A complex network model of semantic memory impairments

In the last decades, several models have been proposed to describe the functions and the structure of human memory. Many of these agree in representing semantic memory, i.e. the part of memory which contains the general knowledge about the world, as a network. On the other hand, the study of complex networks is a new and emerging field at the intersection of physics, mathematics and computer science which aims at characterizing the topological properties of large networks. The paper proposes a quantitative study of the large-scale properties of semantic memory, modelled as the knowledge base of an automatic concept classifier of images. This approach allows us to probe the topological properties of the network, showing that it exhibits the marks of complexity, and provide us with a suitable mathematical framework to study memory impairments. These alterations are firstly modelled as nodes removals and secondly as links modifications, producing markedly different results.