Memory and the Computational Brain

Informationen zum Autor C. R. Gallistel is Co-Director of the Rutgers Center for Cognitive Science. He is one of the foremost psychologists working on the foundations of cognitive neuroscience. His publications include The Symbolic Foundations of Conditional Behavior (2002), and The Organization of Learning (1990).Adam Philip King is Assistant Professor of Mathematics at Fairfield University. Klappentext Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty Zusammenfassung Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty Inhaltsverzeichnis Preface.1. Information.2. Bayesian Updating.3. Functions.4. Representations.5. Symbols.6. Procedures.7. Computation.8. Architectures.9. Data Structures.10. Computing with Neurons.11. The Nature of Learning.12. Learning Time and Space.13. The Modularity of Learning.14. Dead Reckoning in a Neural Network.15. Neural Models of Interval Timing.16. The Molecular Basis of Memory.References.Glossary.Index....

Autorentext
C. R. Gallistel is Co-Director of the Rutgers Center for Cognitive Science. He is one of the foremost psychologists working on the foundations of cognitive neuroscience. His publications include The Symbolic Foundations of Conditional Behavior (2002), and The Organization of Learning (1990). Adam Philip King is Assistant Professor of Mathematics at Fairfield University.

Klappentext

Memory and the Computational Brain offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. A provocative argument that impacts across the fields of linguistics, cognitive science, and neuroscience, suggesting new perspectives on learning mechanisms in the brain Proposes that the field of neuroscience can and should benefit from the recent advances of cognitive science and the development of information theory Suggests that the architecture of the brain is structured precisely for learning and for memory, and integrates the concept of an addressable read/write memory mechanism into the foundations of neuroscience Based on lectures in the prestigious Blackwell-Maryland Lectures in Language and Cognition, and now significantly reworked and expanded to make it ideal for students and faculty

Inhalt

Preface. 1. Information. 2. Bayesian Updating. 3. Functions. 4. Representations. 5. Symbols. 6. Procedures. 7. Computation. 8. Architectures. 9. Data Structures. 10. Computing with Neurons. 11. The Nature of Learning. 12. Learning Time and Space. 13. The Modularity of Learning. 14. Dead Reckoning in a Neural Network. 15. Neural Models of Interval Timing. 16. The Molecular Basis of Memory. References. Glossary. Index.