  This work updates an existing, simplistic computational model of Alzheimer's
Disease (AD) to investigate the behaviour of synaptic compensatory mechanisms
in neural networks with small-world connectivity, and varying methods of
calculating compensation. It additionally introduces a method for simulating
tau neurofibrillary pathology, resulting in a more dramatic damage profile.
Small-world connectivity is shown to have contrasting effects on capacity,
retrieval time, and robustness to damage, whilst the use of more
easily-obtained remote memories rather than recent memories for synaptic
compensation is found to lead to rapid network damage.
