Distinguishing spurious and nominal attractors applying unlearning to an asymmetric neural network

We study a neural network with asymmetric connections used as an associative memory. Asymmetry allows the nominal patterns to be stored in cycles. We apply an unlearning procedure, which modifies the synaptic connections. We analyze the global performance, including the network capacity, the attraction basin's size and also the relaxation time distribution. The latter shows a convenient bimodality that is used for discriminating between spurious and stored memory attractors. We show that unlearning in asymmetric networks allows enhancing the global performance of retrieval including retrieval of a sequence of correlated patterns.