Stabilising Hebbian learning with a third factor in a food retrieval task
Adedoyin Maria Thompson, Bernd Porr and Florentin Wörgötter
Simulation of Adaptive Behavior 2006 (SAB 2006)
Rome, Italy, 25-29 September 2006
Summary
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 blobs in an environment.