Evolution versus Learning in Temporal Neural Networks

  • Hédi Soula
  • Guillaume Beslon
  • Joël Favrel
Conference paper


In this paper, we study the difference between two ways of setting synaptic weights in a “temporal” neural network. Used as a controller of a simulated mobile robot, the neural network is alternatively evolved through an evolutionary algorithm or trained via an hebbian reinforcement learning rule. We compare both approaches and argue that in the last instance only the learning paradigm is able to exploit meaningfully the temporal features of the neural network.


Mobile Robot Synaptic Weight Learning Paradigm Neural Controller Spike Timing Dependent Plasticity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    D. Floreano and F. Mondada. Evolutionary neuro-controllers for autonomous mobile robots. Neural Networks, (11):1461–1478, 1998.CrossRefGoogle Scholar
  2. [2]
    D. Floreano and C. Mattiusi. Evolution of spiking neural controllers for autonomous vision-based robots. In T. Gomi, editor, Evolutionnary Robotics. Berlin: Springer-Verlag, 2001.Google Scholar
  3. [3]
    E. Di Paolo. Spike timing dependent plasticity for evolved robots. Adaptive Behavior, 10(3):243–263, 2002.CrossRefGoogle Scholar
  4. [4]
    H. Soula, G. Beslon, and J. Favrel. Evolving spiking neurons nets to control an animat. In Proc. of ICANNGA 20003, Roanne, pages 193–197. Springer Verlag, 2003.Google Scholar
  5. [5]
    F. Varela, J-R Lachaux, E. Rodriguez, and J. Martinerie. The brainweb: Phase synchronization an large-scale integration. Nature Neuroscience Review, 2:229–239, 2001.CrossRefGoogle Scholar
  6. [6]
    S. Thorpe, D. Fize, and C. Marlot. Speed of processing in the human visual system. Nature, 381:520–522, 1996.CrossRefGoogle Scholar
  7. [7]
    J.J. Hopfield. Pattern recognition computation using action potential timing for stimulus representation. Nature, 376(6535):33–36, July 1995.CrossRefGoogle Scholar
  8. [8]
    W. Maas and C. Bishop, editors. Pulsed Neural Networks. MIT Press, Cambridge, Massachusetts, 2001.Google Scholar
  9. [9]
    G. Beslon, H. Soula, and J. Favrel. A neural model for animats brain. In Proc of ICANNGA 2001, Prague, pages 352–355, 2001.Google Scholar
  10. [10]
    H. Soula, G. Beslon, and J. Favrel. Controlling an animat with a self-organized modular neural network. In Proc. of EWLR’2001, Prague, pages 39–46, 2001.Google Scholar
  11. [11]
    D.O. Hebb. The Organization of Behavior. J. Wiley and Sons, New York, 1949.Google Scholar

Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Hédi Soula
  • Guillaume Beslon
  • Joël Favrel
    • 1
  1. 1.PRISMa Lab.National Institute of Applied Sciences (INSA)LyonFrance

Personalised recommendations