Learning of spatio-temporal codes in a coupled oscillator system

Authors:
Gábor Orosz;Peter Ashwin;Stuart Townley
Affiliations:
Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA and School of Engineering, Computing and Mathematics, University of Exeter, Exeter, UK;School of Engineering, Computing and Mathematics, University of Exeter, Exeter, UK;School of Engineering, Computing and Mathematics, University of Exeter, Exeter, UK
Venue:
IEEE Transactions on Neural Networks
Year:
2009

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Abstract

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.