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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Bibliographic Details
Published in:PLoS computational biology 2013, Vol.9 (7), p.e1003134-e1003134
Main Authors: Reichert, David P, Seriès, Peggy, Storkey, Amos J
Format: Article
Language:English
Subjects:
Biology
Brain research
Computational biology
Computer Science
Councils
Hallucinations - physiopathology
Homeostasis
Humans
Machine learning
Medicine
Mental illness
Models, Biological
Nerve Net
Neurosciences
Noise
Pathology
Physiological aspects
Probability
Studies
Syndrome
Training
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Summary: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.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003134