Simulating spiking neuron networks with a sequential event-driven
approach consumes less computation time than clock-driven methods. On the
other hand, a parallel computing support provides a larger amount of material
ressources for optimizing simulation performance. This PhD dissertation
proposes an event-driven, multithreaded and distributed framework for simulating
spiking neuron networks. The name of the simulator is the acronym
DAMNED, for Distributed And Multithreaded Neural Event-Driven simulation
framework. DAMNED distributes the neurons and connections of the network
on the material ressources synchronized throught a decentralized global
virtual time. DAMNED also couples a local multithreaded processing to the
distributed hardware. DAMNED allows to speed up the simulation and to
manage wider neural networks than sequential processing. DAMNED is suited
to run many models of spiking neurons and networks, and most material
supports are workable. Using DAMNED is presented first on toy networks and
on a benchmark. Next, DAMNED is applied to modelling the control of saccadic
eye movements. Completely based on spiking neurons, the model study
interactions between the neural circuits of the saccadic system that are located
in the brainstem. The model helps validating the hypothesis that the saccade
amplitude would be encoded by a vector summation of the activities in the
superior colliculus motor map rather than a vector average, from comparison
with data obtained in the simulation. Even if further developments and improvements
may be forecasted, the originality of the work is to couple eventdriven
and distributed programming. Moreover, among the parallel simulators
for spiking neuron networks, DAMNED is the only one to couple an internal
multithreading of the logic processes and a distributed architecture of physical
processes. Hence DAMNED is an advance in the area of simulating spiking
neuron networks, mainly for wide size networks. Experiments on simulating
the control of saccadic eye movements by a spiking neural network model and
their contributions for the neuroscience community confirm the perspectives
for further uses of the present work.


Keywords : Spiking Neural Networks, Event-driven simulations, Distributed
Discrete Event Simulations, Multithreading, Computational neuroscience, Saccadic
system