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.

翻译：本文及随附的Python与C++框架源于作者对狭隘（基于判别式）人工智能（AI）所存在问题的认知。该框架试图通过潜在的结构表达，利用一种通用的调节/交换值（能量），实现经验的遗传传递，从而构建一种模型，使神经架构与所有单元过程在遗传和实时信号处理影响的共同作用下协同发展；成功的路径由每个周期内脉冲分布的稳定性所定义，而这种稳定性受遗传编码的形态发育偏好影响。这些原则旨在创建一个多样且鲁棒的网络，能够通过在专为大规模迁移学习至其他媒介而设计的模拟环境中进行训练，从而适应通用任务。