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SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system
Sylvain Chevallier · Helene Paugam-Moisy · Michele Sebag

Tue Dec 07 09:50 AM -- 09:55 AM (PST) @ Regency Ballroom

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.

Author Information

Sylvain Chevallier (LISV)
Helene Paugam-Moisy (LAMIA - UA)
Michele Sebag (Universite Paris-Sud, CNRS)

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