A mathematical model of persistent neural activity in human prefrontal cortex for visual feature binding

Abstract

A two-stage model of sustained neural activity in the prefrontal cortex is proposed in order to simulate feature binding and capacity limits in visual working memory. In the first stage, object features are stored in parallel network layers without explicit conjunctions. A second stage binds features into integrated objects consistent with the recent proposal of Wheeler and Treisman (J. Exp. Psychol. Gen. 131 (2002) 48). Model neurons have extended dendrites which are capable of active non-linear integration. Computer simulation illustrates model ability to segregate feature values of the different objects into cells with different activity amplitude and to maintain segregated feature representations for a limited number of objects. Depending on the task demands, features are retrieved in a second stage and form a unified object representation.