Skip to main content
SpringerLink
Log in

Artificial Neural Networks that Learn Many-Body Physics

  • Chapter
Condensed Matter Theories

Part of the book series: Condensed Matter Theories ((COMT,volume 6))

Abstract

Neural networks are systems of neuron-like units that store information in the connections between the units.1–4 From the viewpoint of the many-body theorist, the neurons may be thought of as particles, and the weighted connections between units provide the interactions between these particles. The neuronal units exhibit varying degrees of activation, and the activation of a given unit in turn promotes or hinders the activation of a unit to which it extends a connection, depending on whether the connection has a positive or negative weight, respectively. Thus, neurons turn each other on (or off). Since there are in general many pathways by which information can travel through an intricate mesh of connections, processing in a neural network is said to be “massively parallel” as opposed to sequential.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. P. Lippmann, IEEE ASSP Mag. 4(2), 4–22 (1987).

    Article  Google Scholar 

  2. J. D. Cowan and D. H. Sharp, Quarterly Reviews of Biophysics 21, 365–427 (1988).

    Article  Google Scholar 

  3. E. Domany, J. Stat. Phys. 51, 743–775 (1988).

    Article  MathSciNet  ADS  MATH  Google Scholar 

  4. J. W. Clark, J. Rafelski, and J. V. Winston, Physics Reports 123(4) 215–273

    Google Scholar 

  5. J. W. Clark, Physics Reports 158(2) 91–157

    Google Scholar 

  6. J. W. Clark, in Nonlinear Phenomena in Complex Systems, edited by A. N. Proto (Elsevier, Amsterdam, 1989), pp. 1–102.

    Chapter  Google Scholar 

  7. J. J. Hopfield, Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    Article  MathSciNet  ADS  Google Scholar 

  8. D. J. Amit, H. Gutfreund, and H. Sompolinsky, Ann. Phys. (NY) 173, 30–67 (1987).

    Article  ADS  Google Scholar 

  9. D. J. Amit, Modeling Brain Function: The World of Attractor Neural Networks (Cambridge University Press, Cambridge, 1989).

    Book  MATH  Google Scholar 

  10. N. Qian and T. J. Sejnowski, J. Mol Biol. 202, 865–884 (1988).

    Article  Google Scholar 

  11. H. Bohr, J. Bohr, S. Brunak, R. M. J. Cotterill, B. Lautrup, L. Nøskov, O. H. Olsen, and S. B. Petersen, FEBS Letters 241, 223–228 (1988).

    Article  Google Scholar 

  12. L. Holly and M. Karplus, Proc. Natl Acad. Sci. USA 86, 152–156 (1989)

    Article  ADS  Google Scholar 

  13. F. Rosenblatt, Psychological Review 65, 386–408 (1958).

    Article  MathSciNet  Google Scholar 

  14. H. D. Block, Rev. Mod. Phys. 34, 123–135 (1962).

    Article  MathSciNet  ADS  MATH  Google Scholar 

  15. W. S. McCulloch and W. Pitts, Bull. Math. Biophys. 5, 115–137 (1943).

    Article  MathSciNet  MATH  Google Scholar 

  16. M. Minsky and S. Papert, Perceptrons: An Introduction to Computational Geometry (MIT Press, Cambridge, MA, 1969).

    MATH  Google Scholar 

  17. J. Denker, D. Schwartz, B. Wittner, S. Solla, J. Hopfield, R. Howard, and L. Jackel, Complex Systems 1, 877–922 (1987).

    MathSciNet  MATH  Google Scholar 

  18. A. K. Hornik, A. M. Stinchcombe, and A. H. White, Neural Networks 2, 359–366 (1989).

    Article  Google Scholar 

  19. D. H. Ackley, G. E. Hinton, and T. J. Sejnowski, Cognitive Science 9, 147–169 (1985)

    Article  Google Scholar 

  20. G. E. Hinton and T. J. Sejnowski, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, edited by D. E. Rumelhart, J. L. McClelland, and the PDP Research Group (MIT Press, Cambridge, MA, 1986), pp. 282–317.

    Google Scholar 

  21. P. J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences Ph.D. Thesis (Harvard University, Cambridge, MA, 1974).

    Google Scholar 

  22. Y. Le Cun, Proc. Cognitiva 85, 599–604 (1985).

    Google Scholar 

  23. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, edited by D. E. Rumelhart, J. L. McClelland, and the PDP Research Group (MIT Press, Cambridge, MA, 1986), pp. 282–317.

