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 
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.


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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

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