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Author: Michael L. Commons
Publisher: Routledge
ISBN: 1317275985
Category : Psychology
Languages : en
Pages : 384
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Book Description
Originally published in 1991, this title was the result of a symposium held at Harvard University. It presents some of the exciting interdisciplinary developments of the time that clarify how animals and people learn to behave adaptively in a rapidly changing environment. The contributors focus on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviours that can satisfy internal needs – an area of inquiry as important for understanding brain function as it is for designing new types of freely moving autonomous robots. Since the authors agree that a dynamic analysis of system interactions is needed to understand these challenging phenomena – and neural network models provide a natural framework for representing and analysing such interactions – all the articles either develop neural network models or provide biological constraints for guiding and testing their design.
Author: Michael L. Commons
Publisher: Routledge
ISBN: 1317275985
Category : Psychology
Languages : en
Pages : 384
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Book Description
Originally published in 1991, this title was the result of a symposium held at Harvard University. It presents some of the exciting interdisciplinary developments of the time that clarify how animals and people learn to behave adaptively in a rapidly changing environment. The contributors focus on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviours that can satisfy internal needs – an area of inquiry as important for understanding brain function as it is for designing new types of freely moving autonomous robots. Since the authors agree that a dynamic analysis of system interactions is needed to understand these challenging phenomena – and neural network models provide a natural framework for representing and analysing such interactions – all the articles either develop neural network models or provide biological constraints for guiding and testing their design.
Author: Daniel S. Levine
Publisher: Psychology Press
ISBN: 1135692246
Category : Psychology
Languages : en
Pages : 573
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Book Description
This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value. Features of the second edition include: * A new section on spatiotemporal pattern processing * Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and recurrent back-propagation networks * A vastly expanded section on models of specific brain areas, such as the cerebellum, hippocampus, basal ganglia, and visual and motor cortex * Up-to-date coverage of applications of neural networks in areas such as combinatorial optimization and knowledge representation As in the first edition, the text includes extensive introductions to neuroscience and to differential and difference equations as appendices for students without the requisite background in these areas. As graphically revealed in the flowchart in the front of the book, the text begins with simpler processes and builds up to more complex multilevel functional systems. For more information visit the author's personal Web site at www.uta.edu/psychology/faculty/levine/
Author: Michael A. Arbib
Publisher: Springer Science & Business Media
ISBN: 1461245362
Category : Computers
Languages : en
Pages : 275
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Book Description
This is an exciting time. The study of neural networks is enjoying a great renaissance, both in computational neuroscience - the development of information processing models of living brains - and in neural computing - the use of neurally inspired concepts in the construction of "intelligent" machines. Thus the title of this volume, Dynamic Interactions in Neural Networks: Models and Data can be given two interpretations. We present models and data on the dynamic interactions occurring in the brain, and we also exhibit the dynamic interactions between research in computational neuroscience and in neural computing, as scientists seek to find common principles that may guide us in the understanding of our own brains and in the design of artificial neural networks. In fact, the book title has yet a third interpretation. It is based on the U. S. -Japan Seminar on "Competition and Cooperation in Neural Nets" which we organized at the University of Southern California, Los Angeles, May 18-22, 1987, and is thus the record of interaction of scientists on both sides of the Pacific in advancing the frontiers of this dynamic, re-born field. The book focuses on three major aspects of neural network function: learning, perception, and action. More specifically, the chapters are grouped under three headings: "Development and Learning in Adaptive Networks," "Visual Function", and "Motor Control and the Cerebellum.
Author: Magnus Enquist
Publisher: Princeton University Press
ISBN: 1400850789
Category : Science
Languages : en
Pages : 256
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Book Description
How can we make better sense of animal behavior by using what we know about the brain? This is the first book that attempts to answer this important question by applying neural network theory. Scientists create Artificial Neural Networks (ANNs) to make models of the brain. These networks mimic the architecture of a nervous system by connecting elementary neuron-like units into networks in which they stimulate or inhibit each other's activity in much the same way neurons do. This book shows how scientists can employ ANNs to analyze animal behavior, explore the general principles of the nervous systems, and test potential generalizations among species. The authors focus on simple neural networks to show how ANNs can be investigated by math and by computers. They demonstrate intuitive concepts that make the operation of neural networks more accessible to nonspecialists. The first chapter introduces various approaches to animal behavior and provides an informal introduction to neural networks, their history, and their potential advantages. The second chapter reviews artificial neural networks, including biological foundations, techniques, and applications. The following three chapters apply neural networks to such topics as learning and development, classical instrumental condition, and the role of genes in building brain networks. The book concludes by comparing neural networks to other approaches. It will appeal to students of animal behavior in many disciplines. It will also interest neurobiologists, cognitive scientists, and those from other fields who wish to learn more about animal behavior.
Author: Clive D.L. Wynne
Publisher: Psychology Press
ISBN: 113478757X
Category : Psychology
Languages : en
Pages : 336
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Book Description
This volume presents an international group of researchers who model animal and human behavior--both simple and complex. The models presented focus on such subjects as the pattern of eating in meals and bouts, the energizing and shaping impact of reinforcers on behavior, transitive inferential reasoning, responding to a compound stimulus, avoidance and escape learning, recognition memory, category formation, generalization, the timing of adaptive responses, and chromosomes exchanging information. The chapters are united by a common interest in adaptive behavior--whether of human, animal, or artificial system--and clearly demonstrate the rich variety of ways in which this fascinating area of research can be approached. In so doing, the book demonstrates the range of thought that qualifies as theorizing in the contemporary study of the mechanisms of adaptive behavior. It has two purposes: to bring together a very wide range of approaches in one place and to give authors space to explain how their ideas developed. Journal literature often presents fully-formed theories with no explanation of how an idea came to have the shape in which it is presented. In this volume, however, leaders in different fields provide background on the development of their ideas. Where once psychologists and a few zoologists had this field to themselves, now various types of computer scientists have added great energy to the mix.
