The computational model described here is driven by the hypothesis
that a major function of the entorhinal cortex (EC)-hippocampal
system is to alter synaptic connections in the neocortex. It
is based on the following postulates: (1) The EC compares the
difference between neocortical representations (primary input)
and feedback information conveyed by the hippocampus (the "reconstructed
input"). The difference between the primary input and the reconstructed
input (termed "error") initiates plastic changes in the hippocampal
networks (error compensation). (2) Comparison of the primary
input and reconstructed input requires that these representations
are available simultaneously in the EC network. We suggest that
compensation of time delays is achieved by predictive structures,
such as the CA3 recurrent network and EC-CA1 connections. (3)
Alteration of intrahippocampal connections gives rise to a new
hippocampal output. The hippocampus generates separated (independent)
outputs, which, in turn, train long-term memory traces in the
EC (independent components, IC). The ICs of the long-term memory
trace are generated in a two-step manner, the operations of
which we attribute to the activities of the CA3 (whitening)
and CA1 (separation) fields. (4) The different hippocampal fields
can perform both nonlinear and linear operations, albeit at
different times (theta and sharp phases). We suggest that long-term
memory is represented in a distributed and hierarchical reconstruction
network, which is under the supervision of the hippocampal output.
Several of these model predictions can be tested experimentally.
