DOI:
10.1016/j.neunet.2016.12.003
Author(s):
Yakov Kazanovich; Roman Borisyuk
Tags:
Neuroscience; Computer Science
article description
We present an oscillatory neural network model that can account for reaction times in visual search experiments. The model consists of a central oscillator that represents the central executive of the attention system and a number of peripheral oscillators that represent objects in the display. The oscillators are described as generalized Kuramoto type oscillators with adapted parameters. An object is considered as being included in the focus of attention if the oscillator associated with this object is in-phase with the central oscillator. The probability for an object to be included in the focus of attention is determined by its saliency that is described in formal terms as the strength of the connection from the peripheral oscillator to the central oscillator. By computer simulations it is shown that the model can reproduce reaction times in visual search tasks of various complexities. The dependence of the reaction time on the number of items in the display is represented by linear functions of different steepness which is in agreement with biological evidence.