Introduction to Neural and Cognitive Modeling: 3rd Edition

By Daniel S. Levine

Book Overview

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.

• Format: Hardcover
• Language: English
• ISBN: 1848726473
• ISBN13: 9781848726475
• Release Date: October 2018
• Publisher: Routledge
• Length: 466 Pages
• Weight: 1.78 lbs.
• Dimensions: 1.1" x 6.0" x 9.0"

Customer Reviews

Indispensable textbook and overall reference

Daniel Levine's "Introduction to Neural and Cognitive Modeling" offers a unique psychological perspective on neural networks, cognitive science, and machine intelligence from one of the foremost experts in the field. This textbook, for those who take the time to read it, offers a comprehensive overview of the history of the discipline, neurophysiology, memory models, learning and conditioning, machine categorization, knowledge representation, and recent advances in the science. For those without a background in neurobiology or differential equations, he includes primers as well to get you up to speed. The flow of the textbook is laid out such that those courses focusing on only one or two topics can cover them without needing to review too much background. Levine also presents a variety of models useful for speech recognition, motor control, spatiotemporal pattern recognition, supervised and unsupervised machine learning and classification models, long and short-term memory models, attention, inhibition, computer vision, error correction, and back propagation. This text is one of the most bookmarked and referenced in my collection, and the one I most frequently recommend and lend to my colleagues. It operates as a textbook as well as a reference, with coverage as deep as it is broad. I highly recommend this text to novice and expert alike, or to anyone interested in the art and science of neural network-based cognitive modeling with a distinct psychological flavor. This one's a gem I'm glad to keep close at hand.