Announcing a New Book

[Hava Siegelmann]

                 Neural Networks and Analog Computation:
                       Beyond the Turing Limit

Author: Hava T. Siegelmann


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.  What
emerges is a Church-Turing-like thesis, applied to the field of analog
computation, which features the neural network model in place of the
digital Turing machine.  This new concept can serve as a point of
departure for the development of alternative, supra-Turing
computational theories.  On a mathematical level, the treatment of
neural computations enriches the theory of computation but also
explicates 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.

Special care has been taken to explain the theory clearly and
concisely.  The first chapter reviews the fundamental terms of modern
computational theory from the point of view of neural networks and
serves as a reference for the remainder of the book.  Each of the
subsequent chapters opens with introductory material and proceeds to
explain the chapter's connection to the development of the theory.
Thereafter, the concept is defined in mathematical terms.


Birkhäuser
Boston * Basel * Berlin
ISBN 0-8176-3949-7

e-mail: orders at birkhauser.ch
Web: http://www.birkhauser.ch