SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system

Abstract

Many complex systems, ranging from neural cell assemblies to insect societies, involve and rely on some division of labor. How to enforce such a division in a decentralized and distributed way, is tackled in this paper, using a spiking neuron network architecture. Specifically, a spatio-temporal model called SpikeAnts is shown to enforce the emergence of synchronized activities in an ant colony. Each ant is modelled from two spiking neurons; the ant colony is a sparsely connected spiking neuron network. Each ant makes its decision (among foraging, sleeping and self-grooming) from the competition between its two neurons, after the signals received from its neighbor ants. Interestingly, three types of temporal patterns emerge in the ant colony: asynchronous, synchronous, and synchronous periodic foraging activities − similar to the actual behavior of some living ant colonies. A phase diagram of the emergent activity patterns with respect to two control parameters, respectively accounting for ant sociability and receptivity, is presented and discussed.
Fichier principal
Vignette du fichier
NIPS2010_1134.pdf (189.8 Ko) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

inria-00545669 , version 1 (11-12-2010)

Identifiers

  • HAL Id : inria-00545669 , version 1

Cite

Sylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag. SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system. NIPS'2010, Dec 2010, Vancouver, Canada. pp.379--387. ⟨inria-00545669⟩
611 View
417 Download

Share

Gmail Facebook X LinkedIn More