Transcending Turing Computability

  • Authors:

  • B. J. Maclennan

  • Affiliations:

  • Department of Computer Science, University of Tennessee, Knoxville, TN, USA/ E-mail: maclennan@cs.utk.edu

  • Venue:
  • Minds and Machines
  • Year:
  • 2003

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Abstract

It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing-machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or inspired by natural systems). This points to the need for new models of computation addressing issues orthogonal to those that have occupied the traditional theory of computation.