Articles liés à Neural Network Models of Conditioning and Action
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
Présentation de l'éditeur
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.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Neural Network Models of Conditioning and Action: Quantitative Analyses of Behavior
Edité par
Lawrence Erlbaum Associates, 1991
ISBN 10 : 0805808426
ISBN 13 : 9780805808421
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Expédition nationale : Etats-Unis
Expédition nationale : Etats-Unis
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