Stochastic Dynamics and High Capacity Associative Memories

The addition of noise to the deterministic Hopfield network, trained with one shot Hebbian learning, is known to bring benefits in the elimination of spurious attractors. This paper extends the analysis to learning rules that have a much higher capacity. The relative energy of desired and spurious attractors is reported and the affect of adding noise to the dynamics is empirically investigated. It is found that the addition of noise brings even more benefit in the case of the higher capacity rules. Key-Words Associative memory, Attractor basins, Hopfield neural networks, Learning rules, Perceptron, Performance measures, Pseudo-inverse.