IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i6p1035-d375689.html
   My bibliography  Save this article

On the Lyapunov Exponent of Monotone Boolean Networks †

Author

Listed:
  • Ilya Shmulevich

    (Institute for Systems Biology, Seattle, WA 98103, USA)

Abstract

Boolean networks are discrete dynamical systems comprised of coupled Boolean functions. An important parameter that characterizes such systems is the Lyapunov exponent, which measures the state stability of the system to small perturbations. We consider networks comprised of monotone Boolean functions and derive asymptotic formulas for the Lyapunov exponent of almost all monotone Boolean networks. The formulas are different depending on whether the number of variables of the constituent Boolean functions, or equivalently, the connectivity of the Boolean network, is even or odd.

Suggested Citation

  • Ilya Shmulevich, 2020. "On the Lyapunov Exponent of Monotone Boolean Networks †," Mathematics, MDPI, vol. 8(6), pages 1-13, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:1035-:d:375689
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/6/1035/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/6/1035/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Luque, Bartolo & Solé, Ricard V., 2000. "Lyapunov exponents in random Boolean networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 284(1), pages 33-45.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ballesteros, Fernando J & Luque, Bartolo, 2002. "Random Boolean networks response to external periodic signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 289-300.
    2. Zozor, S. & Ravier, P. & Buttelli, O., 2005. "On Lempel–Ziv complexity for multidimensional data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 285-302.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:1035-:d:375689. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.