Swarm Cognition and Artificial Life

  • Vito Trianni
  • Elio Tuci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5778)


Swarm Cognition is the juxtaposition of two relatively unrelated concepts that evoke, on the one hand, the power of collective behaviours displayed by natural swarms, and on the other hand the complexity of cognitive processes in the vertebrate brain. Recently, scientists from various disciplines suggest that, at a certain level of description, operational principles used to account for the behaviour of natural swarms may turn out to be extremely powerful tools to identify the neuroscientific basis of cognition. In this paper, we review the most recent studies in this direction, and propose an integration of Swarm Cognition with Artificial Life, identifying a roadmap for a scientific and technological breakthrough in Cognitive Sciences.


Nest Site Collective Intelligence Computational Neuroscience Nest Site Selection Waggle Dance 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aron, S., Deneubourg, J.L., Goss, S., Pasteels, J.M.: Functional self-organization illustrated by inter-nest traffic in ants: The case of the argentinian ant. In: Alt, W., Hoffman, G. (eds.) Biological Motion. Lecture Notes in BioMathematics, vol. 89, pp. 533–547. Springer, Berlin (1990)CrossRefGoogle Scholar
  2. 2.
    Couzin, I.: Collective cognition in animal groups. Trends in Cognitive Sciences 13(1), 36–43 (2009)CrossRefGoogle Scholar
  3. 3.
    Passino, K., Seeley, T., Visscher, P.: Swarm cognition in honey bees. Behavioral Ecology and Sociobiology 62, 401–414 (2008)CrossRefGoogle Scholar
  4. 4.
    Marshall, J.A.R., Bogacz, R., Dornhaus, A., Planqué, R., Kovacs, T., Franks, N.R.: On optimal decision-making in brains and social insect colonies. Journal of the Royal Society Interface 6, 1065–1074 (2009)CrossRefGoogle Scholar
  5. 5.
    Camazine, S., Deneubourg, J.L., Franks, N., Sneyd, J., Theraulaz, G., Bonabeau, E.: Self-Organization in Biological Systems. Princeton University Press, Princeton (2001)MATHGoogle Scholar
  6. 6.
    Couzin, I.D., Krause, J.: Self-organization and collective behavior of vertebrates. Advances in the Study of Behavior 32, 1–75 (2003)CrossRefGoogle Scholar
  7. 7.
    Sumpter, D.: The principles of collective animal behaviour. Philosophical Transactions of the Royal Society of London: Series B 361, 5–22 (2006)CrossRefGoogle Scholar
  8. 8.
    Strogatz, S.H.: Sync: The emerging science of spontaneous order. Hyperion Press, New York (2003)Google Scholar
  9. 9.
    Rumelhart, D., McClelland, J.: Parallel Distributed Processing, vol. 1(2). MIT Press, Cambridge (1986)Google Scholar
  10. 10.
    Beer, R.D.: Dynamical approaches to cognitive science. Trends in Cognitive Sciences 4(3), 91–99 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Thelen, E., Schöner, G., Scheier, C., Smith, L.B.: The dynamics of embodiment: A field theory of infant perseverative reaching. Behavioral and Brain Sciences 24(1), 1–34 (2001)CrossRefGoogle Scholar
  12. 12.
    Fitzpatrick, P., Schmidt, R.C., Carello, C.: Dynamical patterns in clapping behavior. Journal of Experimental Psychology: Human Perception and Performance 22(3), 707–724 (1996)Google Scholar
  13. 13.
    Schöner, G.: Timing, clocks, and dynamical systems. Brain and Cognition 48, 31–51 (2002)CrossRefGoogle Scholar
  14. 14.
    Deco, G., Jirsa, V., Robinson, P., Breakspear, M., Frinston, K.: The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Computational Biology 4(8) (2008)Google Scholar
  15. 15.
    Ratcliff, R., Smith, P.L.: A comparison of sequential sampling models for two-choice reaction time. Psychological Review 111, 333–367 (2004)CrossRefGoogle Scholar
  16. 16.
    Franks, N.R., Dornhaus, A., Fitzsimmons, J.P., Stevens, M.: Speed versus accuracy in collective decision-making. Proceedings of the Royal Society B: Biological Sciences 270(1532), 2457–2463 (2003)CrossRefGoogle Scholar
  17. 17.
    Bedau, M.A.: Artificial life: organization, adaptation and complexity from the bottom up. Trends in Cognitive Sciences 7(11), 505–512 (2003)CrossRefGoogle Scholar
  18. 18.
    Langton, C.G.: Artificial life. In: Langton, C.G. (ed.) Artificial life, pp. 1–47. Addison-Wesley, Reading (1988)Google Scholar
  19. 19.
    Harvey, I., Di Paolo, E.A., Wood, R., Quinn, M., Tuci, E.: Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11(1-2), 79–98 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vito Trianni
    • 1
  • Elio Tuci
    • 1
  1. 1.Institute of Cognitive Sciences and Technologies (ISTC)National Research Council (CNR)RomeItaly

Personalised recommendations