title: Natural signal classification by neural cliques and phase-locked attractors.
abstract: Cortical neural networks are responsible for identification, recognition and classification of natural signals mediated by various sensory channels. These tasks are still too complex to be accomplished by state-of-the-art engineering systems. There is, therefore, a great deal of interest in the development of suitable biologically-motivated architectures which are based on a realistic model of generic neural ensembles. We present a computational architecture for classification of natural signals, such as physiological signals,based on the emergence of instant neural cliques and phase-locked attractors in liquid architectures. The emergence of instant neural cliques enables mapping of complex classes of signals onto specific spatio-temporal firing patterns. The convergence of neural cliques onto attractors, along phase-locked pathways, reveals a new type dynamic behavior of neural ensembles, which lends itself to simple discrete-output computational systems.
authors.affiliation: Technion.
authors.forename: I
authors.initials: I
authors.lastname: Raichelgauz
authors.forename: K
authors.initials: K
authors.lastname: Odinaev
authors.forename: Y Y
authors.initials: YY
authors.lastname: Zeevi
mesh_terms: D008959:Models, Neurological; D009415:Nerve Net; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics