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User sylvchev
title SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system
Authors Sylvain Chevallier, Hélène Paugam-Moisy, Michèle Sebag
Source Advances in Neural Information Processing Systems (NIPS)
Year 2010
Type Conference
Type of publication Other

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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.
Link http://books.nips.cc/papers/files/nips23/NIPS2010_1134.pdf
Bibtex @incollection{NIPS2010_1134,
title = {SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system},
author = {Sylvain Chevallier and H\'{e}l\`{e}ne Paugam-Moisy and Michele Sebag},
booktitle = {Advances in Neural Information Processing Systems 23},
editor = {J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta},
pages = {379--387},
year = {2010}

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