Bidirectional ARTMAP: an artificial mirror neuron system

The recent detection of mirror neurons in monkeys suggests that brains encode parts of an observed action in a similar way they encode own actions. This paper models such a mirror system by means of adaptive resonance theory (ART) artificial neural networks coupling them in a manner similar to ARTMAP systems. Particularly, a bidirectional ARTMAP system (BiARTMAP) is created. The system associates executed actions with consequent action-effects. The associative structure gives the system mirror capabilities: on the one hand, perceived environmental changes cause an action association. On the other hand, activated action patterns cause the expectation of resulting environmental change. We also show that many other proposed cognitive processes relate to the BiARTMAP architecture. Future work includes the incorporation of situational dependencies, the combination of BiARTMAP with vector associative maps (VAMs), and the integration of BiARTMAP in a behavioral module enabling anticipatory behavior. Application wise, BiARTMAP can be applied as a general classifier and/or associative network.