Neural network models of conditioning and action
著者
書誌事項
Neural network models of conditioning and action
(Psychology library editions, . Cognitive science ; v. 6)
Routledge, 2017, c1991
- : hbk
大学図書館所蔵 全1件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Reprint. Originally published: Hillsdale, N.J. : Lawrence Erlbaum Associates, 1991. (A volume in the Quantitative analyses of behavior series)
"This book grew out of a symposium of the same name that was held at Harvard University on June 2-3, 1989."--Pref
ISBN for subseries: 9781138191631
Includes bibliographical references and indexes
内容説明・目次
内容説明
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.
目次
About the Editors. About the Contributors. Preface Part 1: Models of Classical Conditioning 1. Memory Function in Neural and Artificial Networks Daniel L. Alkon, Thomas P. Vogl, Kim T. Blackwell and David Tam 2. Empirically Derived Adaptive Elements and Networks Simulate Associative Learning Douglas A. Baxter, Dean V. Buonomano, Jennifer L. Raymond, David G. Cook, Frederick M. Kuenzi, Thomas J. Carew and John H. Byrne 3. Adaptive Synaptogenesis Can Complement Associative Potentiation/Depression William B. Levy and Costa M. Colbert 4. A Neural Network Architecture for Pavlovian Conditioning: Reinforcement, Attention, Forgetting, Timing Stephen Grossberg 5. Simulations of Conditioned Perseveration and Novelty Preference from Frontal Lobe Damage Daniel S. Levine and Paul S. Prueitt 6. Neural Dynamics and Hippocampal Modulation of Classical Conditioning Nestor A. Schmajuk and James J. DiCarlo 7. Implementing Connectionist Algorithms for Classical Conditioning in the Brain John W. Moore Part 2: Models of Instrumental Conditioning 8. Models of Acquisition and Preference Michael L. Commons, Eric W. Bing, Charla C. Griffy and Edward J. Trudeau 9. A Connectionist Model of Timing Russell M. Church and Hilary Broadbent 10. A Connectionist Approach to Conditional Discriminations: Learning, Short-Term Memory, and Attention William S. Maki and Adel M. Abunawass 11. On the Assignment-of-Credit Problem in Operant Learning John E. R. Staddon and Y. Zhang 12. Behavioral Diversity, Search and Stochastic Connectionist Systems Stephen Jose Hanson. Author Index. Subject Index.
「Nielsen BookData」 より