title: Optimal Hebbian learning rules and the role of asymmetry.
abstract: We study the storage properties associated with generalized Hebbian learning rules which present four free parameters that allow for asymmetry. We also introduce two extra parameters in the post-synaptic potentials in order to further improve the critical capacity. Using signal-to-noise analysis, as well as computer simulations on an analog network, we discuss the performance of the rules for arbitrarily biased patterns and find that the critical storage capacity alpha c becomes maximal for a particular symmetric rule (alpha c diverges in the sparse coding limit). Departures from symmetry decrease alpha c but can increase the robustness of the model.
authors: Centro Brasileiro de Pesquisas Físicas, Rio de Janeiro--RJ, Brazil.
authors: D A
authors: DA
authors: Stariolo
authors: C
authors: C
authors: Tsallis
mesh_terms: D001185:Artificial Intelligence; D008564:Membrane Potentials / Q000502:physiology; D008568:Memory / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D013569:Synapses / Q000502:physiology