Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore plan more efficiently.
In collaboration with Prof. Maass group (IGI, TU Graz, SP9) we proposed a biologically inspired functional model. This model is based on liquid state machines and can learn to predict visual stimuli from address events provided by a Dynamic Vision Sensor (DVS).
We validated this model on various experiments both with simulated and real DVS. The results were accepted for publication in . We are now currently working on using those short-term visual predictions to control robots.
 “Scaling up liquid state machines to predict over address events from dynamic vision sensors”, Jacques Kaiser, Rainer Stal, Anand Subramoney et al., Special issue in Bioinspiration & Biomimetics, 2017.