Active Inference: a Process Theory

This article describes a process theory based on active inference and belief propagation. Starting from the premise that all neuronal processing (and action selection) can be explained by maximizing Bayesian model evidence--or minimizing variational free energy--we ask whether neuronal responses can be described as a gradient descent on variational free energy. Using a standard (Markov decision process) generative model, we derive the neuronal dynamics implicit in this description and reproduce a remarkable range of well-characterized neuronal phenomena. These include repetition suppression, mismatch negativity, violation responses, place-cell activity, phase precession, theta sequences, theta-gamma coupling, evidence accumulation, race-to-bound dynamics, and transfer of dopamine responses. Furthermore, the (approximately Bayes' optimal) behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action.

Publication type: 
Articolo
Author or Creator: 
Friston, Karl
FitzGerald, Thomas
Rigoli, Francesco., Schwartenbeck, Philipp
Pezzulo, Giovanni
Publisher: 
MIT Press,, Cambridge, Mass. , Stati Uniti d'America
Source: 
Neural computation (2016). doi:10.1162/NECO_a_00912
Date: 
2016
Resource Identifier: 
http://www.cnr.it/prodotto/i/361698
https://dx.doi.org/10.1162/NECO_a_00912
info:doi:10.1162/NECO_a_00912
http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00912#.WD7ZZyPhBQp
Language: 
Eng
ISTC Author: 
Giovanni Pezzulo's picture
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