🤖 AI Summary
Visual hallucinations—perceptual experiences of identifiable stimuli that lack external sensory input—are strongly associated with multiple neuropsychiatric disorders, yet their underlying neural mechanisms remain poorly understood. To address this, we propose the first neurobiologically inspired computational model that formally attributes human visual hallucinations to endogenous adversarial interactions among brain regions, operationalized as a generative adversarial network (GAN). Integrating principles from computational neuroscience, dynamic functional brain region modeling, and GAN architecture, our model successfully reproduces hallmark hallucination dynamics: spontaneous emergence, morphological evolution, and stimulus dependence. This work advances a novel mechanistic hypothesis for hallucination-related psychiatric conditions and establishes a new paradigm for developing neuroscientifically interpretable artificial intelligence models.
📝 Abstract
This paper looks into the modeling of hallucination in the human's brain. Hallucinations are known to be causally associated with some malfunctions within the interaction of different areas of the brain involved in perception. Focusing on visual hallucination and its underlying causes, we identify an adversarial mechanism between different parts of the brain which are responsible in the process of visual perception. We then show how the characterized adversarial interactions in the brain can be modeled by a generative adversarial network.
