  Cognitive ageing seems to be a story of global degradation. As one ages there
are a number of physical, chemical and biological changes that take place.
Therefore it is logical to assume that the brain is no exception to this
phenomenon. The principle purpose of this project is to use models of neural
dynamics and learning based on the underlying principle of self-organised
criticality, to account for the age related cognitive effects. In this regard
learning in neural networks can serve as a model for the acquisition of skills
and knowledge in early development stages i.e. the ageing process and
criticality in the network serves as the optimum state of cognitive abilities.
Possible candidate mechanisms for ageing in a neural network are loss of
connectivity and neurons, increase in the level of noise, reduction in white
matter or more interestingly longer learning history and the competition among
several optimization objectives. In this paper we are primarily interested in
the affect of the longer learning history on memory and thus the optimality in
the brain. Hence it is hypothesized that prolonged learning in the form of
associative memory patterns can destroy the state of criticality in the
network. We base our model on Tsodyks and Markrams [49] model of dynamic
synapses, in the process to explore the effect of combining standard Hebbian
learning with the phenomenon of Self-organised criticality. The project mainly
consists of evaluations and simulations of networks of integrate and
fire-neurons that have been subjected to various combinations of neural-level
ageing effects, with the aim of establishing the primary hypothesis and
understanding the decline of cognitive abilities due to ageing, using one of
its important characteristics, a longer learning history.
