  Two rate code models -- a reconstruction network model and a control model --
of the hippocampal-entorhinal loop are merged. The hippocampal-entorhinal loop
plays a double role in the unified model, it is part of a reconstruction
network and a controller, too. This double role turns the bottom-up information
flow into top-down control like signals. The role of bottom-up filtering is
information maximization, noise filtering, temporal integration and prediction,
whereas the role of top-down filtering is emphasizing, i.e., highlighting or
`paving of the way' as well as context based pattern completion. In the joined
model, the control task is performed by cortical areas, whereas reconstruction
networks can be found between cortical areas. While the controller is highly
non-linear, the reconstruction network is an almost linear architecture, which
is optimized for noise estimation and noise filtering. A conjecture of the
reconstruction network model -- that the long-term memory of the visual stream
is the linear feedback connections between neocortical areas -- is reinforced
by the joined model. Falsifying predictions are presented; some of them have
recent experimental support. Connections to attention and to awareness are
made.
