SELF-ASSOCIATION AND HEBBIAN LEARNING IN LINEAR NEURAL NETWORKS

We study Hebbian learning in linear neural networks with emphasis on the self-association information principle. This criterion, in one-layer networks, leads to the space of the principal components and can be generalized to arbitrary architectures. The self-association paradigm appears to be very promising because it accounts for the fundamental features of Hebbian synaptic learning and generalizes the various techniques proposed for adaptive principal component networks, We also include a set of simulations that compare various neural architectures and algorithms.