Symbolic memory for humanoid robots using hierarchical bifurcations of attractors in nonmonotonic neural networks

Bifurcations of attractors take place in associative neural networks with nonmonotonic activation functions, depending on the degree of correlations between stored patterns and the parameter of nonmonotonicity. We describe the bifurcations when auto-correlation based feature vectors of motion patterns of humanoid robots, which are hierarchically correlated, are stored. Also, we describe a memory system which utilizes the neural network dynamics and hierarchically maintains specific and conceptual memories of motions of humanoid robots. The level of abstraction is controlled by a parameter in the retrieval phase without changing the connection weights.