  Limitations in processing capabilities and memory of today's computers make
spiking neuron-based (human) whole-brain simulations inevitably characterized
by a compromise between bio-plausibility and computational cost. It translates
into brain models composed of a reduced number of neurons and a simplified
neuron's mathematical model, leading to the search for new simulation
strategies. Taking advantage of the sparse character of brain-like computation,
the event-driven technique could represent a way to carry out efficient
simulation of large-scale Spiking Neural Networks (SNN). The recent Leaky
Integrate-and-Fire with Latency (LIFL) spiking neuron model is event-driven
compatible and exhibits some realistic neuronal features, opening new avenues
for brain modelling. In this paper we introduce FNS, the first LIFL-based
spiking neural network framework, which combines spiking/synaptic neural
modelling with the event-driven approach, allowing us to define heterogeneous
neuron modules and multi-scale connectivity with delayed connections and
plastic synapses. In order to allow multi-thread implementations a novel
parallelization strategy is also introduced. This paper presents mathematical
models, software implementation and simulation routines on which FNS is based.
Finally, a brain subnetwork is modeled on the basis of real brain structural
data, and the resulting simulated activity is compared with associated brain
functional (source-space MEG) data, demonstrating a good matching between the
activity of the model and that of the experimetal data. This work aims to lay
the groundwork for future event-driven based personalised brain models.
