  We study a model of spiking neurons, with recurrent connections that result
from learning a set of spatio-temporal patterns with a spike-timing dependent
plasticity rule and a global inhibition. We investigate the ability of the
network to store and selectively replay multiple patterns of spikes, with a
combination of spatial population and phase-of-spike code. Each neuron in a
pattern is characterized by a binary variable determining if the neuron is
active in the pattern, and a phase-lag variable representing the spike-timing
order among the active units. After the learning stage, we study the dynamics
of the network induced by a brief cue stimulation, and verify that the network
is able to selectively replay the pattern correctly and persistently. We
calculate the information capacity of the network, defined as the maximum
number of patterns that can be encoded in the network times the number of bits
carried by each pattern, normalized by the number of synapses, and find that it
can reach a value $\alpha_\text{max}\simeq 0.27$, similar to the one of
sequence processing neural networks, and almost double of the capacity of the
static Hopfield model. We study the dependence of the capacity on the global
inhibition, connection strength (or neuron threshold) and fraction of neurons
participating to the patterns. The results show that a dual population and
temporal coding can be optimal for the capacity of an associative memory.
