Neil Slater is correct when saying that NEAT itself is not neural networks evolving neural networks, what I believe is the closest framework to what the question is asking would be HyperNEAT http://axon.cs.byu.edu/~dan/778/papers/NeuroEvolution/stanley3**.pdf
HyperNEAT operates in a very similar way to what you are describing, from a ten thousand foot view the algorithm is as follows:
1)You lay out nodes for a rnn in a Cartesian space, 2d, 3d, whatever you dimension you wish, this set of coordinates is called the substrate.
2)A cppn is queried by passing in two coordinates at a time as input, which gives the cppn a search space of a hypercube in 2x the dimension the coordinates are in (for substrates in space > 2d this is very large)
3)The output of the cppn is used to encode connection, weights, biases, of the rnn coordinates
4)Then the rnn is evaluated by your fitness function and but the evolution (speciation, reproduction, etc) is ran on the cppn that encoded the rnn. So you evolve a population of cppn "genotypes" that encode rnn or cnn "phenotypes".
The third iteration of NEAT is ES-Hyperneat where all you need to layout in the substrate is the input and output layers (Hyperneat you must layout all hidden nodes of the substrate statically). It uses a subdivision tree to subdivide the search spaces and query the subdivided root coordinates of this tree with the cppn just like hyperneat, checking variance along the way to decide if the new node is in a "high information" topological space, to "evolve" hidden nodes into the substrate (rnn).