Neural simulation of acquired and progressive language disorders

Abstract

This paper describes a novel approach to the simulation of language disorders, based upon the notion of a multi-network architecture —a set of autonomous neural networks which have been linked in some manner to perform a complex function that cannot readily be performed by any one network alone. The merits of this approach have been assessed by mapping a neuropsychological model of single-word language processing onto a multi-network architecture. Language disorders may be simulated by damaging, or ‘lesioning’, one or more component networks. Our attempts to simulate two specific language disorders, semantic dementia and deep dysphasia, are described. The relative success of our simulation work is encouraging, and leads us to conclude that a multi-network approach to the simulation of cognitive function and dysfunction offers a valid alternative to the ‘traditional’ single-network based perspective.