Language supported storage and reuse of persistent neural network objects

  • Christopher Burdorf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 686)


This paper describes a language facility which supports storage and reuse of neuron objects. Neuron objects are made persistent as a part of the POCONS language. Subsequently, these objects with all the accumulated attributes can be reused by other applications in a transparent manner. Performance improvements result from reusing objects rather than using new ones.


neural software object-oriented systems neural networks simulation database systems 


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  1. 1.
    Anderson, J. R. Language, Memory, and Thought. Lawrence Erlbaum Associates, Publishers, 1976.Google Scholar
  2. 2.
    Burdorf, C. Per-Trans: A Persistent Stochastic Petri Net Representation Language. In Proceedings of the 22nd Annual Pittsburgh Conference on Modeling and Simulation, 1991.Google Scholar
  3. 3.
    Burdorf, C. POCONS: A Persistent Object-based Connectionist Simulator. In Proceedings of the 1992 SCS Western Multiconference: Object-Oriented Simulation. Society for Computer Simulation, 1992.Google Scholar
  4. 4.
    Burdorf, C. Representing Implication in an Object-Oriented Neural Network System Using Partitioned Connections. In Submitted for publication, 1992.Google Scholar
  5. 5.
    Burdorf, C. and Cammarata, S. PSE: A CLOS-Based Persistent Simulation Environment with Prefetching Capabilities. In Proceedings of the CLOS Workshop, 1989.Google Scholar
  6. 6.
    Burdorf, C. and Cammarata, S. PSE User's Manual. Technical Report WD-5103-DARPA, The RAND Corporation, August 1990.Google Scholar
  7. 7.
    Cammarata, S. and Burdorf, C. PSE: An Object-Oriented Simulation Environment Supporting Persistence. The Journal of Object-Oriented Programming, October 1991.Google Scholar
  8. 8.
    Dahl, J. and Nygaard, K. Simula: A language for programming and description of discrete event systems. User's manual, Norwegian Computing Center, 1967.Google Scholar
  9. 9.
    D'Autrechy, C., Reggia, J. A., Sutton, G. G., and Goodall, S.M. A General-Purpose Simulation Environment for Developing Connectionist Models. Simulation, 1988.Google Scholar
  10. 10.
    Feldman, J. A., Fanty, M. A., and Goddard, N. H. Computing with Structured Neural Networks. IEEE Computer, 1988.Google Scholar
  11. 11.
    Floreen, P., Myllymaki, P, Orponen, P., and Tirri, H. Compiling Object Declarations into Connectionist Networks. AICOM, 1990.Google Scholar
  12. 12.
    D. O. Hebb. The Organization of Behavior. Wiley and Sons, 1949.Google Scholar
  13. 13.
    Morrison, R. and Atkinson, M. P. Persistent Languages and Architectures. In International Workshop on Computer Architectures to Support Security and Persistence of Information. Springer-Verlag, 1990.Google Scholar
  14. 14.
    Padget, J. and Nuyens, G. (Eds.). The EuLisp Definition.Google Scholar
  15. 15.
    Rummelhart, D. E. and McClelland, J. L. Parallel Distributed Processing — Volume 1: Foundations. MIT Press, 1986.Google Scholar
  16. 16.
    Wang, D. and Hsu, C. SLONN: A Simulation Language for modeling of Neural Networks. Simulation, 1990.Google Scholar
  17. 17.
    A. Weitzenfeld. Neural simulation language version 2.1. Technical Report 91-05, Center for Neural Engineering, University of Southern California, August 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Christopher Burdorf
    • 1
  1. 1.School of Mathematical SciencesUniversity of BathBathUK

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