de Vries, B;
Friston, KJ;
(2017)
A factor graph description of deep temporal active inference.
Frontiers in Computational Neuroscience
, 11
, Article 95. 10.3389/fncom.2017.00095.
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Abstract
Active inference is a corollary of the Free Energy Principle that prescribes how self-organizing biological agents interact with their environment. The study of active inference processes relies on the definition of a generative probabilistic model and a description of how a free energy functional is minimized by neuronal message passing under thatmodel. This paper presents a tutorial introduction to specifying active inference processes by Forney-style factor graphs (FFG). The FFG framework provides both an insightful representation of the probabilistic model and a biologically plausible inference scheme that, in principle, can be automatically executed in a computer simulation. As an illustrative example, we present an FFG for a deep temporal active inference process. The graph clearly shows how policy selection by expected free energy minimization results from free energy minimization per se, in an appropriate generative policy model.
| Type: | Article |
|---|---|
| Title: | A factor graph description of deep temporal active inference |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fncom.2017.00095 |
| Publisher version: | http://dx.doi.org/10.3389/fncom.2017.00095 |
| Language: | English |
| Additional information: | © 2017 de Vries and Friston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | Active inference, free-energy principle, factor graphs, belief propagation, message passing, multi-scale dynamical systems |
| UCL classification: | UCL 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 > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10033849 |
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