  A model of sensory information processing is presented. The model assumes
that learning of internal (hidden) generative models, which can predict the
future and evaluate the precision of that prediction, is of central importance
for information extraction. Furthermore, the model makes a bridge to
goal-oriented systems and builds upon the structural similarity between the
architecture of a robust controller and that of the hippocampal entorhinal
loop. This generative control architecture is mapped to the neocortex and to
the hippocampal entorhinal loop. Implicit memory phenomena; priming and
prototype learning are emerging features of the model. Mathematical theorems
ensure stability and attractive learning properties of the architecture.
Connections to reinforcement learning are also established: both the control
network, and the network with a hidden model converge to (near) optimal policy
under suitable conditions. Falsifying predictions, including the role of the
feedback connections between neocortical areas are made.
