Progress In Neural Processing – serie

Since 1985, the methods of statistical mechanics have been successfully applied to the theory of neural networks. The current status of the statistical physics of neural networks was reviewed at the workshop upon which this book is based, and propsects for the future examined. Recent interdisciplinary development with related fields such as computational learning theory, statistics, information theory, and nonlinear dynamics are also presented. Key Contributors include S. Amari, H. Sompolinsky, Y. LeCun, I. Kanter, D. Haussler, H. S. Seung, M. Opper, M. Kearns and M. Biehl.

One of the most exciting applications of neural networks in recent times has been to improve investment performance in the financial markets. Neural networks can be harnessed for this task as they bear many mathematical similarities to the nonlinear econometric models used to study the microstructure of the financial markets.Since 1993, this annual conference has been bringing together investment managers, neural network researchers, statisticians, econometricians, and computer scientists for an insight into the state of the art in this area. The proceedings of the 1995 meeting will give investment managers, institutional investors and analysts a comprehensive look at the most profitable applications of this fascinating new technology in today's financial markets.

This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.

Hardware inaccuracy and imprecision are important considerations when implementing neural algorithms. This book presents a study of synaptic weight noise as a typical fault model for analogue VLSI realisations of MLP neural networks and examines the implications for learning and network performance. The aim of the book is to present a study of how including an imprecision model into a learning scheme as a“fault tolerance hint” can aid understanding of accuracy and precision requirements for a particular implementation. In addition the study shows how such a scheme can give rise to significant performance enhancement.

This book contains the proceedings of the Workshop on Environmental and Energy Applications of Neural Networks. The purpose of this workshop was to provide a forum for discussing environmental, energy, and biomedical applications of neural networks. The applications covered in these proceedings include modeling and predicting soil, air and water pollution; waste reduction; environmental sensing; spectroscopy; hazardous waste handling and cleanup; environmental monitoring of power plants; process monitoring and optimization of power systems; modeling and control of power plants; power load forecasting; fault location and diagnosis of power systems; medical image and signal analysis; medical diagnosis; analysis of environmental health effects; health insurance, and modeling biological systems.

One of the most exciting applications of neural networks in recent times has been to improve investment performance in the financial markets. Neural networks can be harnessed for this task as they bear many mathematical similarities to the nonlinear econometric models used to study the microstructure of the financial markets.Since 1993, this annual conference has been bringing together investment managers, neural network researchers, statisticians, econometricians, and computer scientists for an insight into the state of the art in this area. The proceedings of the 1995 meeting will give investment managers, institutional investors and analysts a comprehensive look at the most profitable applications of this fascinating new technology in today's financial markets.

During the last few years there has been a rapidly increasing interest in neural modeling of brain and cognitive disorders. This multidisciplinary book presents a variety of such models in neurology, neuropsychology and psychiatry. A review of work in this area is given first. Computational models are then presented of memory impairment in Alzheimer's disease, functional brain reorganization following a stroke, patterns of neural activity in epilepsy, disruption of language processes in aphasia and acquired dyslexia, altered cognitive processes in schizophrenia and depression, and related disorders. This is the first book on this topic, with contributions from many of the leading researchers in this field.

This volume selects the best contributions from the Fourth International Conference on Neural Networks in the Capital Markets (NNCM). The conference brought together academics from several disciplines with strategists and decision makers from the financial industries.The various chapters present and compare new techniques from many areas including data mining, information systems, machine learning, and statistical artificial intelligence. The volume focuses on evaluating their usefulness for problems in computational finance and financial engineering.Applications — risk management; asset allocation; dynamic trading and hedging; forecasting; trading cost control. Markets — equity; foreign exchange; bond; commodity; derivatives; Approaches — data mining; statistical AI; machine learning; Monte Carlo simulation; bootstrapping; genetic algorithms; nonparametric methods; fuzzy logic.The chapters emphasizes in-depth and comparative evaluation with established approaches.

