Object selection by an oscillatory neural network.
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.
Kazanovich
Yakov
Y
Centre for Neural and Adaptive Systems, School of Computing, Plymouth University, Drake Circus, Plymouth PL4 8AA, UK. yakovk@soc.plym.ac.uk
Borisyuk
Roman
R
D001683:Biological Clocks / Q000502:physiology*; D016571:Neural Networks, Computer; D010775:Photic Stimulation / Q000379:methods