SELECTIVE ATTENTION MODEL OF MOVING OBJECTS
Tracking moving objects is a vital visual task for the survival of an animal. We describe oscillatory neural network models of visual attention with a central element that can track a moving target among a set, of distracters oil the screen. At the initial stage, the model forms the focus of attention oil all arbitrary object that is considered as a target. Other objects are treated as distracters. we present. here two models: 1) synchronisation based model designed as a network of phase oscillators and 2) spiking neural model which is based oil the idea of resource-limited parallel visual pointers. Selective attention and the tracking process are represented by the partial synchronisation between the central unit and a subgroup of peripheral elements. Simulation results are in overall agreement with the findings from psychological experiments: overlapping between the target and distractors is the main source of errors.
phase oscillator visual pointer partial synchronization object tracking spiking neuron model