Computational model of the entorhinal-hippocampal region derived from a single principle

We show that several properties of the highly elaborate structure of the EC-HC loop can be explained using the single principle that to recall past and to foresee future events a predictive structure is necessary. Networks that develop independent components (ICs) in an efficient manner can be built from two stages. We identify, these stages with the CA3 and CAI layers of the hippocampus (HC). The forming of ICs requires nonlinear operation, whereas IC outputs arise under linear operation and thus two-phase operation follows. Concurrent occurrences of past and present events are required by Hebbian learning and can be achieved by delaying structures. The loop structure requires a third layer that we identify with the entorhinal cortex (EC). Proper encoding into the EC is possible during linear operation in a supervised manner. The DRN can be seen as an error compensating control architecture. Thus the novel information processed by the DRN may be temporally convolved. We assume that blind source deconvolution is executed by the dentate gyrus and show that the dentate gyrus can satisfy the requirements.