Synaptic Deletion and Compensation in Alzheimer’s Disease: a Neural Model

We use a neural network model to investigate how the interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. We show that, in parallel with the experimental data, memory deterioration can be much delayed by strengthening the remaining synaptic weights. Using different dependencies of the compensatory strengthening on the amount of synaptic deletion various compensation strategies can be defined, corresponding to the observed variation in the progression of AD.