Levine, Daniel S. 　著

目次

Contents 　 Part I: Foundations of Neural Network Theory Chapter 1: Neural Networks for Modeling Behavior Chapter 2: Historical Outline Chapter 3: Associative Learning and Synaptic Plasticity Chapter 4: Competition, Lateral Inhibition, and Short-Term Memory Part II: Computational Cognitive Neuroscience Chapter 5: Progress in Cognitive Neuroscience Chapter 6: Models of Conditioning and Reinforcement Learning Chapter 7: Models of Coding, Categorization, and Unsupervised Learning Chapter 8: Models of Supervised Pattern and Category Learning Chapter 9: Models of Complex Mental Functions Appendices Appendix 1: Mathematical Techniques for Neural Networks Appendix 2: Basic Facts of Neurobiology References