Emerging research areas in neuroscience are requiring simulation of large and detailed spiking neural networks. Although event-driven methods have been recently proposed to simulate these networks, they still present some drawbacks. To obtain the advantages of an event-driven simulation method and a traditional time-driven method, we present a hybrid method. This method efficiently simulates neural networks composed of several neural models: highly active neurons or neurons defined by very-complex model are simulated using a time-driven method whereas other neurons are simulated using an event-driven method based in lookup tables. To perform a comparative study of this hybrid method in terms of speed and accuracy, a model of the cerebellar granular layer has been simulated. The performance results showed that a hybrid simulation can provide considerable advantages when the network is composed of neurons with different characteristics.