Dreaming and unlearning in neural networks

In the first part of this talk we review the main definitions, concepts and classical results on neural networks, distinguishing between the two principal cognitive tasks, i.e., "learning" and "retrieval". Focusing on the latter, the Hopfield model is probably the one most extensively investigated, although it exhibits an intrinsic capacity limit (in terms of the ratio between the amount P of stored patterns and the network size N) far below the theoretical known bound. In this talk we will show that this limit can be improved by means of “unlearning” iterations which mimic unconscious mechanisms taking place during the REM phase of mammals.