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 on the screen. At the initial stage, the model forms focus of attention on an arbitrary object that is considered as a target. Other objects are treated as distracters. We present here two models: 1) synchronisation based AMCO model of phase oscillators and 2) spiking neural model which is based on the idea of resource-limited parallel visual pointers. Selective attention and the tracking process are represented by partial synchronization between the central unit and subgroup of peripheral elements. The simulation results are in overall agreement with the findings from psychological experiments: overlapping between target Mid distractor is the main source of error. Future investigations include the dependence between tracking performance and neuron frequency.