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.
Sylvain Chevallier (LISV)
Helene Paugam-Moisy (LAMIA - UA)
Michele Sebag (Universite Paris-Sud, CNRS)
Related Events (a corresponding poster, oral, or spotlight)
2010 Poster: SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system »
Tue. Dec 7th 08:00 -- 08:00 AM Room
More from the Same Authors
2021 : The NeurIPS 2021 BEETL Competition: Benchmarks for EEG Transfer Learning + Q&A »
Xiaoxi Wei · Vinay Jayaram · Sylvain Chevallier · Giulia Luise · Camille Jeunet · Moritz Grosse-Wentrup · Alexandre Gramfort · Aldo A Faisal
2018 : TBA 4 »
2014 Workshop: Challenges in Machine Learning workshop (CiML 2014) »
Isabelle Guyon · Evelyne Viegas · Percy Liang · Olga Russakovsky · Rinat Sergeev · Gábor Melis · Michele Sebag · Gustavo Stolovitzky · Jaume Bacardit · Michael S Kim · Ben Hamner