  Dataflow matrix machines arise naturally in the context of synchronous
dataflow programming with linear streams. They can be viewed as a rather
powerful generalization of recurrent neural networks. Similarly to recurrent
neural networks, large classes of dataflow matrix machines are described by
matrices of numbers, and therefore dataflow matrix machines can be synthesized
by computing their matrices. At the same time, the evidence is fairly strong
that dataflow matrix machines have sufficient expressive power to be a
convenient general-purpose programming platform. Because of the network nature
of this platform, programming patterns often correspond to patterns of
connectivity in the generalized recurrent neural networks understood as
programs. This paper explores a variety of such programming patterns.
