  Dataflow matrix machines are self-referential generalized recurrent neural
nets. The self-referential mechanism is provided via a stream of matrices
defining the connectivity and weights of the network in question. A natural
question is: what should play the role of untyped lambda-calculus for this
programming architecture? The proposed answer is a discipline of programming
with only one kind of streams, namely the streams of appropriately shaped
matrices. This yields Pure Dataflow Matrix Machines which are networks of
transformers of streams of matrices capable of defining a pure dataflow matrix
machine.
