The free energy principle, and its corollary active inference, constitute a bio-inspired theory
that assumes biological agents act to remain in a restricted set of preferred states of the …

This paper develops a Bayesian mechanics for adaptive systems. Firstly, we model the
interface between a system and its environment with a Markov blanket. This affords …

The free energy principle and its corollary, the active inference framework, serve as
theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent …

In theoretical biology, we are often interested in random dynamical systems—like the brain—
that appear to model their environments. This can be formalized by appealing to the …

The spread of ideas is a fundamental concern of today's news ecology. Understanding the
dynamics of the spread of information and its co-option by interested parties is of critical …

While machine learning techniques have been transformative in solving a range of
problems, an important challenge is to understand why they arrive at the decisions they …

Scene understanding and decomposition is a crucial challenge for intelligent systems,
whether it is for object manipulation, navigation, or any other task. Although current machine …

Active inference is a state-of-the-art framework in neuroscience that offers a unified theory of
brain function. It is also proposed as a framework for planning in AI. Unfortunately, the …

In this PhD thesis, we explore and apply methods inspired by the free energy principle to two
important areas in machine learning and neuroscience. The free energy principle is a …

Here we build on recent findings which show that greater alignment between our subjective
experiences (how we feel) and physiological states (measurable changes in our body) plays …