title: Methodological and theoretical issues in neural network models of frontal cognitive functions.
abstract: Neural network models have made significant strides in recent years toward modeling of neuropsychological data. In particular, three different research groups, including that of the present authors, have simulated in model networks some of the behavioral effects of frontal lobe damage. In this article we review these models and discuss their significance in terms of hierarchical organization in the nervous system. These models, to varying degrees, incorporate several widely used neural network principles that have also been used to model a wide range of data in other areas such as categorization, conditioning, and motor control. These principles include associative learning, competition, opponent processing, neuromodulation, and interlevel resonant feedback. Specifically, we show how combinations of these principles serve to model the attentional requirements of cognitive tasks and motor plans.
authors.affiliation: Department of Mathematics, University of Texas at Arlington 76019-0408.
authors.forename: D S
authors.initials: DS
authors.lastname: Levine
authors.forename: R W
authors.initials: RW
authors.lastname: Parks
authors.forename: P S
authors.initials: PS
authors.lastname: Prueitt
mesh_terms: D000818:Animals; D001288:Attention / Q000502:physiology; D003071:Cognition / Q000502:physiology*; D005625:Frontal Lobe / Q000502:physiology*; D006801:Humans; D008253:Macaca mulatta; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009483:Neuropsychological Tests; D014705:Verbal Behavior