Artificial neural networks try to capture aspects of information processing of biological neural nets in artificial systems. One aim lies in an exploration and testing of hypotheses about the working principles of real neural nets, using simulation models at varying levels of abstraction. A second goal is to exploit attractive properties of neural information processing, such as error tolerance, parallel distributed processing as well as learning ability for technical applications. Besides work in the areas of robotics, computer vision, human machine interfaces and datamining we pursue basic research to topics including stability of recurrent networks, properties of competitive layer networks, neural learning and self-organizing maps.
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In an attempt to better understand the cognitive processing of visual information in humans, the Bielefeld eye-tracking group investigates mechanisms of visual attention and perception in various lines of research. As the eye is considered a "window to the mind", eye movements can provide insight into cognitive processing. We follow an empirical-simulative approach: data from empirical eye-tracking studies is used to implement and validate computer models that simulate human visual behaviour.
Cognitive Robotics draws from classical robotics, artificial intelligence, cognitive science and neurobiology to elucidate and synthesize aspects of action-oriented intelligence. Using a robot system with a multi-fingered manipulator and an active binocular camera head we investigate strategies how to coordinate the actions of such system with those of a human partner. We focus on dextrous manipulation of objects, combining tactile and visual sensing, the joining of action primitives into action sequences and the development of learning algorithms.
The research line Attentive Interfaces focuses on the development of alternative human-machine interfaces (HMI). We consider attention to be the key point of our research. Attention in the concept of Attentive Interfaces is a coin with two sides: We exploit correlates of human attention as input to HMI, while at the same time, we want the machine to adapt to the human user as neccessary, that is, to be "attentive".