Neural Network Models of Conditioning and Action
Quantitative Analyses of Behavior
Published on 1. March 1991
Book
Hardback
376 pages
978-0-8058-0842-1 (ISBN)
Description
The result of a conference held at Harvard University, this volume presents some of the exciting interdisciplinary developments that are clarifying how animals and people learn to behave adaptively in a rapidly changing environment. The text focuses on aspects of how recognition learning, reinforcement learning, and motor learning interact to generate adaptive goal-oriented behaviors that can satisfy internal needs -- an important topic for understanding brain function as well as for designing new types of autonomous robots.
Because a dynamic analysis of system interactions is needed to understand these challenging phenomena -- and neural network models provide a natural framework for representing and analyzing such interactions -- all the articles either develop neural network models or provide biological constraints for guiding and testing their design. The result of a conference held at Harvard University, this volume presents some of the exciting interdisciplinary developments 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 behaviors that can satisfy internal needs -- an area of inquiry as important for understanding brain function as it is for designing new types of autonomous robots.
Because a dynamic analysis of system interactions is needed to understand these challenging phenomena -- and neural network models provide a natural framework for representing and analyzing such interactions -- all the articles either develop neural network models or provide biological constraints for guiding and testing their design.
Because a dynamic analysis of system interactions is needed to understand these challenging phenomena -- and neural network models provide a natural framework for representing and analyzing such interactions -- all the articles either develop neural network models or provide biological constraints for guiding and testing their design. The result of a conference held at Harvard University, this volume presents some of the exciting interdisciplinary developments 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 behaviors that can satisfy internal needs -- an area of inquiry as important for understanding brain function as it is for designing new types of autonomous robots.
Because a dynamic analysis of system interactions is needed to understand these challenging phenomena -- and neural network models provide a natural framework for representing and analyzing such interactions -- all the articles either develop neural network models or provide biological constraints for guiding and testing their design.
More details
Series
Language
English
Place of publication
Mahwah
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Professional and scholarly
Dimensions
Height: 229 mm
Width: 152 mm
ISBN-13
978-0-8058-0842-1 (9780805808421)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Michael L. Commons | Stephen Grossberg | John E.R. Staddon
Neural Network Models of Conditioning and Action
Quantitative Analyses of Behavior
Book
03/1991
Lawrence Erlbaum Associates Inc
€55.29
Article exhausted; check different version
Content
Contents: M.L. Commons, S. Grossberg, J.E.R. Staddon, Preface. Part I:Models of Classical Conditioning. D.L. Alkon, T.P. Vogl, K.T. Blackwell, D. Tam, Memory Function in Neural and Artificial Networks. D.A. Baxter, D.V. Buonomano, J.L. Raymond, D.G. Cook, F.M. Kuenzi, T.J. Carew, J.H. Byrne, Empirically Derived Adaptive Elements and Networks Simulate Associative Learning. W.B. Levy, C.M. Colbert, Adaptive Synaptogenesis Can Complement Associate Potentiation/Depression. S. Grossberg, A Neural Network Architecture for Pavlovian Conditioning: Reinforcement, Attention, Forgetting, Timing. D.S. Levine, P.S. Prueitt, Simulations of Conditioned Perseveration and Novelty Preference from Frontal Lobe Damage. N.A. Schmajuk, J.J. DiCarlo, Neural Dynamics of Hippocampal Modulation of Classical Conditioning. J.W. Moore, Implementing Connectionist Algorithms for Classical Conditioning in the Brain. Part II:Models of Instrumental Conditioning. M.L. Commons, E.W. Bing, C.C. Griffy, E.J. Trudeau, Models of Acquisition and Preference. R.M. Church, H.A. Broadbent, A Connectionist Model of Timing. W.S. Maki, A.M. Abunawass, A Connectionist Approach to Conditional Discriminations: Learning, Short-Term Memory, and Attention. J.E.R. Staddon, Y. Zhang, On the Assignment-of-Credit Problem in Operant Learning. S.J. Hanson, Behavioral Diversity, Search and Stochastic Connectionist Systems.