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Neural Comput. 2017 Jan;29(1):1-49. doi: 10.1162/NECO_a_00912. Epub 2016 Nov 21.

Active Inference: A Process Theory.

Author information

1
Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, U.K. k.friston@ucl.ac.uk.
2
Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, U.K., and Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5BE, U.K. thomas.fitzgerald@ucl.ac.uk.
3
Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, U.K. f.rigoli@ucl.ac.uk.
4
Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, U.K.; Max Planck-UCL Centre for Computational Psychiatry and Ageing Research, London, WC1B 5BE, U.K.; Centre for Neurocognitive Research, University of Salzburg, 5020 Salzburg, Austria; and Neuroscience Institute, Christian-Doppler-Klinik, Paracelsus Medical University Salzburg, A-5020 Salzburg, Austria philipp.schwartenbeck.12@alumni.ucl.ac.uk.
5
Institute of Cognitive Sciences and Technologies, National Research Council, 00185 Rome, Italy giovanni.pezzulo@gmail.com.

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.

PMID:
27870614
DOI:
10.1162/NECO_a_00912

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