Kalman-filtering using local interactions
  There is a growing interest in using Kalman-filter models for brain
modelling. In turn, it is of considerable importance to represent Kalman-filter
in connectionist forms with local Hebbian learning rules. To our best
knowledge, Kalman-filter has not been given such local representation. It seems
that the main obstacle is the dynamic adaptation of the Kalman-gain. Here, a
connectionist representation is presented, which is derived by means of the
recursive prediction error method. We show that this method gives rise to
attractive local learning rules and can adapt the Kalman-gain.
