Neural Networks as Sources of Chaotic Motor Activity in Ants and How Complexity Develops at the Social Scale

We discuss a Neural Network model generating activation signals for locomotion in ants. The signals are chaotic and so are the temporal patterns of spontaneous activations in single ants. Active ants are able to move and interact with other nest mates. This process of movement-interaction generates periodic pulses of activity once the number of individuals reaches a certain density value. An algorithmic complexity measure is used for identifying accurately the transition from chaos into order. Finally, an Iterated Function System analysis reveals the richness of dynamical behavior that emerges when ant colonies are self-poised near such a transition.

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