Stabilising Hebbian Learning with a Third Factor in a Food Retrieval Task

  • Adedoyin Maria Thompson
  • Bernd Porr
  • Florentin Wörgötter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)


When neurons fire together they wire together. This is Donald Hebb’s famous postulate. However, Hebbian learning is inherently unstable because synaptic weights will self amplify themselves: the more a synapse is able to drive a postsynaptic cell the more the synaptic weight will grow. We present a new biologically realistic way how to stabilise synaptic weights by introducing a third factor which switches on or off learning so that self amplification is minimised. The third factor can be identified by the activity of dopaminergic neurons in VTA which fire when a reward has been encountered. This leads to a new interpretation of the dopamine signal which goes beyond the classical prediction error hypothesis. The model is tested by a real world task where a robot has to find “food disks” in an environment.


Conditioned Stimulus Synaptic Weight Lateral Hypothalamus Real Robot Successful Learning 
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 Berlin Heidelberg 2006

Authors and Affiliations

  • Adedoyin Maria Thompson
    • 1
  • Bernd Porr
    • 1
  • Florentin Wörgötter
    • 2
  1. 1.Department of Electronics & Electrical EngineeringUniversity of GlasgowGlasgowUnited Kingdom
  2. 2.Bernstein Center of Computational NeuroscienceUniversity GöttingenGermany

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