Currently, my main project deals with associative learning in honeybees. It focuses on experiments in which the so-called proboscis extension response (PER) was modulated using odors as conditioned stimuli. On the basis of the corresponding behavioral and physiological findings, we create computational models of plastic networks that both reproduce and predict the associated learning dynamics on the level of individual bees. With this approach we want to widen our knowledge about the underlying neurophysiological processes.
In a parallel project we're developing a robotic platform that employs spiking neural networks as control architecture. This allows us to apply our models of learning and plasticity within this robotic framework.