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PubMed · 12459289

Object selection by an oscillatory neural network.

Yakov Kazanovich·Roman Borisyuk
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Abstract

We describe a new solution to the problem of consecutive selection of objects in a visual scene by an oscillatory neural network with the global interaction realised through a central executive element (central oscillator). The frequency coding is used to represent greyscale images in the network. The functioning of the network is based on three main principles: (1) the synchronisation of oscillators via phase-locking, (2) adaptation of the natural frequency of the central oscillator, and (3) resonant increase of the amplitudes of the oscillators which work in-phase with the central oscillator. Examples of network simulations are presented to show the reliability of the results of consecutive selection of objects under conditions of constant and varying brightness of the objects.

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Biological ClocksNeural Networks, ComputerPhotic StimulationJournal ArticleResearch Support, Non-U.S. Gov't

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Yakov Kazanovich, Roman Borisyuk. Object selection by an oscillatory neural network.. https://doi.org/10.1016/s0303-2647(02)00068-0

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