Before revision:  Broadly, our work blends chaotic theory and artificial neural networks to answer the long standing question of how neural systems can learn the structure underlying temporal sequences of stimuli .
After revision:  Broadly, our work blends chaotic theory and artificial neural networks to answer the long standing question of how neural systems can learn the dynamic nature of their environment, and systems within their environment, through the mechanism of invertible generalized synchronization .