Energy Decay Network

Abstract

This paper and accompanying Python/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 . 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.