Neural Networks and Analog Computation: Beyond the Turing Limit - Progress in Theoretical Computer Science (Paperback)Hava T. Siegelmann (author)
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The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.
Publisher: Springer-Verlag New York Inc.
Number of pages: 181
Weight: 320 g
Dimensions: 235 x 155 x 11 mm
Edition: Softcover reprint of the original 1st ed. 199
"All of the three primary questions are considered: What computational models can the net simulate (within polynomial bounds)? What are the computational complexity classes that are relevant to the net? How does the net (which, after all, is an analog device) relate to Church's thesis? Moreover the power of the basic model is also analyzed when the domain of reals is replaced by the rationals and the integers."
"Siegelmann's book focuses on the computational complexities of neural networks and making this research accessible...the book accomplishes the said task nicely."
---SIAM Review, Vol. 42, No 3.