  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 grandiose claims have spurred significant debates in both philosophy and
theoretical neuroscience, the mathematical depth and lack of accessible
introductions and tutorials for the core claims of the theory have often
precluded a deep understanding within the literature. Here, we aim to provide a
mathematically detailed, yet intuitive walk-through of the formulation and
central claims of the FEP while also providing a discussion of the assumptions
necessary and potential limitations of the theory. Additionally, since the FEP
is a still a living theory, subject to internal controversy, change, and
revision, we also present a detailed appendix highlighting and condensing
current perspectives as well as controversies about the nature, applicability,
and the mathematical assumptions and formalisms underlying the FEP.
