Topic: free-energy principle

Kathryn Nave discusses how living systems acquire meaning and intelligence through organization, constraints, and active inference. The conversation connects the free-energy principle and organizational closure to questions about brains, cognition, agency, and the boundaries of intelligent life.

The free-energy principle and active inference have received a significant attention in the fields of neuroscience and machine learning. However, it remains to be established whether active inference is an apt explanation for any given neural network that actively exchanges with its environment. To address this issue, we show that a class of canonical neural networks of rate coding models implicitly performs variational Bayesian inference under a well-known form of partially observed Markov decision process model (Isomura, Shimazaki, Friston, Commun Biol, 2022). Based on the proposed theory, w