能量衰减网络（EDeN）
Energy Decay Network (EDeN)
AI总结：
本文提出EDeN网络，通过能量调节机制实现神经架构与单元过程的协同进化，以提升网络的多样性和鲁棒性，适应泛化任务。
摘要：
本文及配套的Python和C++框架是作者对狭义（基于辨别）人工智能问题的感知产物。该框架试图通过潜在的结构表达来开发经验的遗传转移，利用共同的调节/交换值（能量）来创建一种模型，其中神经架构和所有单元过程通过遗传和实时信号处理影响协同进化；成功路线由每轮的尖峰分布稳定性定义，这受到遗传编码的形态发育偏倚影响。这些原则旨在创建一个多样化且稳健的网络，能够通过在为迁移学习设计的模拟中训练，适应其他介质的规模化任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
