City Research Online

Abstract

The role of symmetry in simplifying the dynamics of finite systems is implicit in work on neural networks. It is expected that such systems will be more amenable to analysis whenever regularities are discovered experimentally or postulated within a theoretical model.

This thesis characterises the symmetry of a network by its group of isomorphisms and examines some of the dynamical consequences of a network possessing non-trivial symmetry group. The framework of the discussion, while motivated by the special instance of neural systems, is the idea of a lattice valused relation. Neural networks and automata are developed as exemplary cases of this general concept and a number of specific models discussed.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science Q Science > QA Mathematics
Departments:	School of Science & Technology > Mathematics School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

Export

Downloads