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AN IN SILICO DESCRIPTION OF AN ALZHEIMER NETWORK

Alzheimer’s disease (AD) is the most well known form of dementia. Already 
in 1907 Alois Alzheimer published a first paper about the symptoms of this 
disease, of which we know that it afflicts one in three people above 85. By 
now it is broadly accepted that indeed the loss of short-term memory is 
among the first symptoms of AD, followed by behavioural changes and difficulties 
with motor activities. In the end, Alzheimer is a terminal disease, 
and with its huge direct impact on caregivers and financial impact on society 
it is an essential topic in several research fields. Additionally, by the 
increasing average age of current society it only becomes more urgent. 
Among these fields of research, computational neuroscience can be helpful 
as a first step in deciphering phenomena that occur with the appearance 
of Alzheimer’s disease. For many years after the first descriptions about 
AD there was no clue about a possible cause. Nowadays it is known that 
the appearance of the disease is mediated by a protein called Amyloid beta 
(A�). The exact role of A� remains unknown, though a prominent hypothesis 
is that it affects several presynaptic processes, thereby suppressing 
neuron efficiency. 
We model networks containing up to 600 neurons as computational networks 
in which the vertices represent a neuron. The internal dynamics of 
the neuron are governed by the so-called Izhikevich model. The connections 
represent the synapses between neurons. In this way, the behaviour of neural 
networks can be simulated by using computational mathematics. 
In this thesis a computational framework designed to map experimental 
data to synaptic parameters is constructed. In this way, having in vitro 
measurements of A�-treated neuronal networks and untreated control networks, 
the existence of significant differences in the synaptic properties of 
the two types of networks can be systematically investigated. 
Taking our cues from previous research by Philip Hahn & Cameron 
McIntyre [2010], we attempt to use a Gauss-Newton least mean square 
error optimisation. The computational framework then extracts the first 
two statistical moments, Mean Firing Rate (MFR) and Coefficient of Variation 
(CV), from measurements. This Gauss-Newton method subsequently 
searches for synaptic properties yielding networks with comparable MFR 
and CV. 
We conclude that this Gauss-Newton method should be handled with 
great care. In particular, we find that, for our particular model the algorithm 
either diverges or converges at a poor rate. Besides this statement and 
giving an overview on how varying the synaptic parameters affects the behaviour 
of neural networks, this thesis gives elaborate suggestions for future 
computational research on Alzheimer’s disease.