Oscillatory neural models of novelty detection and memory

Roman Borisyuk
Department of Mathematics, University of Plymouth

(May 22, 2003 3:30 PM - 4:30 PM)

Oscillatory neural models of novelty detection and memory

Abstract

 

Oscillatory neural model (ONM) with traditional Hebbian-like learning rule for associative memorization of sequences is considered

ONM of novelty detection which is based on frequency encoding of input information and oscillatory mechanism of memory formation will be presented. The adaptation of natural frequencies of network oscillators to the frequency of the input signal is used as the mechanism of information memorization. The recognition principle for familiar stimuli is based on the resonance amplification of network activity

A new ONM that combines consecutive selection of objects, attention focus formation, memorisation, and discrimination between new and familiar objects has been developed. The model works with visual information and fulfils the following operations: (1) separation of different objects according to their spatial connectivity; (2) consecutive selection of objects located in the visual field into the attention focus; (3) representation of objects in the working memory; and (4) novelty detection of objects.