  This paper and accompanying Python and C++ Framework is the product of the
authors 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 by genetic and
real time signal processing influences; successful routes are defined by
stability of the spike distribution per epoch which is influenced by
genetically encoded morphological development biases.These principles are aimed
towards creating a diverse and robust network that is capable of adapting to
general tasks by training within a simulation designed for transfer learning to
other mediums at scale.
