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The goal of this paper is to propose a model of the hippocampal system that reconciles the presence of neurons that look like "place cells" with the implication of the hippocampus (Hs) in other cognitive tasks (e.g., complex conditioning acquisition and memory tasks). In the proposed model, "place cells" or "view cells" are learned in the perirhinal and entorhinal cortex. The role of the Hs is not fundamentally dedicated to navigation or map building, the Hs is used to learn, store, and predict transitions between multimodal states. This transition prediction mechanism could be important for novelty detection but, above all, it is crucial to merge planning and sensory-motor functions in a single and coherent system. A neural architecture embedding this model has been successfully tested on an autonomous robot, during navigation and planning in an open environment.

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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.