  We report on both analytical and numerical results concerning stochastic
Hopfield--like neural automata exhibiting the following (biologically inspired)
features: (1) Neurons and synapses evolve in time as in contact with respective
baths at different temperatures. (2) The connectivity between neurons may be
tuned from full connection to high random dilution or to the case of networks
with the small--world property and/or scale-free architecture. (3) There is
synaptic kinetics simulating repeated scanning of the stored patterns. Though
these features may apparently result in additional disorder, the model
exhibits, for a wide range of parameter values, an extraordinary computational
performance, and some of the qualitative behaviors observed in natural systems.
In particular, we illustrate here very efficient and robust associative memory,
and jumping between pattern attractors.
