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Summary
Written for cognitive scientists, psychologists, computer scientists, engineers, and neuroscientists, this book provides an accessible overview of how computational network models are being used to model neurobiological phenomena. Each chapter presents a representative example of how biological data and network models interact with the authors' research. The biological phenomena cover network- or circuit-level phenomena in humans and other higher-order vertebrates.
Table of Contents
| List of Contributors | p. vii |
| Series Foreword | p. ix |
| Preface | p. xi |
| Hebb-Marr Networks and the Neurobiological Representation of Action in Space | p. 1 |
| References | p. 60 |
| Molecular Mechanisms for Synaptic Modification in the Visual Cortex: Interaction Between Theory and Experiment | p. 65 |
| Introduction | p. 65 |
| Concluding Remarks | p. 89 |
| References | p. 91 |
| Memorial Operation of Multiple, Interacting Simulated Brain Structures | p. 95 |
| Introduction | p. 95 |
| Acknowledgments | p. 127 |
| References | p. 128 |
| Adaptive Signal Processing and the Cerebellum: Models of Classical Conditioning and Vor Adaptation | p. 131 |
| Introduction | p. 131 |
| Acknowledgments | p. 179 |
| References | p. 180 |
| Elemental Adaptive Processes of Neurons and Synapses: A Statistical/Computational Perspective | p. 187 |
| Introduction1 | p. 187 |
| Concluding Thoughts | p. 230 |
| References | p. 231 |
| I Thought I Saw It Move: Computing Optical Flow in the Primate Visual System | p. 237 |
| References | p. 262 |
| Correlation-Based Models of Neural Development | p. 267 |
| Formulation of the Model Equations | p. 339 |
| the Characteristic Patterns of Ocular Dominance | p. 342 |
| References | p. 344 |
| Modeling Cortical Computation with Backpropagation | p. 355 |
| References | p. 382 |
| Author Index | p. 385 |
| Subject Index | p. 395 |
| Table of Contents provided by Publisher. All Rights Reserved. |
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