This textbook provides a general introduction to the field of neural networks, concentrating on networks for modeling brain processes involved in cognitive and behavioral functions.
This textbook provides a general introduction to the field of neural networks, concentrating on networks for modeling brain processes involved in cognitive and behavioral functions.
Daniel S. Levine is Professor of Psychology at the University of Texas at Arlington. He is a Fellow and former President of the International Neural Network Society. His research involves computational modeling of brain processes in decision making and cognitive-emotional interactions.
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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