Author: Daniel S. Levine
Publisher: Psychology Press
ISBN: 1317784545
Category : Psychology
Languages : en
Pages : 546
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Book Description
The articles gathered in this volume represent examples of a unique approach to the study of mental phenomena: a blend of theory and experiment, informed not just by easily measurable laboratory data but also by human introspection. Subjects such as approach and avoidance, desire and fear, and novelty and habit are studied as natural events that may not exactly correspond to, but at least correlate with, some (known or unknown) electrical and chemical events in the brain.
Author: D.L. Alkon
Publisher: Springer Science & Business Media
ISBN: 1461321158
Category : Medical
Languages : en
Pages : 490
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Book Description
This is the second volume to be based on a series of symposia being held periodically on the neurobiology of conditioning. The first, entitled Conditioning: Representation of Involved Neural Functions was based on a symposium held in Asilomar, Cali fornia, in October 1982 (Woody, 1982). The present volume is based on a sym posium, organized by D. Alkon and C. Woody, held at the Marine Biological Laboratory in Woods Hole, Massachusetts in November 1983. This series of sym posia and their publication are more than justified by the extraordinary progress be ing made during recent years in all branches of neuroscience and its application to our understanding of some of the basic neuronal mechanisms of conditioning and learning. Invertebrate models of conditioning have been used by many in the attempt to obtain a more thoroughly controlled analysis at the single cellular and synaptic level of the mechanisms involved in elementary conditioning in a simple nervous system. Examples of this approach are presented in this volume and utilize insects (grasshopper), crustacea (crayfish), and particularly the relatively simple nervous systems of mollusks (Aplysia and Hermissenda). In such preparations it is possible to carry out precise electrophysiological and neurochemical studies of single iden tified cells and synapses involved in such simple processes as habituation and sensitization, as well as simple forms of "associative" conditioning, usually using simple aversive or withdrawal reflexes.
Author: Yohan J. John
Publisher:
ISBN:
Category :
Languages : en
Pages : 188
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Book Description
Abstract: A central aspect of intelligence is the ability to apprehend and exploit temporal regularities in information streams. Efficient selection of actions often requires sensitivity to the time elapsed since event onsets. This thesis investigates neural mechanisms underlying learned internal representations of temporal intervals and their linkage via reinforcement learning to the production of timed actions. The focus is on interval timing in the seconds-to-minutes range within which most timed choices occur. Instrumental conditioning tasks have shed light on the underlying neural structures, pharmacology, and psychophysical properties associated with interval timing. This thesis presents a novel, neurobiologically plausible network model of reinforcement-driven interval timing. The model learns to control the temporal onset and offset of voluntary actions using the experienced outcome of each action as a teaching signal. Simulations of the model replicate experimental observations from the fixed interval (FI) procedure, the peak interval (PI) procedure, and the free-operant psychophysical procedure (FOPP). A statistical regularity observed in interval timing studies is the scalar property, a temporal analogue of the Weber-Fechner law. This property can be achieved using various combinations of mechanisms and assumptions, so further constraints are needed from neurobiology. Consistent with observations of ramping neurons in frontal cortex, the thesis demonstrates the scalar property for a neural system in which timing is governed by a bounded integrator whose adaptive integration rate becomes proportional to the reinforcement rate experienced during task exposures. Neural and behavioral data further implicate cortico-basal ganglia circuitry, and reveal that dopamine and acetylcholine have distinctive effects on learning and timed action. The simple reinforcement rate-based model was therefore embedded into a more capable neurobiological model, in which learned stimulus- and context-sensitive representations become selective for particular temporal intervals. Phasic dopamine responses constitute teaching signals that establish such time-sensitive cortical representations, and also serve in the credit-assignment of these representations to basal ganglia pathways that control response selection. Competition among the response-selecting cells determines the time of switching between response options. This new circuit model is not solely dedicated to timing, but exhibits interval timing as an aspect of learned instrumental behavior that efficiently exploits intermittent resources.
Author: Sebastian Thrun
Publisher: Springer International Series in Engineering and Computer Science
ISBN:
Category : Comics & Graphic Novels
Languages : en
Pages : 298
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Book Description
Describes a paradigm for machine learning that may open a new generation of methods, especially for situations in which a series of different learning tasks provides an opportunity for synergy among them. The explanation-based neural network approach transfers knowledge across multiple learning tasks, allowing domain knowledge accumulated in previous learning efforts to guide generalization in new learning tasks. The result is more accurate generalizations with less data than previous methods. The method is demonstrated in contexts of supervised learning, reinforced learning, robotics, and chess. Annotation copyright by Book News, Inc., Portland, OR
Author: J.W. Donahoe
Publisher: Elsevier
ISBN: 0080537367
Category : Computers
Languages : en
Pages : 601
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Book Description
This internationally authored volume presents major findings, concepts, and methods of behavioral neuroscience coordinated with their simulation via neural networks. A central theme is that biobehaviorally constrained simulations provide a rigorous means to explore the implications of relatively simple processes for the understanding of cognition (complex behavior). Neural networks are held to serve the same function for behavioral neuroscience as population genetics for evolutionary science. The volume is divided into six sections, each of which includes both experimental and simulation research: (1) neurodevelopment and genetic algorithms, (2) synaptic plasticity (LTP), (3) sensory/hippocampal systems, (4) motor systems, (5) plasticity in large neural systems (reinforcement learning), and (6) neural imaging and language. The volume also includes an integrated reference section and a comprehensive index.
