We propose an event-driven framework dedicated to the design and the simulation of networks of spiking neurons. It consists of an abstract model of spiking neurons and an efficient event-driven simulation engine so as to achieve good performance in the simulation phase while maintaining a high level of flexibility and programmability in the modelling phase. Our model of neurons encompasses a large class of spiking neurons ranging from usual leaky integrate-and-fire neurons to more abstract neurons, e.g. defined as complex finite state machines. As a result, the proposed framework allows the simulation of large networks that can be composed of unique or different types of neurons.