Friston, Karl, FitzGerald, Thomas ORCID: https://orcid.org/0000-0002-3855-1591, Rigoli, Francesco, Schwartenbeck, Philipp and Pezzulo, Giovanni
(2017)
Active inference: A process theory.
Neural Computation, 29 (1).
pp. 1-49.
ISSN 0899-7667
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Abstract
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.
Item Type: | Article |
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Faculty \ School: | Faculty of Social Sciences > School of Psychology |
Depositing User: | Pure Connector |
Date Deposited: | 05 Jan 2017 00:01 |
Last Modified: | 04 Mar 2024 17:33 |
URI: | https://ueaeprints.uea.ac.uk/id/eprint/61907 |
DOI: | 10.1162/NECO_a_00912 |
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