Abstract
There have been many studies about dynamics of neural fields. Especially, the neural field which allowed localized excitation areas provided base for the self-organizing map (SOM) algorithm. Here, we focus on a neural oscillatory field, and proposed a three-layered model of a neural oscillatory field which allows stable localized oscillatory excitation areas. Our comuputer simulation results show that the neural oscillatory field with two Mexican-hat-type connections keeps two or more than two localized oscillatory excitation areas stably around maximal points of an external input. In this case, the neural oscillatory field realizes in-phase phase-locking within each localized oscillatory excitation area, but maximizes the phase difference between different localized oscillatory excitation areas. This neural oscillatory field provides base for an oscillatory SOM algorithm, and will be useful to solve the binding problem with separated extraction of information.
