I just wrote a python program to convert decision trees into neural networks.
The idea is pretty much described in this paper: https://www.cise.ufl.edu/~arunava/papers/clnl94.pdf
It was really fun and I hope to put up the code onto github once I do some analysis of how effective (or ineffective) it is.

My hope is to apply this to a random forest and then train each resultant net in order to get better performance than random forests.