Abstract
This work models the progression of beta-amyloid pathology according to Small's synaptic scaling theory in an updated version of Ruppin and Reggia's associative neural network model of Alzheimer's disease, 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 informational 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 additionally provide a possible explanation for the apparent low correlation between amyloid plaque density and cognitive decline in the early stages of Alzheimer's disease.