An oscillatory neural network model of sparse distributed memory and novelty detection

A model of sparse distributed memory is developed that is based on phase re lations between the incoming signals and an oscillatory mechanism for infor mation processing. This includes phase-frequency encoding of input informat ion, 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 dynami cs in response to new and familiar stimuli. The application of the model to hippocampal working memory is discussed. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.