This volume selects the best contributions from the Fourth International Conference on Neural Networks in the Capital Markets (NNCM). The conference brought together academics from several disciplines with strategists and decision makers from the financial industries.The various chapters present and compare new techniques from many areas including data mining, information systems, machine learning, and statistical artificial intelligence. The volume focuses on evaluating their usefulness for problems in computational finance and financial engineering.Applications — risk management; asset allocation; dynamic trading and hedging; forecasting; trading cost control. Markets — equity; foreign exchange; bond; commodity; derivatives; Approaches — data mining; statistical AI; machine learning; Monte Carlo simulation; bootstrapping; genetic algorithms; nonparametric methods; fuzzy logic.The chapters emphasizes in-depth and comparative evaluation with established approaches.

RAM-based networks are a class of methods for building pattern recognition systems. Unlike other neural network methods, they train very rapidly and can be implemented in simple hardware. This important book presents an overview of the subject and the latest work by a number of researchers in the field of RAM-based networks.

After the explosion of research on neural networks in the eighties, the nineties have seen a boom in industrial applications of neural networks. In contrast to the large output of publications in international neural network journals, which reflects the increase in neural network research, the large number of successful applications are less accessible. To make a survey of successful applications in industry in Europe, a project called SIENA was initiated with support from the European Community. The aim of SIENA was to assess the business impact of neural networks. Data were collected on both the supplier side and the end-user side of the market. In addition, case studies of successful "money-making" applications using neural networks were gathered. This book contains detailed descriptions of some of the applications.

Neuromorphic systems are implementations in silicon of sensory and neural systems whose architecture and design are based on neurobiology. This growing area offers exciting possibilities, such as sensory systems that can compete with human senses and pattern recognition systems that can run in real time. It is at the intersection of neurophysiology, computer science and electrical engineering.This book brings together recent developments in Europe and the US, so that researchers in both academia and industry can find out about the state of the art. As well as elementary material on what neuromorphic systems are and why they are growing in importance, the book contains details of current work. There are articles on aspects of implementing sensory neuromorphic systems, and also on neuromorphic hardware.

This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of the existing theory of RBF networks and applications is given at the beginning.

The main aim of this book is to raise and clear up the intriguing problems of noise and chaos in the nervous system. What functional role do fluctuations in neural systems play? Are there chaotic processes in the brain? What is the neural code, and how robust is it towards noise? Are there mechanisms that can control noise and chaos?The book provides an introduction to this new and hot field of research, and at the same time brings the reader to the forefront of scientific inquiry. It is intended primarily for biologists involved in theoretical treatment and for physicists with an interest in biology, but the overview character of the articles makes it also well suited for a broader readership.

Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests — from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise.This book reviews the state-of-the-art in current applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology.

Connectionist Models of Cognition and Perception collects together refereed versions of twenty-three papers presented at the Seventh Neural Computation and Psychology Workshop (NCPW7). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on connectionist modelling in psychology.The articles have the main theme of connectionist modelling of cognition and perception, and are organised into six sections, on: cell assemblies, representation, memory, perception, vision and language. This book is an invaluable resource for researchers interested in neural models of psychological phenomena.

This book collects together refereed versions of papers presented at the Eighth Neural Computation and Psychology Workshop (NCPW 8). NCPW is a well-established workshop series that brings together researchers from different disciplines, such as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology. The articles are centred on the theme of connectionist modelling of cognition and perceptionn.The proceedings have been selected for coverage in:• Index to Scientific & Technical Proceedings® (ISTP® / ISI Proceedings)• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)• Index to Social Sciences & Humanities Proceedings® (ISSHP® / ISI Proceedings)• Index to Social Sciences & Humanities Proceedings (ISSHP CDROM version / ISI Proceedings)• CC Proceedings — Engineering & Physical Sciences• CC Proceedings — Biomedical, Biological & Agricultural Sciences

