Information Storage

  • Joseph T. LizierEmail author
Part of the Springer Theses book series (Springer Theses)


Information storage is considered an important aspect of the dynamics of many natural and man-made processes, for example: in human brain networks [1] and artificial neural networks [2], synchronisation between coupled systems [3], coordinated motion in modular robots [4], and in the dynamics of inter-event distribution times [5]. The term is still often used rather loosely or in a qualitative sense however, and as yet we do not have a good understanding of how information storage interacts with information transfer and modification to give rise to distributed computation.


Domain Wall Excess Entropy Information Capacity Entropy Rate Local Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    M.G. Kitzbichler, M.L. Smith, S.R. Christensen, E. Bullmore, Broadband criticality of human brain network synchronization. PLoS Comput. Biol. 5(3), e1000314 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    J. Boedecker, O. Obst, N.M. Mayer, M. Asada, Initialization and self-organized optimization of recurrent neural network connectivity. HFSP J. 3(5), 340–349 (2009)CrossRefGoogle Scholar
  3. 3.
    R. Morgado, M. Cieśla, L. Longa, F.A. Oliveira, Synchronization in the presence of memory. Europhys. Lett. 79(1), 10002 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    M. Prokopenko, V. Gerasimov, I. Tanev, Evolving spatiotemporal coordination in a modular robotic system, in Proceedings of the Ninth International Conference on the Simulation of Adaptive Behavior (SAB’06), Rome, ed. by S. Nolfi, G. Baldassarre, R. Calabretta, J. Hallam, D. Marocco, J.-A. Meyer, D. Parisi. Lecture Notes in Artificial Intelligence, vol. 4095 (Springer, Berlin, 2006), pp. 548–559Google Scholar
  5. 5.
    K.I. Goh, A.L. Barabási, Burstiness and memory in complex systems. Europhys. Lett. 81(4), 48002 (2008)MathSciNetCrossRefGoogle Scholar
  6. 6.
    J.T. Lizier, M. Prokopenko, A.Y. Zomaya, Detecting non-trivial computation in complex dynamics, in Proceedings of the 9th European Conference on Artificial Life (ECAL, 2007) Lisbon, Portugal, ed. by F. Almeida e Costa, L.M. Rocha, E. Costa, I. Harvey, A. Coutinho. Lecture Notes in Artificial Intelligence, vol. 4648 (Springer, Berlin, 2007), pp. 895–904Google Scholar
  7. 7.
    J.T. Lizier, M. Prokopenko, A.Y. Zomaya, Local measures of information storage in complex distributed computation. Inf. Sci. 208, 39–54 (2012)Google Scholar
  8. 8.
    C.G. Langton, Computation at the edge of chaos: phase transitions and emergent computation. Physica D 42(1–3), 12–37 (1990)MathSciNetCrossRefGoogle Scholar
  9. 9.
    A.S. Klyubin, D. Polani, C.L. Nehaniv, Tracking information flow through the environment: simple cases of stigmergy, in Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALife IX), Boston, ed. by J. Pollack, M. Bedau, P. Husbands, T. Ikegami, R.A. Watson (MIT Press, Cambridge, 2004), pp. 563–568Google Scholar
  10. 10.
    I. Couzin, R. James, D. Croft, J. Krause, Social organization and information transfer in schooling fishes, ed. by B.C.K. Laland, J. Krause, in Fish Cognition and Behavior, ser. Fish and Aquatic Resources (Blackwell Publishing, Boston, 2006), pp. 166–185Google Scholar
  11. 11.
    P. Grassberger, Toward a quantitative theory of self-generated complexity. Int. J. Theor. Phys. 25(9), 907–938 (1986)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    P. Grassberger, Long-range effects in an elementary cellular automaton. J. Stat. Phys. 45(1–2), 27–39 (1986)Google Scholar
  13. 13.
    K. Lindgren, M.G. Nordahl, Complexity measures and cellular automata. Complex Syst. 2(4), 409–440 (1988)MathSciNetzbMATHGoogle Scholar
  14. 14.
    C.R. Shalizi, Causal architecture, complexity and self-organization in time series and cellular automata. Ph.D. Dissertation, University of Wisconsin-Madison, 2001Google Scholar
  15. 15.
    J.P. Crutchfield, D.P. Feldman, Regularities unseen, randomness observed: levels of entropy convergence. Chaos 13(1), 25–54 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    M. Wójtowicz, Java Cellebration v. 1.50, Online software (2002),
  17. 17.
    N. Ay, N. Bertschinger, R. Der, F. Güttler, E. Olbrich, Predictive information and explorative behavior of autonomous robots. Eur. Phys. J. B 63(3), 329–339 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    M. Lungarella, T. Pegors, D. Bulwinkle, O. Sporns, Methods for quantifying the informational structure of sensory and motor data. Neuroinformatics 3(3), 243–262 (2005)CrossRefGoogle Scholar
  19. 19.
    T.M. Cover, J.A. Thomas, Elements of Information Theory (Wiley, New York, 1991)Google Scholar
  20. 20.
    K. Marton, P.C. Shields, Entropy and the consistent estimation of joint distributions. Ann. Probab. 22(2), 960–977 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    J.T. Lizier, M. Prokopenko, A.Y. Zomaya, Information modification and particle collisions in distributed computation. Chaos 20(3), 037109 (2010)MathSciNetCrossRefGoogle Scholar
  22. 22.
    W. Hordijk, C.R. Shalizi, J.P. Crutchfield, Upper bound on the products of particle interactions in cellular automata. Physica D 154(3–4), 240–258 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    D.P. Feldman, J.P. Crutchfield, Synchronizing to periodicity: the transient information and synchronization time of periodic sequences. Adv. Complex Syst. 7(3–4), 329–355 (2004)MathSciNetzbMATHGoogle Scholar
  24. 24.
    J.E. Hanson, J.P. Crutchfield, Computational mechanics of cellular automata: an example. Physica D 103(1–4), 169–189 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    J.E. Hanson, J.P. Crutchfield, The attractor-basin portait of a cellular automaton. J. Stat. Phys. 66, 1415–1462 (1992)Google Scholar
  26. 26.
    C.R. Shalizi, R. Haslinger, J.-B. Rouquier, K.L. Klinkner, C. Moore, Automatic filters for the detection of coherent structure in spatiotemporal systems. Phys. Rev. E 73(3), 036104 (2006)MathSciNetCrossRefGoogle Scholar
  27. 27.
    J.T. Lizier, M. Prokopenko, A.Y. Zomaya, Coherent information structure in complex computation. Theory Biosci. Theory Biosci. 131(3), 193–203 (2012), doi: 10.1007/s12064-011-0145-9

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.CSIRO ICT CentreMarsfieldAustralia

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