  In this work we study, analytically and employing Monte Carlo simulations,
the influence of the competition between several activity-dependent synaptic
processes, such as short-term synaptic facilitation and depression, on the
maximum memory storage capacity in a neural network. In contrast with the case
of synaptic depression, which drastically reduces the capacity of the network
to store and retrieve "static" activity patterns, synaptic facilitation
enhances the storage capacity in different contexts. In particular, we found
optimal values of the relevant synaptic parameters (such as the
neurotransmitter release probability or the characteristic facilitation time
constant) for which the storage capacity can be maximal and similar to the one
obtained with static synapses, that is, without activity-dependent processes.
We conclude that depressing synapses with a certain level of facilitation allow
to recover the good retrieval properties of networks with static synapses while
maintaining the nonlinear characteristics of dynamic synapses, convenient for
information processing and coding.