    Google Scholar 

  24. D. B. Parker, in Proc. Conf. Neural Networks for Computing, AIP Conference Proceedings 151, edited by J. S. Denker (American Institute of Physics, New York, 1986), pp. 327–332.

    Google Scholar 

  25. Y. C. Lee, G. Doolen, H. H. Chen, G. Z. Sun, T. Maxwell, H. Y. Lee, and C. L. Giles, Physica D22, 276–289 (1986)

    MathSciNet  Google Scholar 

  26. T. Maxwell, C. L. Giles, Y. C. Lee, and H. H. Chen, in Proc, Conf. Neural Networks for Computing, AIP Conference Proceedings 151, edited by J. S. Denker (American Institute of Physics, New York, 1986), pp. 299–304.

    Google Scholar 

  27. E. Mjolsness, D. H. Sharp, and B. K. Alpert, Advances in Applied Mathematics 10, 137–163 (1989).

    Article  MathSciNet  MATH  Google Scholar 

  28. B. Widrow and M. E. Hoff, in Institute of Radio Engineers, Western Electronic Show and Convention, Convention Record, Part 4 (1960), pp. 96-104.

    Google Scholar 

  29. F. Crick, Nature 337, 129–132 (1989).

    Article  ADS  Google Scholar 

  30. G. E. Hinton, in Proceedings of the Eighth Annual Conference of the Cognitive Science Society (Amherst, Massachusetts, 1986)

    Google Scholar 

  31. D. C. Plaut, S. J. Nowlan, and G. E. Hinton, Carnegie-Mellon University Computer Science Technical Report CMU-CS-86-126 (1986).

    Google Scholar 

  32. T. J. Sejnowski and C. R. Rosenberg, Complex Systems 1, 145–168 (1987).

    MATH  Google Scholar 

  33. G. W. Cottrell, P. Munro, and D. Zipser, in Proceedings of the Ninth Annual Conference of the Cognitive Science Society (Seattle, Washington, 1987), pp, 461–473.

    Google Scholar 

  34. J. L. Elman and D. Zipser, in Technical Report No. 8701, Institute for Cognitive Science, University of California, San Diego (1987).

    Google Scholar 

  35. S. R. Lehky and T. J. Sejnowski, Nature 333, 452–454 (1988).

    Article  ADS  Google Scholar 

  36. Y. Le Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, in Neural Information Processing Systems, Vol. 2, edited by D. Touretzky (Denver; Morgan Kaufman, 1990).

    Google Scholar 

  37. H. Bohr, J. Bohr, S. Brunak, R. M. J. Cotterill, H. Fredholm, B. Lautrup, and S. B. Petersen, FEBS Letters 261, 43–46 (1990).

    Article  Google Scholar 

  38. G. H. Paine and H. A. Scheraga, Biopolymers 26, 1125–1162 (1987).

    Article  Google Scholar 

  39. P. Y. Chou and G. D. Fasman, Ann. Rev. Biochem. 47, 251–276 (1978).

    Article  Google Scholar 

  40. H. Andreassen, H. Bohr, J. Bohr, S. Brunak, T. Bugge, R. M. J. Cotterill, C. Jacobsen, P. Kusk, B. Lautrup, S. B. Petersen, T. Saermark, and K. Ulrich, Journal of Acquired Immune Deficiency Syndromes (AIDS) 3, 615–622 (1990).

    Google Scholar 

  41. H. Bohr, private communication.

    Google Scholar 

  42. Chart of the Nuclides, revised 1983 by F. W. Walker, D. G. Miller, and F. Feiner (General Electric, San Jose, CA, 1984).

    Google Scholar 

  43. J. L. McClelland and D. E. Rumelhart, Explorations in Parallel Distributed Processing (MIT Press, Cambridge, MA, 1988).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, Missouri, 63130, USA

    John W. Clark & Srinivas Gazula

Authors
  1. John W. Clark
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Srinivas Gazula
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Lecce, Lecce, Italy

    S. Fantoni

  2. University of Pisa, Pisa, Italy

    S. Rosati

Rights and permissions

Reprints and permissions

Copyright information

© 1991 Springer Science+Business Media New York

About this chapter

Cite this chapter

Clark, J.W., Gazula, S. (1991). Artificial Neural Networks that Learn Many-Body Physics. In: Fantoni, S., Rosati, S. (eds) Condensed Matter Theories. Condensed Matter Theories, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3686-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-3686-4_1

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-6638-6

  • Online ISBN: 978-1-4615-3686-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation