Charles Bonnet syndrome: evidence for a generative model in the cortex?

Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual...

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Autores principales:	David P Reichert, Peggy Seriès, Amos J Storkey
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/5c0e065beaab41be8931464a1e7e0e1d
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spelling	oai:doaj.org-article:5c0e065beaab41be8931464a1e7e0e1d2021-11-18T05:52:00ZCharles Bonnet syndrome: evidence for a generative model in the cortex?1553-734X1553-735810.1371/journal.pcbi.1003134https://doaj.org/article/5c0e065beaab41be8931464a1e7e0e1d2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874177/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain.David P ReichertPeggy SerièsAmos J StorkeyPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 7, p e1003134 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 David P Reichert Peggy Seriès Amos J Storkey Charles Bonnet syndrome: evidence for a generative model in the cortex?
description	Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain.
format	article
author	David P Reichert Peggy Seriès Amos J Storkey
author_facet	David P Reichert Peggy Seriès Amos J Storkey
author_sort	David P Reichert
title	Charles Bonnet syndrome: evidence for a generative model in the cortex?
title_short	Charles Bonnet syndrome: evidence for a generative model in the cortex?
title_full	Charles Bonnet syndrome: evidence for a generative model in the cortex?
title_fullStr	Charles Bonnet syndrome: evidence for a generative model in the cortex?
title_full_unstemmed	Charles Bonnet syndrome: evidence for a generative model in the cortex?
title_sort	charles bonnet syndrome: evidence for a generative model in the cortex?
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/5c0e065beaab41be8931464a1e7e0e1d
work_keys_str_mv	AT davidpreichert charlesbonnetsyndromeevidenceforagenerativemodelinthecortex AT peggyseries charlesbonnetsyndromeevidenceforagenerativemodelinthecortex AT amosjstorkey charlesbonnetsyndromeevidenceforagenerativemodelinthecortex
_version_	1718424727488299008

Charles Bonnet syndrome: evidence for a generative model in the cortex?

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