Active inference is a mathematical framework which originated in computational
neuroscience as a theory of how the brain implements action, perception and learning …

While interoception is of major neuroscientific interest, its precise definition and delineation
from exteroception continue to be debated. Here, we propose a functional distinction …

Recent advances in neuroscience have characterised brain function using mathematical
formalisms and first principles that may be usefully applied elsewhere. In this paper, we …

The Kalman filter is an algorithm for the estimation of hidden variables in dynamical systems
under linear Gauss-Markov assumptions with widespread applications across different …

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement
learning and sequential decision making, covering value-based RL, policy-gradient …

During communication, humans express their emotional states using various modalities (eg,
facial expressions and gestures), and they estimate the emotional states of others by paying …

Expected free energy (EFE) is a central quantity in active inference which has recently
gained popularity due to its intuitive decomposition of the expected value of control into a …

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient,
adaptive, and explainable by learning an explicit model of the environment. While the …

Building autonomous---ie, choosing goals based on one's needs--and adaptive--ie,
surviving in ever-changing environments--agents has been a holy grail of artificial …

Based on a generative model (GM) and beliefs over hidden states, the free energy principle
(FEP) enables an agent to sense and act by minimizing a free energy bound on Bayesian …