Here is proposed a review of the different choices to structure spike trains, using deterministic metrics. Temporal constraints observed in biological or computational spike trains are first taken into account The relation with existing neural codes (rate coding, rank coding, phase coding, ..) is then discussed. To which extend the "neural code" contained in spike trains is related to a metric appears to be a key point, a generalization of the Victor-Purpura metric family being proposed for temporal constrained causal spike trains. KEY WORDS Spiking network. Neural code. Gibbs distribution. 1 Global time constraints in spike trains The output of a neural network is a set of events, defined by their occurrence times, up to some precision: F = {