NEURAL-NETWORK MODELING OF MEMORY DETERIORATION IN ALZHEIMERS-DISEASE

The clinical course of Alzheimer's disease (AD) is generally character ized by progressive gradual deterioration, although large clinical var iability exists. Motivated by the recent quantitative reports of synap tic changes in AD, we use a neural network model to investigate how th e interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. Within the model we show that the deterioration of memory retrieval due to synap tic deletion can be much delayed by multiplying all the remaining syna ptic weights by a common factor, which keeps the average input to each neuron at the same level. This parallels the experimental observation that the total synaptic area per unit volume (TSA) is initially prese rved when synaptic deletion occurs. By using different dependencies of the compensatory factor on the amount of synaptic deletion one can de fine various compensation strategies, which can account for the observ ed variation in the severity and progression rate of AD.