A novel method for efficiently simulating large scale realistic neural networks is described. Most information transmission in these networks is accomplished by the so called action potentials, events which are considerably sparse and well-localized in time. This facilitates a dramatic reduction of the computational load through the application of the event-driven simulation schemes. However, some complex neuronal models require the simulator to calculate large expressions, in order to update the neuronal state variables between these events. This requirement slows down these neural state updates, impeding the simulation of very active large neural populations in real-time. Moreover, neurons of some of these complex models produce firings (action potentials) some time after the arrival of the presynaptic potentials. The calculation of this delay involves the computation of expressions that sometimes are difficult to solve analytically. To deal with these problems, our method makes use of precalculated lookup tables for both, fast update of the neural variables and the prediction of the firing delays, allowing efficient simulation of large populations with detailed neural models.