ANTI-HEBBIAN LEARNING IN TOPOLOGICALLY CONSTRAINED LINEAR-NETWORKS - A TUTORIAL

Using standard results from the adaptive signal processing literature, we review the learning behavior of various constrained linear neural networks made up of anti-Hebbian synapses, where learning is driven by the criterion of minimizing the node information energy. We point out how simple learning rules of Hebbian type can provide fast self-organization, under rather wide connectivity constraints. We verify the results of the theory in a set of simulations.