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 Neuroscience

Our group investigates the neural mechanisms underlying learning and memory using computational methods. The main language of communication is English. We highly welcome bright students who would like to pursue a Bachelor/ Master thesis 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".

Neural Plasticity Lab

Neuroplasticity describes the brain's ability to adapt to new experiences and boundary conditions over the entire lifespan. The Neural Plasticity Lab is working on issues of functional, integrative and cognitive neuroscience with an emphasis on cortical plasticity, learning and aging.

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).

Organic Computing

We do research on self-organizing systems in computer vision, control, and machine learning.

Real-Time Computer Vision

Dealing with the topic of efficient computer vision, our group has a long and successful history at the chair. Our current focus lies on the technologies for driver assistance systems, which represent an important and challenging field of application. These intelligent systems analyse the vehicle´s environment via different types of sensors, for instance video and radar, thus, increasing safety and comfort for the driver.

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, such as reinforcement learning.

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.

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

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