Author Description
Theory Of Neural Information Processing Systems Provides An Explicit, Coherent, And Up-to-date Account Of The Modern Theory Of Neural Information Processing Systems. It Has Been Carefully Developed For Graduate Students From Any Quantitative Discipline, Including Mathematics, Computer Science, Physics, Engineering Or Biology, And Has Been Thoroughly Class-tested By The Authors Over A Period Of Some 8 Years. Exercises Are Presented Throughout The Text And Notes On Historical Background And Further Reading Guide The Student Into The Literature. All Mathematical Details Are Included And Appendices Provide Further Background Material, Including Probability Theory, Linear Algebra And Stochastic Processes, Making This Textbook Accessible To A Wide Audience.
Menrva's Summary
THE THESIS
This text investigates the mathematical foundations and theoretical frameworks governing neural information processing systems. The authors, A. C. C. Coolen, P. Sollich, and R. Kuehn, draw upon their extensive experience in physics and computational modeling to provide a rigorous, self-contained guide. By integrating concepts from probability theory, linear algebra, and stochastic processes, the book establishes a comprehensive methodology for analyzing neural network behavior and information storage capacity.
THE SCOPE MAP
What You Will Find
- Mathematical derivations for neural network dynamics
- Analysis of information processing capacity and storage
- Integration of stochastic processes and statistical mechanics
- Pedagogical exercises and historical context notes
Scope Limits
- Does not cover high-level software implementation or coding practices
- Excludes non-quantitative or purely qualitative discussions of neural architecture
THE AUTHORITY PERSPECTIVE
Experts recognize this work as a rigorous, foundational text suitable for graduate-level study across quantitative disciplines. Readers frequently note the high density of the mathematical prose, which requires a solid background in linear algebra and probability to fully comprehend.
Page Count:
596
Publication Date:
2005-01-01
Publisher:
Oup Oxford
ISBN-10:
0191583006
ISBN-13:
9780191583001
Engineering
Mathematics
Physics
Computer Science
textbook