title: An oscillatory neural network model of sparse distributed memory and novelty detection.
abstract: A model of sparse distributed memory is developed that is based on phase relations between the incoming signals and an oscillatory mechanism for information processing. This includes phase-frequency encoding of input information, natural frequency adaptation among the network oscillators for storage of input signals, and a resonance amplification mechanism that responds to familiar stimuli. Simulations of this model show different types of dynamics in response to new and familiar stimuli. The application of the model to hippocampal working memory is discussed.
authors.affiliation: Centre for Neural and Adaptive Systems, School of Computing, University of Plymouth, UK. borisyuk@soc.plym.ac.uk
authors.forename: R
authors.initials: R
authors.lastname: Borisyuk
authors.forename: M
authors.initials: M
authors.lastname: Denham
authors.forename: F
authors.initials: F
authors.lastname: Hoppensteadt
authors.forename: Y
authors.initials: Y
authors.lastname: Kazanovich
authors.forename: O
authors.initials: O
authors.lastname: Vinogradova
mesh_terms: D006624:Hippocampus / Q000502:physiology; D008568:Memory; D008959:Models, Neurological; D009415:Nerve Net