This thoroughly, thoughtfully revised edition of a very successful textbook makes the principles and the details of neural network modeling accessible to cognitive scientists of all varieties as well as to others interested in these models. Research since the publication of the first edition has been systematically incorporated into a framework of proven pedagogical value.
Features of the second edition include:
* A new section on spatiotemporal pattern processing
* Coverage of ARTMAP networks (the supervised version of adaptive resonance networks) and recurrent back-propagation networks
* A vastly expanded section on models of specific brain areas, such as the cerebellum, hippocampus, basal ganglia, and visual and motor cortex
* Up-to-date coverage of applications of neural networks in areas such as combinatorial optimization and knowledge representation
As in the first edition, the text includes extensive introductions to neuroscience and to differential and difference equations as appendices for students without the requisite background in these areas. As graphically revealed in the flowchart in the front of the book, the text begins with simpler processes and builds up to more complex multilevel functional systems.
For more information visit the author's personal Web site at www.uta.edu/psychology/faculty/levine/
"The book offers an in-depth introduction to the realm of neural networks and is suitable for advanced undergraduate and graduate students….It manages to convey the diversity and applicability of the neural network field. The book is well written, well documented, and inspiring."
—Cognitive Science Society Newsletter
"While numerous textbooks on artificial neural networks have been published, textbooks on biologically/psychologically oriented neural networks are much fewer in number. Therefore, this book continues to keep its niche. It remains as an attractive choice for an introductory textbook…this is a welcome update of a well-conceived and well-written introductory textbook."
Contents: Preface. Preface to the Second Edition. Brain and Machine: The Same Principles? Historical Outline. Associative Learning and Synaptic Plasticity. Competition, Lateral Inhibition, and Short-Term Memory. Conditioning, Attention, and Reinforcement. Coding and Categorization. Optimization, Control, Decision, and Knowledge Representation. A Few Recent Technical Advances. Appendices: Basic Facts of Neurobiology. Difference and Differential Equations in Neural Networks.