Abstract : In this paper we present a novel approach to model Spiking Neural Networks. These networks, referred as third generation ones, add a new dimension to the second generation: the temporal axis. We propose a formalisation of Spiking Neural Networks based on Timed Automata Networks. Neurons are modelled as timed automata waiting for inputs on a number of different channels (synapses), for a given amount of time (the accumulation period). When this period is over, the current potential value is computed taking into account the current inputs and the previous decayed potential value. If the current potential overcomes a given threshold, the automaton emits a broad- cast signal over its output channel, otherwise it restarts another accumulation period. After each emission, the automaton is constrained to remain inactive for a fixed refractory period. Spiking neural networks are formalised as sets of automata, one for each neuron, running in parallel and sharing channels ac- cording to the structure of the network. The model is then validated against some properties defined via proper temporal logic formulae.