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Neural Network Models of Conditioning and Action (Quantitative Analyses of Behavior) ペーパーバック – 1991/4/1
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.
- 本の長さ376ページ
- 言語英語
- 出版社Lawrence Erlbaum Assoc Inc
- 発売日1991/4/1
- 寸法16.51 x 3.18 x 24.13 cm
- ISBN-100805808426
- ISBN-13978-0805808421
登録情報
- 出版社 : Lawrence Erlbaum Assoc Inc (1991/4/1)
- 発売日 : 1991/4/1
- 言語 : 英語
- ペーパーバック : 376ページ
- ISBN-10 : 0805808426
- ISBN-13 : 978-0805808421
- 寸法 : 16.51 x 3.18 x 24.13 cm
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