A Mathematical Walkthrough and Discussion of the Free Energy Principle
Beren Millidge, Anil Seth, Christopher L Buckley

TL;DR
This paper provides a detailed, accessible mathematical explanation of the Free Energy Principle, discussing its core claims, assumptions, limitations, and ongoing debates in neuroscience and machine learning.
Contribution
It offers an intuitive walkthrough of the FEP's formulation, clarifying its mathematical foundations and addressing controversies and assumptions within the theory.
Findings
Clarifies the mathematical formulation of the FEP
Discusses assumptions and limitations of the theory
Summarizes current debates and perspectives
Abstract
The Free-Energy-Principle (FEP) is an influential and controversial theory which postulates a deep and powerful connection between the stochastic thermodynamics of self-organization and learning through variational inference. Specifically, it claims that any self-organizing system which can be statistically separated from its environment, and which maintains itself at a non-equilibrium steady state, can be construed as minimizing an information-theoretic functional -- the variational free energy -- and thus performing variational Bayesian inference to infer the hidden state of its environment. This principle has also been applied extensively in neuroscience, and is beginning to make inroads in machine learning by spurring the construction of novel and powerful algorithms by which action, perception, and learning can all be unified under a single objective. While its expansive and often…
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Taxonomy
TopicsNeural dynamics and brain function · Advanced Thermodynamics and Statistical Mechanics · Functional Brain Connectivity Studies
