Estimates of storage capacity in the q-state Potts-glass neural network

We study the absolute storage capacity of the q-state Potts-glass neural network which determines how many memory patterns can be really retrieved. By using theoretical analysis combined with a characteristic of the distribution of local field, a general formula for estimating the storage capacity is proposed, and dynamical simulations for q = 2 and q = 3 are presented for comparison. Compared with the previous theory, it is found that in the case of q = 2 our estimate is in good agreement with the simulation result, while for the case of q = 3 it provides a lower boundary of the storage capacity instead. The result may provide useful information for possible applications of neural networks.