2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/80172A neural network with fixed topology can be regarded as a parametrization of functions, which decides on the correlations between functional variations when parameters are adapted. We propose an analysis, based on a differential geometry point of view, that allows to calculate these correlations. In practise, this describes how one response is unlearned while another is trained. Concerning conventional feed-forward neural networks we find that they generically introduce strong correlations, are predisposed to forgetting, and inappropriate for task decomposition. Perspectives to solve these problems are discussed.LaTeX, 7 pages, 3 figuresAdaptation and Self-Organizing SystemsDisordered Systems and Neural NetworksNeural and Evolutionary Computingtext