%0 Journal Article
%T Neural Oscillators Programming Simplified
%A Patrick McDowell
%A Theresa Beaubouef
%J Applied Computational Intelligence and Soft Computing
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/963917
%X The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmerĄ¯s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications. 1. Introduction Scientists have long employed neural oscillators as a method to study neuron/ganglia-based processes that serve as central pattern generators for various organisms and as a method to generate control and coordination signals for various robotic mechanisms. For example, significant work has been done in trying to understand the functions of various neurons and components of such biological neural networks [1, 2], developing properties general to all networks of neural oscillators [3], and the modeling of processes such as locomotion in simple animals [4¨C8]. Associative neural network models with behavior similar to central path generators have also been developed [9]. Computer scientists have researched and developed several varieties of artificial neural networks that model natural neural networks to some extent. Artificial neural network is a well-developed field, and the literature in this general area is quite vast; however, literature related to neural oscillators and central pattern generators using these computerized neural network models, such as in [10], is not so prevalent. Even so, the use of such models in controlling the motion of robots has been well established [11¨C13]. For researchers wanting to apply the techniques of neural oscillators and/or central path generators for the programming robotic controls, modeling rhythmic patterns, and so forth, without the necessity of understanding complex theoretical mathematical models or learning how simple invertebrates swim, there appears to be no single source of basic information available. Many articles provide basic diagrams of oscillators and occasionally some tuning parameters, but most do not provide discussions concerning the general nature of the mechanism, conceptual overviews, or implementation information. To help
%U http://www.hindawi.com/journals/acisc/2012/963917/