Expected Float Entropy Minimisation: A Relationship Content Theory of Consciousness

Explore the theory of Expected Float Entropy minimisation (EFE minimisation) in this 46-minute conference talk from the Models of Consciousness Conference. Delve into a mathematical approach to explaining how the brain defines the content of consciousness up to relationship isomorphism. Learn about the contrast between EFE minimisation and other theories like Karl Friston's Free Energy Principle and Giulio Tononi's Integrated Information Theory. Discover how EFE involves a version of conditional Shannon Entropy parameterized by relationships and how it can be considered a generalization of the initial topology. Understand the process of finding primary relational structures defined by the system itself, and how objects are represented within this framework. Examine the development of computationally cheaper surrogates for EFE to aid in the theory's application. Follow the presentation through various topics including information context, Bayes Theorem, mathematical formulation, machine learning, brain data, and relationship isomorphisms.

Introduction
Outline
Information
Context
The Brain
Bayes Theorem
Relationships
Mathematical formulation
Player problem forger
Observations
Float Entropy
Expected Float Entropy
Examples
Machine Learning
More Relationships
Brain Data
Relationship Isomorphisms
Relationship Isomorphism