Comparation based bottom-up and top-down filtering model of the hippocampus and its environment

Abstract

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.