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

PLoS Computational Biology
David P ReichertAmos J Storkey

Abstract

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 h...Continue Reading

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Related Concepts

Establishment and Maintenance of Localization
Visual System
Fluctuation
Biological Neural Networks
Photopsia
Hallucinations, Visual
Cortex Bone Disorders
Adrenal Cortex Diseases
Prefrontal Cortex
Imbalance

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