MIRRORING MAPS AND ACTIONS REPRESENTATION THROUGH EMBODIED INTERACTIONS

摘要

In this paper, we present a neural architecture aimed to reproduce the qualitative properties of the mirror neurons system which encodes neural representations of actions either performed or observed. Several biological researches have emphasized some of its important aspects, for instance, the tight coupling between perception and action, the crucial role of timing (temporal information for encoding and detection), or its particular neural connectivity. We attempt to model these in a network of spiking neurons to learn the accurate temporal relationships between sensorimotor maps for action representation. After the learning, the neural connectivity efficiently induces functional capabilities in the whole network exhibiting statistics similar with observed evidences in the mirror neurons system and comparable to those of small-world networks (e.g., scale-free dynamics and hierarchical organization).