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1) Dataflow matrix machines (DMMs) generalize neural nets by replacingstreams of numbers with linear streams (streams supporting linearcombinations), allowing arbitrary input and output arities for activationfunctions, countable-sized networks with finite dynamically changeable activepart capable of unbounded growth, and a very expressive self-referentialmechanism.  2) DMMs are suitable for general-purpose programming, while retaining the keyproperty of recurrent neural networks: programs are expressed via matrices ofreal numbers, and continuous changes to those matrices produce arbitrarilysmall variations in the associated programs.  3) Spaces of V-values (vector-like elements based on nested maps) areparticularly useful, enabling DMMs with variadic activation functions andconveniently representing conventional data structures.

Dataflow Matrix Machines and V-values: a Bridge between Programs and Neural Nets