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title SpikeAnts: a spiking neuron network modelling the emergence of organization in a complex system.
Authors Chevallier, S. , Paugam-Moisy, H. and Sebag, M.
Source "Advances in Neural Information Processing Systems 23" : p. 114-119.
Year 2010
Type Conference
Type of publication ISC-PIF scholar

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Abstract The emergence of organization is at the core of many complex systems, from neural cell assemblies to living insect societies. Since information processing by a Spiking Neuron Network (SNN) is based on temporal dynamics, SNNs appear to be powerful neural information processing systems for modelling the emergence of collective phenomena in complex systems.
A spatio-temporal model, called SpikeAnts, is proposed for reproducing the emergence of synchronized activities in social insect colonies: each ant agent is modelled by only two spiking neurons and the ant colony is a sparsely connected SNN.
Individual decision making results from the competition of an active and a passive neurons which process information received from neighbouring agents.
The spatio-temporal coupling of the ant agents results in three types of temporal patterns at the population scale: asynchronous, synchronous aperiodic, and synchronous periodic, similar to real-world behaviour observed in ant societies.
A phase diagram of the emergent synchronization patterns with respect to two order parameters, respectively modelling the ant sociability and receptivity, is presented and discussed.

author = {Chevallier, S. and Paugam-Moisy, H. and Sebag, M.},
title = {{S}pike{A}nts: a spiking neuron network modelling the emergence
of organization in a complex system},
year = {2010},
month = {December},
x-equipe = {AO},
x-support = {actes},
x-type = Poste CR2 'Cognition Sociale' fléché vers le CREA en 2010.
Mots clé: modélisation de systèmes sociaux, reconstruction de dynamiques sociales, systèmes complexes, sciences cognitives

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