There are five different types of eye movements: saccades, smooth pursuit, vestibular ocular eye movements, optokinetic eye movements, and vergence eye movements. The purpose of this book series is focused primarily on mathematical models of the horizontal saccadic eye movement system and the smooth pursuit system, rather than on how visual information is processed.
In Part 1, early models of saccades and smooth pursuit are presented. A number of oculomotor plant models are described here beginning with the Westheimer model published in 1954, and up through our 1995 model involving a 4th order oculomotor plant model. In Part 2, a 2009 version of a state-of-the-art model is presented for horizontal saccades that is 3rd-order and linear, and controlled by a physiologically based time-optimal neural network. Part 3 describes a model of the saccade system, focusing on the neural network. It presents a neural network model of biophysical neurons in the midbrain for controlling oculomotor muscles during horizontal human saccades.
In this book, a multiscale model of the saccade system is presented, focusing on a multiscale neural network and muscle fiber model. Chapter 1 presents a comprehensive model for the control of horizontal saccades using a muscle fiber model for the lateral and medial rectus muscles. The importance of this model is that each muscle fiber has a separate neural input. This model is robust and accounts for the neural activity for both large and small saccades. The muscle fiber model consists of serial sequences of muscle fibers in parallel with other serial sequences of muscle fibers. Each muscle fiber is described by a parallel combination of a linear length tension element, viscous element, and active-state tension generator. Chapter 2 presents a biophysically realistic neural network model in the midbrain to drive a muscle fiber oculomotor plant during horizontal monkey saccades. Neural circuitry, including omnipause neuron, premotor excitatory and inhibitory burst neurons, long lead burst neuron, tonic neuron, interneuron, abducens nucleus, and oculomotor nucleus, is developed to examine saccade dynamics. The time-optimal control mechanism demonstrates how the neural commands are encoded in the downstream saccadic pathway by realization of agonist and antagonist controller models. Consequently, each agonist muscle fiber is stimulated by an agonist neuron, while an antagonist muscle fiber is unstimulated by a pause and step from the antagonist neuron. It is concluded that the neural network is constrained by a minimum duration of the agonist pulse, and that the most dominant factor in determining the saccade magnitude is the number of active neurons for the small saccades. For the large saccades, however, the duration of agonist burst firing significantly affects the control of saccades. The proposed saccadic circuitry establishes a complete model of saccade generation since it not only includes the neural circuits at both the premotor and motor stages of the saccade generator, but it also uses a time-optimal controller to yield the desired saccade magnitude.
Author Biography:
Alireza Ghahari received his B.Sc. degree in electrical engineering from the Sharif University of Technology, Iran, in August 2007. Thereafter, he completed his M.Sc. in electrical and computer engineering at the University of Tehran, Iran, in March 2010. During those years of study, he gained valuable insights into systems engineering by taking courses in a variety of contexts, such as statistical signal processing, information theory and coding, computer vision, and pattern recognition. He started his Ph.D. in the ECE department at University of Connecticut in Fall 2010. His dissertation major advisor was Prof. John Enderle. He has come to realize the profound contributions of Prof. Enderle in the field of theoretical and computational neuroscience, and truly considers John to be a major influence, both academically and personally. His research areas of interest include spiking neural networks analysis and implementation, brain computer interface, and development of computational techniques. John D. Enderle is a Professor of Biomedical Engineering and Electrical & Computer Engineering at the University of Connecticut, where he was Biomedical Engineering Program Director from 1997-2010. He received his B.S., M.E., and Ph.D. degrees in biomedical engineering, and M.E. degree in electrical engineering from Rensselaer Polytechnic Institute, Troy, New York, in 1975, 1977, 1980, and 1978, respectively.
Dr. Enderle is a Fellow of the IEEE, the past Editor-in-Chief of the EMB Magazine (2002-2008), the 2004 EMBS Service Award Recipient, Past-President of the IEEE-EMBS, and was EMBS Conference Chair for the 22nd Annual International Conference of the IEEE EMBS and World Congress on Medical Physics and Biomedical Engineering in 2000. He is also a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), Fellow of the American Society for Engineering Education, Fellow of the Biomedical Engineering Society, and a Rensselaer Alumni Association Fellow. Enderle is a former member of the ABET Engineering Accreditation Commission (2004-2009). In 2007, Enderle received the ASEE National Fred Merryfield Design Award. He is also a Teaching Fellow at the University of Connecticut since 1998. Enderle is the Biomedical Engineering Book Series Editor for Morgan & Claypool Publishers.
Enderle is also involved with research to aid persons with disabilities. He is the Editor of the NSF book series on NSF Engineering Senior Design Projects to Aid Persons with Disabilities, published annually since 1989. Enderle is also an author of the book Introduction to Biomedical Engineering, published by Elsevier in 2000 (first edition), 2005 (second edition), and 2011 (third edition). Over his career, Enderle has been an author of over 200 publications and 49 books or book chapters. Enderle's current research interest involves characterizing the neurosensory control of the human visual and auditory system.
