Information-Selectivity of Beta-Amyloid Pathology in an Associative Memory Model

Abstract

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This work updates Ruppin and Reggia's associative neural network model of Alzheimer's disease by simulating beta-amyloid pathology and modelling the progression of beta-amyloid throughout the network according to Small's synaptic scaling theory, leading to a self-reinforcing cascade of damage. Using an information theoretic approach, it is shown that the simulated beta-amyloid pathology initially selectively targets neurons with low contribution to the overall performance of the network, but that it targets neurons with increasingly higher significance to the network as the disease progresses. The results provide a possible explanation for the apparent low correlation between amyloid plaque density and cognitive decline in the early stages of Alzheimer's disease.

Keywords

• Amyloid
• Alzheimer's disease
• computational model
• synaptic scaling
• Associative Memory