title: Sparse neural networks with large learning diversity.
abstract: Coded recurrent neural networks with three levels of sparsity are introduced. The first level is related to the size of messages that are much smaller than the number of available neurons. The second one is provided by a particular coding rule, acting as a local constraint in the neural activity. The third one is a characteristic of the low final connection density of the network after the learning phase. Though the proposed network is very simple since it is based on binary neurons and binary connections, it is able to learn a large number of messages and recall them, even in presence of strong erasures. The performance of the network is assessed as a classifier and as an associative memory.
authors.affiliation: Electronics Department, Télécom Bretagne (Institut Télécom), Brest, France. vincent.gripon@telecombretagne.eu
authors.forename: Vincent
authors.initials: V
authors.lastname: Gripon
authors.forename: Claude
authors.initials: C
authors.lastname: Berrou
mesh_terms: D003198:Computer Simulation; D006801:Humans; D007858:Learning; D011939:Mental Recall; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*