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.. .
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 .
A
, leading to the search for new simulation strategies
R
eventdriven technique allows us
the event-driven technique could represent a way
R
horizons in whole-brain
avenues for brain
R
present FNS, a
introduce FNS, the first
D
exact event-driven
R
implemented in Java and oriented to wholebrain simulations. FNS
, which
R
whole-brain
neural
A
neuron
R
, providing fast simulations at the same time. A
. In order to allow multi-thread implementations a
R
implemented in order to further speed up simulations
introduced
R
reduced brain network model (1400 neurons and 45000 synapses) is synthesized
brain subnetwork is modeled
R
model
simulated
R
. The conducted test shows
, demonstrating
A
the
R
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..
experimetal data. This work aims to lay the groundwork for future event-driven based personalised brain models