Titolo della tesi: Models for Associative Memory in Artificial and Biological Neural Networks
Since it's onset, research in the field of Artificial Neural Networks has pursued two different, but far from alternative objectives. On the one hand, there is a strong interest in modelling the functioning of a biological brain. On the other hand, there is a very practical interest in harvesting the secrets of the brain's extraordinary efficiency and precision in data elaboration, which is in the general case far from matched by state of the art Artificial Intelligence. In this thesis we discuss some interesting examples of this duality, in the context of Associative Memory, between algorithms and biologically inspired learning rules.