论文标题
能量的熵方程
An Entropy Equation for Energy
论文作者
论文摘要
本文描述了一个熵方程，但应用于测量能量而不是信息。因此，与人脑有关，这两个量都可以用来表示存储的信息。人脑利用能源效率形成其结构，这很可能与神经元接线有关。该能源效率也可以用作聚类算法的基础，该算法在不同的论文中进行了描述。本文更多地是关于全局属性的讨论，其中用于聚类算法的规则还可以创建熵方程e =（平均 *方差）。这表明工作是通过熵的“变化”释放的能量来完成的。该方程式是如此简单和通用，以至于可以为完全不同的域提供参数，在该域以有关物理及其他地区全球能量特性的讨论结束。与爱因斯坦的相对论方程式进行了比较，并且大胆的建议是黑洞内部的能量为零。
This paper describes an entropy equation, but one that should be used for measuring energy and not information. In relation to the human brain therefore, both of these quantities can be used to represent the stored information. The human brain makes use of energy efficiency to form its structures, which is likely to be linked to the neuron wiring. This energy efficiency can also be used as the basis for a clustering algorithm, which is described in a different paper. This paper is more of a discussion about global properties, where the rules used for the clustering algorithm can also create the entropy equation E = (mean * variance). This states that work is done through the energy released by the 'change' in entropy. The equation is so simplistic and generic that it can offer arguments for completely different domains, where the journey ends with a discussion about global energy properties in physics and beyond. A comparison with Einstein's relativity equation is made and also the audacious suggestion that a black hole has zero-energy inside.
