This paper and accompanying Python/C++ framework is the product of the author’s perceived problems with narrow (discrimination based) AI (Artificial Intelligence). The framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value (‘energy’) to create a model whereby neural architecture and all unit processes are co-dependently developed. These expressions are born from fractal definition, stochastically tuned and managed by genetic experience; successful routes are maintained through global rules: Stability of signal propagation/function over cross functional (external state, internal immediate state, and genetic bias towards selection of previous expressions). These principles are aimed towards creating a diverse and robust network, hopefully reducing the need for transfer learning and computationally expensive translations as demand on compute increases.