title: Learning pattern recognition through quasi-synchronization of phase oscillators.
abstract: The idea that synchronized oscillations are important in cognitive tasks is receiving significant attention. In this view, single neurons are no longer elementary computational units. Rather, coherent oscillating groups of neurons are seen as nodes of networks performing cognitive tasks. From this assumption, we develop a model of stimulus-pattern learning and recognition. The three most salient features of our model are: 1) a new definition of synchronization; 2) demonstrated robustness in the presence of noise; and 3) pattern learning.
authors.affiliation: Laboratoire d’Informatique de l’X, Laboratoire d’Informatique de l’École Polytechnique, Palaiseau Cedex 91128, France. katya@lix.polytechnique.fr
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Vassilieva
authors.forename: Guillaume
authors.initials: G
authors.lastname: Pinto
authors.forename: José Acacio
authors.initials: JA
authors.lastname: de Barros
authors.forename: Patrick
authors.initials: P
authors.lastname: Suppes
mesh_terms: D001185:Artificial Intelligence; D001683:Biological Clocks / Q000502:physiology*; D003344:Cortical Synchronization / Q000502:physiology*; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000592:standards*