  We study the effect of competition between short-term synaptic depression and
facilitation on the dynamical properties of attractor neural networks, using
Monte Carlo simulation and a mean field analysis. Depending on the balance
between depression, facilitation and the noise, the network displays different
behaviours, including associative memory and switching of the activity between
different attractors. We conclude that synaptic facilitation enhances the
attractor instability in a way that (i) intensifies the system adaptability to
external stimuli, which is in agreement with experiments, and (ii) favours the
retrieval of information with less error during short--time intervals.