This volume collects together peer reviewed versions of most of the papers presented at the Ninth Neural Computation and Psychology Workshop (NCPW9), held in 2004 at the University of Plymouth (England). The conference invited submissions on neural computation models of all cognitive and psychological processes. The special theme of this year's workshop was “Modeling of Language, Cognition and Action. This topic had the aim to extend the conference appeal from the connectionist psychology community to leaders in neuroscience, robotics and cognitive systems design.The chapters cover the breadth of research in neural computation and psychology, with numerous papers that focus on language modeling, this year's special theme. The book includes chapters from internationally renowned researchers in the various fields of cognitive psychology (such as Art Glenberg and Jonathan Evans) as well as computer science and robotics (such as Stefan Wermter & Stefano Nolfi).The proceedings have been selected for coverage in:• Neuroscience Citation Index®• Index to Scientific & Technical Proceedings® (ISTP® / ISI Proceedings)• Index to Scientific & Technical Proceedings (ISTP CDROM version / ISI Proceedings)• Index to Social Sciences & Humanities Proceedings® (ISSHP® / ISI Proceedings)• Index to Social Sciences & Humanities Proceedings (ISSHP CDROM version / ISI Proceedings)• CC Proceedings — Engineering & Physical Sciences• CC Proceedings — Biomedical, Biological & Agricultural Sciences

This book introduces a host of connectionist models of cognition and behavior. The major areas covered are high-level cognition, language, categorization and visual perception, and sensory and attentional processing. All of the articles cover unpublished research work. The key contribution of this book is that it focuses exclusively on the advances in connectionist modeling in psychology. The papers are relatively short, and were explicitly written to be accessible to both connectionist modelers and experimental psychologists.

The neural computational approach to cognitive and psychological processes is relatively new. However, Neural Computation and Psychology Workshops (NCPW), first held 16 years ago, lie at the heart of this fast-moving discipline, thanks to its interdisciplinary nature — bringing together researchers from different disciplines such as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their work on models of cognitive processes.Once again, the Eleventh Neural Computation and Psychology Workshop (NCPW11), held in 2008 at the University of Oxford (England), reflects the interdisciplinary nature and wide range of backgrounds of this field. This volume is a collection of peer-reviewed contributions of most of the papers presented at NCPW11 by researchers from four continents and 15 countries.

At the fascinating frontiers of neurobiology, mathematics and psychophysics, this book addresses the problem of human and computer vision on the basis of cognitive modeling. After recalling the physics of light and its transformation through media and optics, Hérault presents the principles of the primate's visual system in terms of anatomy and functionality. Then, the neuronal circuitry of the retina is analyzed in terms of spatio-temporal filtering. This basic model is extended to the concept of neuromorphic circuits for motion processing and to the processing of color in the retina. For more in-depth studies, the adaptive non-linear properties of the photoreceptors and of ganglion cells are addressed, exhibiting all the power of the retinal pre-processing of images as a system of information cleaning suitable for further cortical processing. As a target of retinal information, the primary visual area is presented as a bank of filters able to extract valuable descriptors of images, suitable for categorization and recognition and also for local information extraction such as saliency and perspective. All along the book, many comparisons between the models and human perception are discussed as well as detailed applications to computer vision.

This volume collects together most of the papers presented at the Twelfth Neural Computation and Psychology Workshop (NCPW12) held in 2010 at Birkbeck College (England). The conference invited submissions on neurocomputational models of all cognitive and psychological processes. The special theme of this conference was “From Theory to Applications”, which allowed submissions of pure theoretical work and of pure applied work. This topic extended the boundaries of the conference and highlighted the extent to which computational models of cognition and models in general are integrated in the cognitive sciences.The chapters in this book cover a wide range of research topics in neural computation and psychology, including cognitive development, language processing, higher-level cognition, but also ecology-based modeling of cognition, philosophy of science, and real-world applications.

Computational Models of Cognitive Processes collects refereed versions of papers presented at the 13th Neural Computation and Psychology Workshop (NCPW13) that took place July 2012, in San Sebastian (Spain). This workshop series is a well-established and unique forum that brings together researchers from such diverse disciplines as artificial intelligence, cognitive science, computer science, neurobiology, philosophy and psychology to discuss their latest work on models of cognitive processes.