  We investigate the efficient transmission and processing of weak,
subthreshold signals in a realistic neural medium in the presence of different
levels of the underlying noise. Assuming Hebbian weights for maximal synaptic
conductances -- that naturally balances the network with excitatory and
inhibitory synapses -- and considering short-term synaptic plasticity affecting
such conductances, we found different dynamic phases in the system. This
includes a memory phase where population of neurons remain synchronized, an
oscillatory phase where transitions between different synchronized populations
of neurons appears and an asynchronous or noisy phase. When a weak stimulus
input is applied to each neuron, increasing the level of noise in the medium we
found an efficient transmission of such stimuli around the transition and
critical points separating different phases for well-defined different levels
of stochasticity in the system. We proved that this intriguing phenomenon is
quite robust, as it occurs in different situations including several types of
synaptic plasticity, different type and number of stored patterns and diverse
network topologies, namely, diluted networks and complex topologies such as
scale-free and small-world networks. We conclude that the robustness of the
phenomenon in different realistic scenarios, including spiking neurons,
short-term synaptic plasticity and complex networks topologies, make very
likely that it could also occur in actual neural systems as recent
psycho-physical experiments suggest.
