Hypercomputation / Field Computation Bibliography

Hypercomputing may be defined as computing outside of the bounds of Turing-computability. There are two senses in which this may be interpreted. The narrower interpretation is that hypercomputation permits the computation of functions that are not Turing-computable. The broader interpretation calls into question the entire set of assumptions on which Turing-computability is based (discrete symbols, finite data structures, discrete finite rules, discrete-time operations, etc.), and thereby provides a new model of computation orthogonal to the Turing model.

Field computation is hypercomputation in both of these senses.

Bibliography of Field-Computations Approaches to Hypercomputation

Papers Authored or Co-authored by Bruce MacLennan

  1. My talk "Transcending Turing Computability," handouts or slides (both in postscript form). - New!

  2. Field Computation in Natural and Artificial Intelligence: Extended Version (compressed postscript), Technical Report CS-99-422, April 14, 1999. Abridged in Information Sciences, Vol. 119 (1999), pp. 73-89. Also available in CogPrint archives.

    A summary (Technical Report CS-98-398, August 21, 1998) is available as compressed postscript and as hypertext. This paper was presented at Neuro-quantum Information Processing session of 3rd International Conference on Computational Intelligence and Neuroscience, Research Triangle Park, NC, October 23-28, 1998.

  3. ``Field Computation in Motor Control'' (postscript form), by Bruce MacLennan, invited for Self-Organization, Computational Maps and Motor Control, ed. by Pietro G. Morasso and Vittorio Sanguineti, Elsevier-North Holland, in press. A version of this paper is available as hypertext (although it contains a few glitches).

  4. ``Image and Symbol: Continuous Computation and the Emergence of the Discrete,'' by Bruce MacLennan, invited contribution for book, Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, edited by Vasant Honavar and Leonard Uhr, New York, NY: Academic Press, 1994, pp. 207-240. Also University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-93-199, December 18, 1992 (revised August 5, 1993), 33 pages. A proposed theoretical construct (the simulacrum) for connectionist models analogous to the calculus in symbolic models.

  5. ``Continuous Symbol Systems: The Logic of Connectionism,'' by Bruce MacLennan, Neural Networks for Knowledge Representation and Inference, edited by Daniel S. Levine and Manuel Aparicio IV, Hillsdale, NJ: Lawrence Erlbaum, 1994, pp. 83-120. Also University of Tennessee, Knoxville, Computer Science Department technical report CS-91-145, September 1991, 47 pages. This paper presents a preliminary formulation of continuous symbol systems and indicates how they may aid in understanding the development of connectionist theories.

  6. ``A Computationally Universal Field Computer That is Purely Linear,'' University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-93-206, September 14, 1993, 28 pp.; by David H. Wolpert and Bruce J. MacLennan. Also Santa Fe Institute Technical Report 93-09-056. This paper proves a particular field computer (a spatial continuum-limit neural net) governed by a purely linear integro-differential equation is computationally universal.

  7. ``Characteristics of Connectionist Knowledge Representation,'' by Bruce MacLennan, Information Sciences 70 (1993), pp. 119-143. Also University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-91-147, November 1991, 22 pages. We present a construct, called a simulacrum, which has a similar relation to connectionist knowledge representation as the calculus does to symbolic knowledge representation.

  8. ``Field Computation in the Brain,'' (postscript) by Bruce J. MacLennan, University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-92-174, October 1, 1992, 35 pages. Continuous information representation and processing, field-based theories of sensorimotor intentions and of model-based deduction.

  9. ``Research Issues in Flexible Computing: Two Presentations in Japan,'' by Bruce J. MacLennan, University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-92-172, September 4, 1992, 17 pages. The text of two presentations made in Japan both of which deal with the Japanese "Real World Computing Project" (informally known as the "Sixth Generation Project")

  10. ``Continuous Spatial Automata,'' by Bruce J. MacLennan, University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-90-121, November 1990, 9 pages. Definition of continuous spatial automata, in which the cells and their states form a continuum; continuous "Life" as an example. A sequence of example states is also available.

