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The graphical brain: Belief propagation and active inference
This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MIT Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798592/ https://www.ncbi.nlm.nih.gov/pubmed/29417960 http://dx.doi.org/10.1162/NETN_a_00018 |
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author | Friston, Karl J. Parr, Thomas de Vries, Bert |
author_facet | Friston, Karl J. Parr, Thomas de Vries, Bert |
author_sort | Friston, Karl J. |
collection | PubMed |
description | This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. |
format | Online Article Text |
id | pubmed-5798592 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MIT Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-57985922018-02-05 The graphical brain: Belief propagation and active inference Friston, Karl J. Parr, Thomas de Vries, Bert Netw Neurosci Research This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. MIT Press 2017-12-01 /pmc/articles/PMC5798592/ /pubmed/29417960 http://dx.doi.org/10.1162/NETN_a_00018 Text en © 2017 Massachusetts Institute of Technology http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Friston, Karl J. Parr, Thomas de Vries, Bert The graphical brain: Belief propagation and active inference |
title | The graphical brain: Belief propagation and active inference |
title_full | The graphical brain: Belief propagation and active inference |
title_fullStr | The graphical brain: Belief propagation and active inference |
title_full_unstemmed | The graphical brain: Belief propagation and active inference |
title_short | The graphical brain: Belief propagation and active inference |
title_sort | graphical brain: belief propagation and active inference |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798592/ https://www.ncbi.nlm.nih.gov/pubmed/29417960 http://dx.doi.org/10.1162/NETN_a_00018 |
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