Energy Decay Network (EDeN)
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
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| _version_ | 1866910157033701376 |
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| author | Shelley, Jamie Nicholas Consultancy, Optishell |
| author_facet | Shelley, Jamie Nicholas Consultancy, Optishell |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2103_15552 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Energy Decay Network (EDeN) Shelley, Jamie Nicholas Consultancy, Optishell Neural and Evolutionary Computing Artificial Intelligence 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. |
| title | Energy Decay Network (EDeN) |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2103.15552 |