Self-organized Neural Representation of Time

  • Michail Maniadakis
  • Panos Trahanias
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)


Time is crucially involved in most of the activities of humans and animals. However, the cognitive mechanisms that support experiencing and processing time remain largely unknown. In the present work we follow a self-organized connectionist modeling approach to study how time may be encoded in a neural network based cognitive system in order to provide suggestions for possible time processing mechanisms in the brain. A particularly interesting feature of our study regards the implementation of a single computational model to accomplish two different robotic behavioral tasks which assume diverse manipulation of time intervals. Examination of the implemented cognitive systems revealed that it is possible to integrate the main theoretical models of time representation existing today into a new and particularly effective theory that can sufficiently explain a series of neuroscientific observations.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bueti, D.: The sensory representation of time. Frontiers in Integrative Neuroscience 5(34) (2011)Google Scholar
  2. 2.
    Dragoi, V., Staddon, J., Palmer, R., Buhusi, C.: Interval timing as an emergent learning property. Psychol. Review 110(1), 126–144 (2003)CrossRefGoogle Scholar
  3. 3.
    Droit-Volet, S., Meck, W., Penney, T.: Sensory modality and time perception in children and adults. Behav. Process 74, 244–250 (2007)CrossRefGoogle Scholar
  4. 4.
    Gibbon, J., Church, R., Meck, W.: Scalar timing in memory. In: Gibbon, J., Allan, L.G. (eds.) Timing and Time Perception, pp. 52–77. New York Academy of Sciences, New York (1984)Google Scholar
  5. 5.
    Ivry, R.B., Schlerf, J.E.: Dedicated and intrinsic models of time perception. Tr. in Cognitive Sciences 12(7), 273–280 (2008)CrossRefGoogle Scholar
  6. 6.
    Janssen, P., Shadlen, M.N.: A representation of the hazard rate of elapsed time in macaque area lip. Nat. Neurosci. 8(2), 234–241 (2005)CrossRefGoogle Scholar
  7. 7.
    Karmarkar, U.R., Buonomano, D.V.: Timing in the absence of clocks: Encoding time in neural network states. Neuron 53(3), 427–438 (2007)CrossRefGoogle Scholar
  8. 8.
    Laje, R., Cheng, K., Buonomano, D.: Learning of temporal motor patterns: an analysis of continuous versus reset timing. Front. Integr. Neurosc. 5(61) (2011)Google Scholar
  9. 9.
    Maniadakis, M., Trahanias, P.: Temporal cognition: a key ingredient of intelligent systems. Frontiers in Neurorobotics 5 (2011)Google Scholar
  10. 10.
    Maniadakis, M., Trahanias, P., Tani, J.: Explorations on artificial time perception. Neural Networks 22, 509–517 (2009)CrossRefGoogle Scholar
  11. 11.
    Maniadakis, M., Wittmann, M., Trahanias, P.: Time experiencing by robotic agents. In: Proc. 11th European Symposium on Artificial Neural Networks (2011)Google Scholar
  12. 12.
    Meck, W., Penney, T., Pouthas, V.: Cortico-striatal representation of time in animals and humans. Current Opinion in Neurobiology 18(2), 145–152 (2008)CrossRefGoogle Scholar
  13. 13.
    Miall, C.: The storage of time intervals using oscillating neurons. Neural Computation 1, 359–371 (1989)CrossRefGoogle Scholar
  14. 14.
    Ruppin, E.: Evolutionary autonomous agents: A neuroscience perspective. Nature Reviews Neuroscience 3(2), 132–141 (2002)CrossRefGoogle Scholar
  15. 15.
    Simen, P., Balci, F., de Souza, L., Cohen, J., Holmes, P.: A model of interval timing by neural integration. J. Neuroscience 31, 9238–9253 (2011)CrossRefGoogle Scholar
  16. 16.
    Staddon, J., Higa, J.: Time and memory: towards a pacemaker-free theory of interval timing. J. Exp. Anal. Behav. 71(2), 215–251 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michail Maniadakis
    • 1
  • Panos Trahanias
    • 1
  1. 1.ICSFoundation for Research and TechnologyHellasGreece

Personalised recommendations