatry using computational methods. We analyze clinical and neuroimaging data using both data-driven and theory-based modeling. This integration helps generating a better (qualitative) and robust (quantitative) understanding of pathophysiological processes of neurological diseases, as well as their diagnosis and treatment.

Computational Neuroscience

Our group investigates the neural mechanisms underlying learning and memory using computational methods. The main language of communication is English. We welcome students who would like to pursue a Bachelor/Master thesis or an internship in our group. Our unit is located at the Institute of Neural Computation and is a member of the Mercator Research Group (MRG) "Structure of Memory".

Databases and Information Systems

The research group of Databases and Information Systems at RUB devises novel techniques for managing decentralised data, information, and knowledge with a focus on query optimisation, federated query processing, and data quality. As part of our research, we also study the application of Machine Learning approaches to solve diverse problems in Data and Knowledge Management. For example, we leverage latent features from sub-symbolic representations of knowledge to uncover hidden patterns that can be used to predict missing assertions or properties in knowledge bases.

Neural Data Science

We develop advanced analysis methods to apply them to neural and behavioural data, and combine them with computational models. In close collaboration with experimental research groups we analyse and model large data sets of electrophysiological recordings. Our goal is to identify the neural mechanisms underlying cognitive control, in particular in prefrontal and basal ganglia circuits, and to determine how the neuromodulator dopamine contributes to them. We use a variety of analysis and computational modelling techniques, such as machine learning approaches and numerical simulations of single neuron and network activity.

Optical Imaging Group

Our research focuses on understanding visual information processing in the brain. We investigate how manifold parameters given in natural visual sceneries are represented in real-time across large neuronal populations using voltage-sensitive dye imaging (VSDI).

Theory of Embodied Cognition

We develop a neurally grounded theory of embodied cognition. A number of exemplary problems are chosen to provide access to important basic properties of embodied cognitive systems that are open to experiment and modeling. The main theoretical tools are attractor dynamics and dynamic field theory.

Theory of Machine Learning

The Theory of Machine Learning workgroup is concerned with the design and analysis of adaptive information processing systems. We are interested in systems that improve over time through learning, self-adaptation, and evolution. Our systems improve autonomously based on data, in contrast to manual instruction or programming.

Theory of Neural Systems

We are an interdisciplinary research group focusing on principles of self-organization in neural systems, ranging from artificial neural networks to the hippocampus. By bringing together machine learning and computational neuroscience we explore ways of extracting representations from data that are useful for goal-directed learning.

Trustworthy Machine Learning

Trustworthy Machine Learning: investigating novel approaches to privacy-preserving federated learning, the theoretical foundations of deep learning, and collaborative training of explainable models.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210