Abstract

There is a broad consensus about the fundamental role of thehippocampal system (hippocampus and its adjacent areas) in theencoding and retrieval of episodic memories. This paper presents afunctional model of this system. Although memory is not asingle-unit cognitive function, we took the view that the wholesystem of the smooth, interrelated memory processes may have acommon basis. That is why we follow the Ockham's razor principleand minimize the size or complexity of our model assumption set.The fundamental assumption is the requirement of solving the socalled ``homunculus fallacy'', which addresses the issue ofinterpreting the input. Generative autoassociators seem to offer aresolution of the paradox. Learning to represent and to recallinformation, in these generative networks, imply maximization ofinformation transfer, sparse representation and noveltyrecognition. A connectionist architecture, which integrates theseaspects as model constraints, is derived. Numerical studiesdemonstrate the novelty recognition and noise filtering propertiesof the architecture. Finally, we conclude that the derivedconnectionist architecture can be related to the neurobiologicalsubstrate.