This paper presents a new model for simulating Spiking Neural Networksusing discrete event simulation which might possibly offer advantagesconcerning simulation speed and scalability. Spiking Neural Networksare considered as a new computation paradigm, representing an enhancementof Artificial Neural Networks by offering more flexibility and degreeof freedom for modeling computational elements. Although this typeof Neural Networks is rather new and there is not very much knownabout its features, it is clearly more powerful than its predecessor,being able to simulate Artificial Neural Networks in real time butalso offering new computational elements that were not availablepreviously. Unfortunately, the simulation of Spiking Neural Networkscurrently involves the use of continuous simulation techniques whichdo not scale easily to large networks with many neurons. Within thescope of the present paper, we discuss a new model for Spiking NeuralNetworks, which allows the use of discrete event simulation techniques,possibly offering enormous advantages in terms of simulation flexibilityand scalability without restricting the qualitative computationalpower.