Dana H. Ballard is a Professor in the Department of Computer Sciences at the University of Texas at Austin. His research delves into natural computation, exploring the intersection of computer science and cognition. Ballard's work seeks to understand how the brain processes information, applying these insights to the development of intelligent systems. His approach bridges theoretical concepts with practical applications in robotics and artificial intelligence.
The book offers an in-depth exploration of the computational principles that support emerging brain models. It delves into the foundational concepts and methodologies that are shaping our understanding of brain function and neural networks, making it a vital resource for those interested in neuroscience and computational modeling.
Exploring the parallels between brain function and silicon computing, the author argues for a hierarchical understanding of both systems. Despite their differing architectures, Ballard posits that computational tools are vital for deciphering the complexities of neural circuitry. By breaking down brain computation into various levels of abstraction, he reveals that the brain's organization mirrors that of computing, allowing for a simplified approach to understanding its intricate processes.
InhaltsverzeichnisHierarchic System Overview.Preprocessing Operations.Lung Boundary Detection.Candidate Tumor Site Detection.Boundary Detection via Dynamic Programming.Feature Selection and Classification.Experimental Results.Conclusions.