Turing's connectionism : an investigation of neural network architectures
Author(s)
Bibliographic Information
Turing's connectionism : an investigation of neural network architectures
（Discrete mathematics and theoretical computer science）
Springer-Verlag, c2002
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Note
Includes bibliographical references (p. [187]-196) and index
Description and Table of Contents
Description
Christof Teuscher revives, analyzes, and simulates Turing's ideas, applying them to different types of problems, and building and training Turing's machines using evolutionary algorithms. In a little known paper entitled 'Intelligent Machinery' Turing investigated connectionist networks, but his work was dismissed as a 'schoolboy essay'and it was left unpublished until 1968, 14 years after his death. This is not a book about today's (classical) neural networks, but about the neuron network-like structures proposed by Turing. One of its novel features is that it actually goes beyond Turing's ideas by proposing new machines. The book also contains a Foreward by B. Jack Copeland and D. Proudfoot.
Table of Contents
Foreword by B.J. Copeland and D. Proudfoot.- INTRODUCTION: Turing's Anticipation of Connectionism. Alan Mathison Turing. Connectionism and Artificial Neural Networks. Historical Context and Related Work. Organization of the Book. Book Web-Site.- INTELLIGENT MACHINERY: Machines. Turing's Unorganized Machines. Formalization and Analysis of Unorganized Machines. New Unorganized Machines. Simulation of TBI-type Machines with MATLAB.- SYNTHESIS OF LOGICAL FUNCTIONS AND DIGITAL SYSTEMS WITH TURING NETWORKS: Combinational versus Sequential Systems. Synthesis of Logical Functions with A-type Networks. Synthesis of Logical Functions with TB-type Networks. Multiplexer and Demultiplexer. Delay-Unit. Shift-Register. How to Design Complex Systems. Hardware Implementation.- ORGANIZING UNORGANIZED MACHINES: Evolutionary Algorithms. Evolutionary Artificial Neural Networks. Example: Evolve Networks that Regenerate Bitstreams. Signal Processing in Turing Networks. Pattern Classification. Examples: Pattern Classification with Genetic Algorithms. A Learning Algorithm for Turing Networks.- NETWORK PROPERTIES AND CHARACTERISTICS: General Properties. Computational Power. State Machines. Threshold Logic. Dynamical Systems and the State-Space Model. Random Boolean Networks. Attractors. Network Stability and Activity. Chaos, Bifurcation, and Self-Organized Criticality. Topological Evolution and Self-Organization. Hypercomputation: Computing Beyond the Turing Limit with Turing's Neural Networks?- EPILOGUE.
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