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This paper proposes a neural network architecture designed to exhibit learning and communication capabilities via imitation. Our architecture allows a "proto imitation" behavior using the "perception ambiguity" inherent to real environments. In the perspective of turn-taking and gestural communication between two agents, new experiments on movement synchronization in an interaction game are presented. Synchronization is obtained as a global attractor depending on the coupling between agents' dynamic. We also discuss the non-supervised context of the imitation process and we present new experiments in which the same architecture is able to learn perception-action associations without any explicit reinforcement. The learning is based on the ability to detect novelty or irregularities in the communication rhythm.

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International audienceThe goal of this paper is to adapt a already known traffic forecasting methodology, based on statistical data processing in the field of wavelets, to the case of WiMax networks, to predict where and when upgrading must take place

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