This update of the 1981 classic on neural networks includes new commentaries by the authors that show how the original ideas are related to subsequent developments. As researchers continue to uncover ways of applying the complex information processing abilities of neural networks, they give these models an exciting future which may well involve revolutionary developments in understanding the brain and the mind -- developments that may allow researchers to build adaptive intelligent machines. The original chapters show where the ideas came from and the new commentaries show where they are going.
This update of the 1981 classic on neural networks includes new commentaries by the authors that show how the original ideas are related to subsequent developments. As researchers continue to uncover ways of applying the complex information processing abilities of neural networks, they give these models an exciting future which may well involve revolutionary developments in understanding the brain and the mind -- developments that may allow researchers to build adaptive intelligent machines. The original chapters show where the ideas came from and the new commentaries show where they are going.
Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of "neural computing", or "neural networks", as the previous neural models are nowadays called. The incentive, of course, has been to develop new com puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in formation processing principles of the same type as those encountered in the brain. All new ideas for the "neural computers" are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer tain lines of thought, disciplines, and criteria should be followed. It is the pur pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con tents is taken over from the first edition.
In Memory Distortion, contributions from a multidisciplinary team of eminent scholars form the basis of an exploration of a range of phenomena including: hypnosis, confabulation, source amnesia, flashbulb memories and repression.
This handbook includes an overview of those areas of cognition and language processing that are relevant to the field of communication disorders, and provides examples of theoretical approaches to problems and issues in communication disorders. The first section includes a collection of chapters that outline some of the basic considerations and areas of cognition and language that underlie communication processing; a second section explains and exemplifies some of the influential theories of psycholinguistic/cognitive processing; and the third section illustrates theoretical applications to clinical populations. There is coverage of theories that have been either seminal or controversial in the research of communication disorders. Given the increasing multi-cultural workload of many practitioners working with clinical populations, chapters relating to bilingual populations are also included. The volume book provides a single interdisciplinary source where researchers and students can access information on psycholinguistic and cognitive processing theories relevant to clinical populations. A range of theories, models, and perspectives are provided. The range of topics and issues illustrate the relevance of a dynamic interaction between theoretical and applied work, and retains the complexity of psycholinguistic and cognitive theory for readers (both researchers and graduate students) whose primary interest is the field of communication disorders.
The rapid growth of neural network research has led to a major reappraisal of many fundamental assumptions in cognitive and perceptual psychology. This text—aimed at the advanced undergraduate and beginning postgraduate student—is an in-depth guide to those aspects of neural network research that are of direct relevance to human information processing. Examples of new connectionist models of learning, vision, language and thought are described in detail. Both neurological and psychological considerations are used in assessing its theoretical contributions. The status of the basic predicates like exclusive-OR is examined, the limitations of perceptrons are explained and properties of multi-layer networks are described in terms of many examples of psychological processes. The history of neural networks is discussed from a psychological perspective which examines why certain issues have become important. The book ends with a general critique of the new connectionist approach. It is clear that new connectionism work provides a distinctive framework for thinking about central questions in cognition and perception. This new textbook provides a clear and useful introduction to its theories and applications.
The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoretical developments. Each paper is largely self-contained and includes a complete bibliography. The methodological part of the book contains two papers on learning, one paper which presents a computational model of intracortical inhibitory effects, a paper presenting a new development of the random neural network, and two papers on associative memory models. The applications and examples portion contains papers on image compression, associative recall of simple typed images, learning applied to typed images, stereo disparity detection, and combinatorial optimisation.
The purpose of this book is to describe the memory system of the brain, taking into account all the levels of neural organization: molecule, cell, small network, and anatomical circuit. This synthetic approach is necessary for determining the real mechanisms among the potential ones, that is the neural bases of learning and memory in intact organisms functioning under normal conditions. For this purpose, data from molecular, cellular and behavioral neurobiology, neuropsychology, animal and human psychology, and neural modellization are comprehensively reviewed by leading specialists and brought together in an original synthesis.
This festschrift represents the proceedings of a conference held in honor of Bennet B. Murdock, one of the foremost researchers and theoreticians on human memory and cognition. A highly renowned investigator respected for both his empirical and theoretical contributions to the field, Murdock summarized and focused a large amount of research activity with his 1974 book Human Memory: Theory and Data. This unique collection of articles addresses many of the issues discussed in his classic text. Divided into five principal sections, its coverage includes: theoretical perspectives on human memory ranging from a biological view to an exposition of the value of formal models; recent progress in the study of processes in immediate memory and recognition memory; and new developments in componential and distributed approaches to the modeling of human memory. Each section concludes with an integrative commentary provided by some of Murdock’s eminent colleagues from the University of Toronto. Thus, this book offers a diversity of perspectives on contemporary topics in the discipline, and will be of interest to students and scholars in all branches of cognitive science.
This review volume represents the first attempt to provide a comprehensive overview of this exciting and rapidly evolving development. The book comprises specially commissioned articles by leading researchers in the areas of neural networks and connectionist systems, classifier systems, adaptive network systems, genetic algorithm, cellular automata, artificial immune systems, evolutionary genetics, cognitive science, optical computing, combinatorial optimization, and cybernetics.
