Introduction to neural and cognitive modeling

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.

This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part one explores the philosophy of modeling and the field's history starting from the mid-1940s, and then discusses past models of associative learning and of short-term memory that provide building blocks for more complex recent models. Part two of the book reviews recent experimental findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence learning, behavioral control, decision making, reasoning, and creativity. The book presents these models both as abstract ideas and through examples and concrete data for specific brain regions. The book includes two appendices to help ground the reader: one reviewing the mathematics used in network modeling, and a second reviewing basic neuroscience at both the neuron and brain region level. The book also includes equations, practice exercises, and thought experiments.