Charles Bonnet Syndrome: Evidence for a Generative Model in the Cortex?

David P Reichert,Amos J Storkey,Peggy Seriès,Boris S Gutkin

doi:10.1371/journal.pcbi.1003134

David P Reichert, Amos J Storkey + Show 2 more

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https://doi.org/10.1371/journal.pcbi.1003134

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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 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.

Highlights

Visual hallucinations can offer fascinating insights into the mechanisms underlying perceptual processing and the generation of visual experience in the brain
deep Boltzmann machine (DBM) as models of cortical inference We argue that the DBM is promising as a model of hallucinations, and other aspects of a hypothetical generative model in the cortex, because it implements a generative model that learns to synthesise representations of sensory data
The hypothesis we explored is that homeostatic regulation of neuronal firing rate in response to sensory deprivation underlies the emergence of hallucinations in Charles Bonnet syndrome (CBS)

Summary

Introduction

Visual hallucinations can offer fascinating insights into the mechanisms underlying perceptual processing and the generation of visual experience in the brain. Hallucinations in CBS can be very complex in the sense that they entail vivid, life-like, and elaborate imagery of objects, people, animals, or whole visual scenes. The primary cause of CBS is loss of vision due to eye diseases, with no clear pathology in the brain itself and no necessary impairment to mental health other than the hallucinations. De-afferentation of the visual system and sensory deprivation seem to be the important factors in the development of CBS, and comparisons have been made to phantom limb phenomena. Unlike for example in the case of schizophrenia, most often accompanied by auditory hallucinations [5], in CBS there does not seem to be a more pervasive malfunction of the cognitive system, but rather some form of over-compensation or maladaptation of the relatively healthy brain to the lack of sensory stimulation

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