title: Anti-Hebbian learning in topologically constrained linear networks: a tutorial.
abstract: 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.
authors.affiliation: Dept. of Electr. and Syst. Eng., Connecticut Univ., Storrs, CT.
authors.forename: F
authors.initials: F
authors.lastname: Palmieri
authors.forename: J
authors.initials: J
authors.lastname: Zhu
authors.forename: C
authors.initials: C
authors.lastname: Chang