Introduction to Neural and Cognitive Modeling
📘 About This Book
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/
📖 Summary
Introduction to Neural and Cognitive Modeling by Daniel S. Levine is a comprehensive, thoroughly and thoughtfully revised second edition textbook that bridges the gap between complex computational theories and accessible cognitive science. Spanning 512 pages and published in the year 2000, this foundational work aims to make both the underlying principles and the intricate details of neural network modeling fully approachable for cognitive scientists of all varieties, alongside any curious readers interested in understanding how biological and artificial neural systems operate. Building upon the success of its predecessor, this edition systematically incorporates a wealth of modern research into a structured framework of proven pedagogical value. Throughout the volume, Levine explores the mechanics of how networks of neurons can process information, learn from their environments, and simulate various aspects of human thought and behavior. The core ideas delve into specialized architectures and processing techniques. Notably, the book features a dedicated new section on spatiotemporal pattern processing, which addresses how neural systems handle information that changes across both space and time. Readers are introduced to advanced network types, including ARTMAP networks, which represent the supervised version of adaptive resonance networks, as well as recurrent back-propagation networks that allow for complex temporal dynamics and feedback loops. Beyond abstract architectures, the text offers a vastly expanded section dedicated to models of specific brain areas. Levine guides the reader through computational representations of biological structures such as the cerebellum, the hippocampus, the basal ganglia, and both the visual and motor cortices. By connecting mathematical and computational models to actual neuroanatomy and physiology, the book provides a grounded perspective on how different regions of the brain contribute to learning, memory, movement, and perception. Furthermore, the volume provides up-to-date coverage of practical applications where neural networks are utilized to solve real-world problems. Whether applied to cognitive psychology, artificial intelligence, or neuroscience, Levine's text serves as an indispensable roadmap for anyone seeking to understand the intricate intersection of the mind, the brain, and computation.
🎯 Key Lessons
⚖️ Pros & Cons
✅ Pros
Thoroughly and thoughtfully revised for modern pedagogical use
Expands deeply into models of specific biological brain areas
Introduces advanced topics like ARTMAP and recurrent back-propagation
Makes complex neural modeling accessible to a wide variety of cognitive scientists
⚠️ Cons
May require prior foundational knowledge in mathematics or psychology
Research published after the year 2000 is not included
❓ FAQ
Who wrote Introduction to Neural and Cognitive Modeling? +
The book was written by Daniel S. Levine.
When was this second edition published? +
This thoroughly revised second edition was published in 2000.
What is the page count of the book? +
The book contains 512 pages.
What new network architectures are covered in this edition? +
This edition covers ARTMAP networks and recurrent back-propagation networks, among other updates.
Which specific brain areas are modeled in the expanded sections? +
The text covers models of the cerebellum, hippocampus, basal ganglia, and the visual and motor cortex.