  11. ``Field Computation: A Theoretical Framework for Massively Parallel Analog Computation, Parts I-IV,'' by Bruce J. MacLennan, University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-90-100, February 1990, 62 pages. This report presents a theoretical framework for understanding and designing massively parallel analog computers. The theory treats computations as continuous transformations of fields which are continuous assemblies of continuous values data.

  12. ``Continuous Computation: Taking Massive Parallelism Seriously,'' by Bruce J. MacLennan, poster presentation, Los Alamos National Laboratory Center for Nonlinear Studies 9th Annual International Conference, Emergent Computation, Los Alamos NM, May 22-26, 1989. Also University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-89-83, June 1989, 13 pages.

  13. ``Outline of a Theory of Massively Parallel Analog Computation,'' by Bruce J. MacLennan, poster presentation at IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., June 18-22, 1989. Abstract in proceedings, Vol II, p. 596. Also University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-89-84, June 1989, 23 pages.

  14. ``Field Computation: A Model of Massively Parallel Computation in Electronic, Optical, Molecular and Biological Systems,'' by Bruce J. MacLennan, extended abstract in Proceedings of AAAI Spring Symposium, Parallel Models of Intelligence: How Can Slow Components Think So Fast?, Stanford, March 22-24, 1988, pp. 180-183.

  15. ``Field Computation and Nonpropositional Knowledge,'' by Bruce J. MacLennan, Naval Postgraduate School Technical Report NPS52-87-040, September 1987, 31 pages.

  16. ``Technology-Independent Design of Neurocomputers: The Universal Field Computer,'' by Bruce J. MacLennan, IEEE First Annual International Conference on Neural Networks, June 21-24, 1987. Appears in Proceedings, IEEE First International Conference on Neural Networks, Vol. III, pp. 39-49.

Papers Authored or Co-authored by Jonathan Mills

  1. Jonathan W. Mills, The Continuous Retina: Image Processing with a Single-sensor Artificial Neural Field Network. Indiana Univ. Comp. Sci. TR 443 (November 1995) 6 pgs.

  2. Jonathan Mills, Programmable VLSI Extended Analog Computer for Cyclotron Beam Control. Indiana Univ. Comp. Sci. TR 441 (September 1995) 9 pgs.

  3. Robert A. Montante and Jonathan W. Mills. Measuring information capacity in a VLSI analog logic circuit. (March 1993). REVISION with new title: Probabilistic error correction in arbitrarily large Lukasiewicz logic arrays. Indiana Univ. Comp. Sci. TR 377 (April 1993). 15pgs.

Other Papers

  1. Bournez, Olivier, & Cosnard, Michel. On the computational power and super-Turing capabilities of dynamical systems. Research Report No. 95-30, Laboratoire de l'Informatique du Parallelisme, Ecole Normale Superieure de Lyon, October 23, 1995.

  2. ``Field Computer Simulator User's Guide,'' by Tomislav Goles, University of Tennessee, Knoxville, Department of Computer Science Technical Report CS-90-124, December 1990, 42 pages. This report describes a simulator for a general purpose field computer. The simulator is implemented as an extension of Common LISP, and provides a set of field transformation operators that we believe will be useful for programming practical applications. A tar file of the LISP source is also available.

  3. Pour-El, M. B. Abstract Computability and its Relation to the General Purpose Analog Computer. Transactions of the American Mathematical Society 199 (1974), 1-29.

  4. Rubel, Lee A. The Brain as an Analog Computer, Journal Theoretical Neurobiology 4 (1985), 73-81.

  5. Rubel, Lee A. Digital Simulation of Analog Computation, and Church's Thesis. The Journal of Symbolic Logic 54, 3 (Sept. 1989), 1011-1017.

  6. Rubel, Lee A. The Extended Analog Computer. Advances in Applied Mathematics 14 (1993), 39-50.

  7. Skinner, S. R., Cruz-Cabrera, A. A., Steck, J. E., & Behrman, E. C. Backpropagation error training of an optical neural network. Optical Society of Americal Annual Meeting, Long Beach, CA, Oct. 1997.
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Last updated: Wed Oct 17 17:15:46 EDT 2001