Title: FNS: an event-driven spiking neural network framework for efficient simulations of large-scale brain models,
Abstract: 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. Taking advantage of the sparse character of
brain-like computation, eventdriven technique allows us 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 horizons
in whole-brain modelling. In this paper we present FNS, a LIFL-based exact
event-driven spiking neural network framework implemented in Java and oriented
to wholebrain simulations. FNS combines spiking/synaptic whole-brain modelling
with the event-driven approach, allowing us to define heterogeneous modules and
multi-scale connectivity with delayed connections and plastic synapses,
providing fast simulations at the same time. A novel parallelization strategy
is also implemented in order to further speed up simulations. This paper
presents mathematical models, software implementation and simulation routines
on which FNS is based. Finally, a reduced brain network model (1400 neurons and
45000 synapses) is synthesized on the basis of real brain structural data, and
the resulting model activity is compared with associated brain functional
(source-space MEG) data. The conducted test shows a good matching between the
activity of model and that of the emulated subject, in outstanding simulation
times (about 20s for simulating 4s of activity with a normal PC). Dedicated
sections of stimuli editing and output synthesis allow the neuroscientist to
introduce and extract brain-like signals, respectively...