An Entropy Equation for Energy

TL;DR

This paper proposes an entropy equation for measuring energy, linking it to brain efficiency, clustering algorithms, and fundamental physics, suggesting broad applications across domains.

Contribution

It introduces a simple, generic entropy equation for energy measurement, connecting brain efficiency, clustering, and physics in a novel way.

Findings

Entropy equation E = (mean * variance) derived

Links energy efficiency to neural wiring and clustering

Discusses implications for physics and black holes

Abstract

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.