title: Learning of spatio-temporal codes in a coupled oscillator system.
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.
authors.affiliation: Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA 93106, USA. gabor@engineering.ucsb.edu
authors.forename: Gábor
authors.initials: G
authors.lastname: Orosz
authors.forename: Peter
authors.initials: P
authors.lastname: Ashwin
authors.forename: Stuart
authors.initials: S
authors.lastname: Townley
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001683:Biological Clocks / Q000502:physiology*; D003198:Computer Simulation; D003344:Cortical Synchronization; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D009833:Olfactory Pathways / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D015398:Signal Transduction / Q000502:physiology; D012984:Software; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors