Energy Decay Network (EDeN)

TL;DR

The paper introduces Energy Decay Network (EDeN), a framework combining genetic and real-time influences to develop adaptable neural architectures with stability-based training for transfer learning.

Contribution

It proposes a novel energy-based model that co-develops neural structures through genetic and signal processing influences for robust, adaptable AI.

Findings

Framework successfully develops diverse neural architectures.

Models demonstrate stability in spike distribution across epochs.

Potential for transfer learning across different mediums.

Abstract

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.