Dataflow Matrix Machines as a Generalization of Recurrent Neural   Networks
  Dataflow matrix machines are a powerful generalization of recurrent neural networks. They work with multiple types of arbitrary linear streams, multiple types of powerful neurons, and allow to incorporate higher-order constructions. We expect them to be useful in machine learning and probabilistic programming, and in the synthesis of dynamic systems and of deterministic and probabilistic programs. 
Michael Bukatin and Steve Matthews and Andrey Radul
cs.NE