Luttrell S P, 1999, Self-organised modular neural networks for encoding data, in Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems (Perspectives in Neural Computing, Springer-Verlag, ed. Sharkey A J C), pp. 235-263
It is shown how a neural network can be optimised so that multiple interlinked network modules emerge by self-organisation. The processing task chosen to illustrate this is encoding high-dimensional data, such as images,where multiple network modules implement a factorial encoder, in which the high-dimensional data space is broken up into a number of low-dimensional subspaces, each of which is separately encoded. This type of factorial encoder emerges through a process of self-organisation, provided that the input data lies on a curved manifold, as is indeed the case in image processing applications.