Energy Decay Network (EDeN)

• URL: http://arxiv.org/abs/2103.15552v5
• Date: Wed, 18 Sep 2024 00:19:45 GMT
• Title: Energy Decay Network (EDeN)
• Authors: Jamie Nicholas Shelley, Optishell Consultancy,
• Abstract summary: The Framework attempts to develop a genetic transfer of experience through potential structural expressions. Successful routes are defined by stability of the spike distribution per epoch.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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