Dreaming neural networks: Rigorous results
Recently, a daily routine for associative neural networks has been proposed: the network Hebbian-learns during the awake state (thus behaving as a standard Hopfield model), then, during its sleep state, it consolidates pure patterns and removes spurious ones, optimizing information storage: this forces the synaptic matrix to collapse to the projector one (ultimately approaching the Kanter-Sompolinsky model), allowing for the maximal critical capacity (for symmetric interactions).