Skip to main content
SpringerLink
Log in

Can Dynamic Neural Filters Produce Pseudo-Random Sequences?

  • Conference paper
Artificial Neural Networks: Biological Inspirations – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3696))

Included in the following conference series:

Abstract

Dynamic neural filters (DNFs) are recurrent networks of binary neurons. Under proper conditions of their synaptic matrix they are known to generate exponentially large cycles. We show that choosing the synaptic matrix to be a random orthogonal one, the average cycle length becomes close to that of a random map. We then proceed to investigate the inversion problem and argue that such a DNF could be used to construct a pseudo-random generator. Subjecting this generator’s output to a battery of tests we demonstrate that the sequences it generates may indeed be regarded as pseudo-random.

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 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Quenet, B., Horn, D.: The dynamic neural filter: a binary model of spatiotemporal coding. Neural Comput. 15, 309–329 (2003)

    Article  MATH  Google Scholar 

  2. Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the Theory of Neural Computation. Addison-Wesley Longman Publishing, Amsterdam (1991)

    Google Scholar 

  3. Peretto, P.: An Introduction to the Modeling of Neural Networks. Cambridge University Press, Cambridge (1992)

    Book  MATH  Google Scholar 

  4. Gutfreund, H., Reger, D.J., Young, A.P.: The nature of attractors in an asymmetric spin glass with deterministic dynamics. J. Phys. A: Math. Gen. 21, 2775–2797 (1988)

    Article  MathSciNet  Google Scholar 

  5. Bastolla, U., Parisi, G.: Attractors in Fully Asymmetric Neural Networks. J. Phys. A: Math. Gen. 30, 5613–5631 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  6. Karrasa, D.A., Zorkadis, V.: On neural network techniques in the secure management of communication systems through improving and quality assessing pseudorandom stream generators. Neural Networks 16, 899–905 (2003)

    Article  Google Scholar 

  7. Crounse, K., Yang, T., Chua, L.O.: Pseudo-random sequence generation using the cnn universal machine with applications to cryptography. In: Proc. IVth IEEE International Workshop on Cellular Neural Networks and Their Applications, pp. 433–438 (1996)

    Google Scholar 

  8. Yao, A.C.: Theory and applications of trapdoor functions. In: Proc. 23th FOCS, pp. 464–479 (1982)

    Google Scholar 

  9. Blum, M., Micali, S.: How to generate cryptographically strong sequences of pseudo-random bits. SIAM J. Comput. 13, 850–864 (1984)

    Article  MATH  MathSciNet  Google Scholar 

  10. Massey, J.L.: An introduction to contemporary cryptology. Proc. of the IEEE 76, 533–549 (1988)

    Article  Google Scholar 

  11. Goldreich, O.: Foundations of Cryptography: Basic Tools. Cambridge University Press, Cambridge (2001)

    Book  MATH  Google Scholar 

  12. Goldreich, O., Levin, L.: Hard-core predicates for any one-way function. In: Proc. of the 21st ACM STOC, pp. 25–32 (1989)

    Google Scholar 

  13. Nemhauser, G., Wolsey, L.: Integer and Combinatorial Optimization. John Wiley and Sons, Chichester (1988)

    MATH  Google Scholar 

  14. Johnson, E., Nemhauser, G., Savelsbergh, M.: Progress in linear programming based branch-and-bound algorithms: An exposition. INFORMS J. Comp. 12, 2–23 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  15. Makhorin, A.: Gnu linear programming kit version 4.4. Free Software Foundation (2004)

    Google Scholar 

  16. Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Barker, E., Leigh, S., Levenson, M., Vangel, M., Banks, D., Heckert, A., Dray, J., Vo, S.: A statistical test suite for random and pseudorandom number generators for cryptographic applications. In: NIST (2001), http://csrc.nist.gov/rng/SP800–22b.pdf

  17. Soto, J.: Statistical testing of random number generators. In: Proc. 22nd NISSC (1999)

    Google Scholar 

  18. Driver, P.M., Humphries, N.: Protean Behavior: The Biology of Unpredictability. Oxford University Press, Oxford (1988)

    Google Scholar 

  19. Rapoport, A., Budescu, D.V.: Generation of random series in two-person strictly competitive games. J. Exp. Psych.: Gen. 121, 352–363 (1992)

    Article  Google Scholar 

  20. Neuringer, A.: Can people behave ”randoml”? the role of feedback. J. Exp Psych: Gen. 115, 62–75 (1986)

    Article  Google Scholar 

  21. Neuringer, A., Voss, C.: Approximating chaotic behavior. Psych. Sci. 4, 113–119 (1993)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Max-Planck Institute of Neurobiology, Department of Systems and Computational Neurobiology, Am Klopferspitz 18, D-82152, Martinsried, Germany

    Yishai M. Elyada

  2. School of Physics and Astronomy, Raymond & Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Tel-Aviv, 69978, Israel

    David Horn

Authors
  1. Yishai M. Elyada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Horn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015 HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Elyada, Y.M., Horn, D. (2005). Can Dynamic Neural Filters Produce Pseudo-Random Sequences?. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_34

Download citation

  • DOI: https://doi.org/10.1007/11550822_34

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28752-0

  • Online ISBN: 978-3-540-28754-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation