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The Adaptive Brain, II: Vision, Speech, Language, and Motor Control focuses on a unified theoretical analysis and predictions of important psychological and neurological data that illustrate the development of a true theory of mind and brain. The publication first elaborates on the quantized geometry of visual space and neural dynamics of form perception. Discussions focus on reflectance rivalry and spatial frequency detection, figure-ground separation by filling-in barriers, and disinhibitory propagation of functional scaling from boundaries to interiors. The text then takes a look at neural dynamics of perceptual grouping and brightness perception. Topics include simulation of a parametric binocular brightness study, smoothly varying luminance contours versus steps of luminance change, macrocircuit of processing stages, paradoxical percepts as probes of adaptive processes, and analysis of the Beck theory of textural segmentation. The book examines the neural dynamics of speech and language coding and word recognition and recall, including automatic activation and limited-capacity attention, a macrocircuit for the self-organization of recognition and recall, role of intra-list restructuring arid contextual associations, and temporal order information across item representations. The manuscript is a vital source of data for scientists and researchers interested in the development of a true theory of mind and brain.
The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl
In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Neural Networks for Pattern Recognition takes the pioneering work in artificial neural networks by Stephen Grossberg and his colleagues to a new level. In a simple and accessible way it extends embedding field theory into areas of machine intelligence that have not been clearly dealt with before. Following a tutorial of existing neural networks for pattern classification, Nigrin expands on these networks to present fundamentally new architectures that perform realtime pattern classification of embedded and synonymous patterns and that will aid in tasks such as vision, speech recognition, sensor fusion, and constraint satisfaction. Nigrin presents the new architectures in two stages. First he presents a network called Sonnet 1 that already achieves important properties such as the ability to learn and segment continuously varied input patterns in real time, to process patterns in a context sensitive fashion, and to learn new patterns without degrading existing categories. He then removes simplifications inherent in Sonnet 1 and introduces radically new architectures. These architectures have the power to classify patterns that may have similar meanings but that have different external appearances (synonyms). They also have been designed to represent patterns in a distributed fashion, both in short-term and long-term memory.
This volume contains the proceedings of the workshop held in March 1990 at Austin, Texas on Self-Organization, Emerging Properties and Learning. The workshop was co-sponsored by NATO Scientific Affairs Division, Solvay Institutes of Physics and Chemistry, the University of Texas at Austin and IC2 Institute at Austin. It gathered representatives from a large spectrum of scientific endeavour. The subject matter of self-organization extends over several fields such as hydrodynamics, chemistry, biology, neural networks and social sciences. Several key concepts are common to all these different disciplines. In general the self-organization processes in these fields are described in the framework of the nonlinear dynamics, which also governs the mechanisms underlying the learning processes. Because of this common language, it is expected that any progress in one area could benefit other fields, thus a beneficial cross fertilization may result. In last two decades many workshops and conferences had been organized in various specific fields dealing with self-organization and emerging properties of systems. The aim of the workshop in Austin was to bring together researchers from seemingly unrelated areas and interested in self-organization, emerg{ng properties and learning capabilities of interconnected multi-unit systems. The hope was to initiate interesting exchange and lively discussions. The expectations of the organiziers are materialized in this unusual collection of papers, which brings together in a single volume representative research from many related fields. Thus this volume gives to the reader a wider perspective over the generality and ramifications of the key concepts of self organization.
The Adaptive Brain I
Suitable for advanced undergraduates and graduate students, this overview introduces theoretical and practical aspects of adaptive control, with emphasis on deterministic and stochastic viewpoints. 1995 edition.
Pattern Recognition by Self-Organizing Neural Networks presentsthe most recent advances in an area of research that is becoming vitally important in the fields ofcognitive science, neuroscience, artificial intelligence, and neural networks in general. The 19articles take up developments in competitive learning and computational maps, adaptive resonancetheory, and specialized architectures and biological connections. Introductorysurvey articles provide a framework for understanding the many models involved in various approachesto studying neural networks. These are followed in Part 2 by articles that form the foundation formodels of competitive learning and computational mapping, and recent articles by Kohonen, applyingthem to problems in speech recognition, and by Hecht-Nielsen, applying them to problems in designingadaptive lookup tables. Articles in Part 3 focus on adaptive resonance theory (ART) networks,selforganizing pattern recognition systems whose top-down template feedback signals guarantee theirstable learning in response to arbitrary sequences of input patterns. In Part 4, articles describeembedding ART modules into larger architectures and provide experimental evidence fromneurophysiology, event-related potentials, and psychology that support the prediction that ARTmechanisms exist in the brain. Contributors: J.-P. Banquet, G.A. Carpenter, S.Grossberg, R. Hecht-Nielsen, T. Kohonen, B. Kosko, T.W. Ryan, N.A. Schmajuk, W. Singer, D. Stork, C.von der Malsburg, C.L. Winter.
This volume collects a number of the invited lectures and a few selected contrib utions presented at the International Symposium on Structure and Dynamics of Nucleic Acids, Proteins and Membranes held August 31st through September 5th, 1986, in Riva del Garda, Italy. The title of the conference as well as a number of the topics covered represent a continuation of two previous conferences, the first held in 1982 at the University of California in San Diego, and the second in 1984 in Rome at the Accademia dei Lincei. These two earlier conferences have been documented in Structure and Dynamics: Nucleic Acids and Proteins, edited by E. Clementi and R. H. Sarma, Adenine Press, New York, 1983, and Structure and Motion: Membranes, Nucleic Acids and Proteins, edited by E. Clementi, G. Corongiu, M. H. Sarma and R. H. Sarma, Adenine Press, New York, 1985. At this conference in Riva del Garda we were very hesitant to keep the name of the conference the same as the two previous ones. Indeed, a number of topics discussed in this conference were not included in the previous ones and even the emphasis of this gathering is only partly reflected in the conference title. An alternative title would have been Structure and Dynamics of Nucleic Acids, Proteins, and Higher Functions, or, possibly, "higher components" rather than "higher functions.