Natural Signal Classification by Neural Cliques and Phase-Locked Attractors

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.