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Active Inference: A Process Theory

Friston, K; FitzGerald, T; Rigoli, F; Schwartenbeck, P; Pezzulo, G; (2017) Active Inference: A Process Theory. Neural Computation , 29 (1) pp. 1-49. 10.1162/NECO_a_00912. Green open access

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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.

Type: Article
Title: Active Inference: A Process Theory
Open access status: An open access version is available from UCL Discovery
DOI: 10.1162/NECO_a_00912
Publisher version: http://dx.doi.org/10.1162/NECO_a_00912
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Artificial Intelligence, Neurosciences, Computer Science, Neurosciences & Neurology, DECISION-MAKING, DOPAMINE NEURONS, PARIETAL CORTEX, FREE-ENERGY, EVIDENCE ACCUMULATION, BAYESIAN-INFERENCE, INFORMATION-THEORY, PREDICTION ERRORS, CHOICE BEHAVIOR, VISUAL-CORTEX
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Department of Neuromuscular Diseases
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: http://discovery.ucl.ac.uk/id/eprint/1530701
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