The purpose of this book is to introduce and survey the various quantitative methods which have been proposed for describing, simulating, embodying, or characterizing the processing of electrical signals in nervous systems. We believe that electrical signal processing is a vital determinant of the functional organization of the brain, and that in unraveling the inherent complexities of this processing it will be essential to utilize the methods of quantification and modeling which have led to crowning successes in the physical and engineering sciences. In comprehensive terms, we conceive neural modeling to be the attempt to relate, in nervous systems, function to structure on the basis of operation. Sufficient knowledge and appropriate tools are at hand to maintain a serious and thorough study in the area. However, work in the area has yet to be satisfactorily integrated within contemporary brain research. Moreover, there exists a good deal of inefficiency within the area resulting from an overall lack of direction, critical self-evaluation, and cohesion. Such theoretical and modeling studies as have appeared exist largely as fragmented islands in the literature or as sparsely attended sessions at neuroscience conferences. In writing this book, we were guided by three main immediate objectives. Our first objective is to introduce the area to the upcoming generation of students of both the hard sciences and psychological and biological sciences in the hope that they might eventually help bring about the contributions it promises.

"This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

For students of neuroscience and cognitive science who wish to explore the functioning of the brain further, but lack an extensive background in computer programming or maths, this new book makes neural systems modelling truly accessible. Short, simple MATLAB computer programs give readers all the experience necessary to run their own simulations.

Research in neural modeling and neural networks has escalated dramatically in the last decade, acquiring along the way terms and concepts, such as learning, memory, perception, recognition, which are the basis of neuropsychology. Nevertheless, for many, neural modeling remains controversial in its purported ability to describe brain activity. The difficulties in "modeling" are various, but arise principally in identifying those elements that are fundamental for the expression (and description) of superior neural activity. This is complicated by our incomplete knowledge of neural structures and functions, at the cellular and population levels. The first step towards enhanced appreciation of the value of neural modeling and neural networks is to be aware of what has been achieved in this multidisciplinary field of research. This book sets out to create such awareness. Leading experts develop in twelve chapters the key topics of neural structures and functions, dynamics of single neurons, oscillations in groups of neurons, randomness and chaos in neural activity, (statistical) dynamics of neural networks, learning, memory and pattern recognition.

Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

This book constitutes, together with its compagnion LNCS 1606, the refereed proceedings of the International Work-Conference on Artificial & Neural Networks, IWANN'99, held in Alicante, Spain in June 1999. The 91 revised papers presented were carefully reviewed & selected for inclusion in the book. This volume is devoted to applications of biologically inspired artificial neural networks in various engineering disciplines. The papers are organized in parts on artificial neural nets simulation & implementation, image processing & engineering applications.

Simulation in NSL - Modeling in NSL - Schematic Capture System - User Interface and Graphical Windows - The Modeling Language NSLM - The Scripting Language NSLS - Adaptive Resonance Theory - Depth Perception - Retina - Receptive Fields - The Associative Search Network: Landmark Learning and Hill Climbing - A Model of Primate Visual-Motor Conditional Learning - The Modular Design of the Oculomotor System in Monkeys - Crowley-Arbib Saccade Model - A Cerebellar Model of Sensorimotor Adaptation - Learning to Detour - Face Recognition by Dynamic Link Matching - Appendix I : NSLM Methods - NSLJ Extensions - NSLC Extensions - NSLJ and NSLC Differences - NSLJ and NSLC Installation Instructions.