THERMODYNAMIC PROPERTIES OF FULLY CONNECTED Q-ISING NEURAL NETWORKS

The thermodynamic and retrieval properties of fully connected Q-Ising networks are studied in the replica-symmetric mean-field approximation . In particular, capacity-gain parameter and capacity-temperature phas e diagrams are derived for Q = 3, 4 and Q = infinity and different dis tributions of the stored patterns. Furthermore, the optimal gain funct ion is determined in order to obtain the best performance. Where appro priate, the results are compared with the diluted and layered versions of these models.