Brains are probably the most complex systems in nature. They control the behavior of animals and men and have evolved over millions of years to suit their task. In this course basic aspects of behavioral biology are studied. These include topics as neural processing of sensory information, motor control, orientation and navigation, learning and memory, communication and social interaction. The Department of Biological Cybernetics is involved in teaching of part 2 (motion, motor control and orientation).
The aim of this type of course is to introduce the methodological methods for research on either the generation and control of sensory-guided motor patterns or the neurobionics/biorobotics of motor control. Methods trained are planning of experiments, computer aided data acquisition and evaluation as well as oral or written presentation of results. Example experiments include but are not limited to studies on visuo-tactile orientation, tactile pattern recognition and task-dependant adaption of motor patterns in insects.
Students may also become familiar with formal concepts and modelling techniques of biological data in order to transfer biological principles to technical applications. Example experiments may include hard- and/or software modeling of limb movements and the implementation of active sensing concepts, visuo-tactile orientation and/or tactile pattern recognition on robots.
The main focus of this module is the physiology of sensory-guided movements and motor control.
Students carry out individual research experiments that are immediately related to ongoing projects of the workgroup. Topic and scope of each project are subject to an individual agreement with the supervising senior researcher. Examples of possible research topics are: Tactile and visual orientation and sensory induced changes in locomotion, tactile pattern recognition, control and coordination of multiple joints and/or limbs during different motor behaviours, context-dependent changes in muscular and nervous activation patterns. Possible research methods include motion capture and kinematic analyses of natural movement sequences, learning paradigms, force measurements, extra- and intracellular recording techniques and electromyography.
The main focus of this module is neurobionics of information processing and motor control.
Students carry out individual research experiments that are immediately related to ongoing projects of the workgroup. Topic and scope of each project are subject to an individual agreement with the supervising senior researcher. Examples of possible research topics are: Software modeling of neural information processing and/or motor control, software modeling of active tactile sampling and pattern recognition, hardware modeling of multi-joint coordination and hardware modeling of the active tactile sense. Possible research methods include software modeling in Matlab, hardware modeling using either an educational robotics kit or parts of the existing experimental robot platforms of the workgroup.
The aim of this module is to deepen the methodological repertoire for research on either the physiology of sensory-guided movements or the bionics of information processing and motor control.
Students carry out individual research experiments that are immediately related to ongoing projects of the workgroup. Topic and scope of each project are subject to an individual agreement with the supervising senior researcher. Examples of possible research topics include various aspects of the physiology of sensorimotor control in insects, and modeling of sensorimotor control in software and hardware. Possible research methods include motion capture, various electrophysiological recording techniques, software modeling in MatLab, etc.
This module is an introduction to behavioural control systems. Specifically, it concentrates on properties of dynamical systems. This includes linear filters as well as nonlinear elements that are ordered serially, in parallel, or via recurrent connections. Following the understanding of such "simple" systems, the properties of massively parallel systems ("neural networks") will be considered.
To develop a better intuition of the properties of such complex systems, software simulations will be performed, using customized programs, accompanied by theoretical instructions. An important aspect concerns training methods for artificial neural networks, thus introducing formal principles of plasticity and learning. These methods include supervised learning, unsupervised learning and learning with a critic. Practical application of the theoretical issues covered by this module will be based on modeling of biological systems. To this end, selected biological experiments will be performed and subsequently interpreted by means of computer simulations.