This paper presents a new model for simulating Spiking Neural Networks using discrete event simulation which might possibly offer advantages concerning simulation speed and scalability. Spiking Neural Networks are considered as a new computation paradigm, representing an enhancement of Artificial Neural Networks by offering more flexibility and degree of freedom for modeling computational elements. Although this type of Neural Networks is rather new and there is not very much known about its features, it is clearly more powerful than its predecessor, being able to simulate Artificial Neural Networks in real time but also offering new computational elements that were not available previously. Unfortunately, the simulation of Spiking Neural Networks currently involves the use of continuous simulation techniques which do not scale easily to large networks with many neurons. Within the scope of the present paper, we discuss a new model for Spiking Neural Networks, which allows the use of discrete event simulation techniques, possibly offering enormous advantages in terms of simulation flexibility and scalability without restricting the qualitative computational power.