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Updated: Jul 4, 2026

A Lateralized Odor Learning Model in Neonatal Rats for Dissecting Neural Circuitry Underpinning Memory Formation
Published on: August 18, 2014
An oscillatory neural network model of sparse distributed memory and novelty detection
R Borisyuk1, M Denham, F Hoppensteadt
1Centre for Neural and Adaptive Systems, School of Computing, University of Plymouth, UK. borisyuk@soc.plym.ac.uk
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.
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